From 3be3ef98d776baa2bd6e7bf1022f8009e7c62e71 Mon Sep 17 00:00:00 2001
From: Mahmoud Mabrouk <mahmoud@agenta.ai>
Date: Wed, 22 Jul 2026 17:47:51 +0200
Subject: [PATCH 1/9] feat(website): Agenta 2.0 announcement post + hero tag
 (draft)

Claude-Session: https://claude.ai/code/session_013Qe1Vf2yvj33BVd5W1q7HB
---
 .../blog/introducing-agenta-2-0/hero.png      | Bin 0 -> 51110 bytes
 website/src/components/Hero.astro             |   5 +
 .../content/posts/introducing-agenta-2-0.mdx  | 117 ++++++++++++++++++
 3 files changed, 122 insertions(+)
 create mode 100644 website/public/blog/introducing-agenta-2-0/hero.png
 create mode 100644 website/src/content/posts/introducing-agenta-2-0.mdx

diff --git a/website/public/blog/introducing-agenta-2-0/hero.png b/website/public/blog/introducing-agenta-2-0/hero.png
new file mode 100644
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HcmV?d00001

diff --git a/website/src/components/Hero.astro b/website/src/components/Hero.astro
index fff5df61b1..5f7e831cb7 100644
--- a/website/src/components/Hero.astro
+++ b/website/src/components/Hero.astro
@@ -24,6 +24,11 @@ const SHOW_HERO_VIDEO = false;
 >
   <div style="display:flex;flex-direction:column;align-items:center;gap:32px;">
     <div style="display:flex;flex-direction:column;align-items:center;gap:28px;">
+      <a
+        href="/blog/introducing-agenta-2-0"
+        style="display:inline-flex;align-items:center;gap:6px;height:22px;padding:0 12px;border-radius:999px;background:rgba(255,255,255,0.06);box-shadow:0 0 0 1px rgba(255,255,255,0.14);font:var(--text-caption);letter-spacing:0.03em;color:rgba(255,255,255,0.8);text-decoration:none;"
+        >Agenta 2.0 — read the announcement →</a
+      >
       <h1
         class="ag-h1"
         style="margin:0;font:var(--text-display-xl);color:#F7F6F4;max-width:920px;text-wrap:pretty;"
diff --git a/website/src/content/posts/introducing-agenta-2-0.mdx b/website/src/content/posts/introducing-agenta-2-0.mdx
new file mode 100644
index 0000000000..9e08431398
--- /dev/null
+++ b/website/src/content/posts/introducing-agenta-2-0.mdx
@@ -0,0 +1,117 @@
+---
+slug: "introducing-agenta-2-0"
+title: "Introducing Agenta 2.0"
+description: "Agenta 2.0 is the open-source workspace for building and running agents: build them through chat, improve them with feedback, share them with your team."
+category: Article
+date: 2026-07-22
+readingTime: "6 min read"
+heroImage: /blog/introducing-agenta-2-0/hero.png
+ogImage: /blog/introducing-agenta-2-0/hero.png
+author: mahmoud-mabrouk
+featured: false
+tags: ["Article"]
+---
+
+Today, we are introducing Agenta 2.0, an open-source workspace for building and running agents.
+
+Agenta lets you build specialized agents for different areas of work. You can work with them as AI coworkers, share them with your team, and use them to automate recurring work.
+
+## Agents are becoming a new building block for work
+
+Agents have reached a point where they can perform and organize substantial work.
+
+Give an agent a goal, the right context, and access to tools, and it can decide how to approach the job. It can inspect information, use applications, write scripts, and adapt when the situation changes.
+
+Until now, automating work often meant defining every step in advance. You designed a workflow and specified what should happen at each branch. This remains the right approach when a process is fixed and every step must be predictable.
+
+But much of our work is not like that. Research, product decisions, customer support, sales, and company operations require context and judgment. The right next step depends on what the system finds.
+
+An agent can become the basic unit that performs this work. At a technical level, we think of the agent as a new compute primitive. In practical terms, you describe the job, provide the context and tools, and let the agent create the logic it needs.
+
+People still provide direction, feedback, and judgment. Agents take on more of the execution.
+
+## Building useful agents still requires too much work
+
+Most companies are not working this way yet.
+
+A useful agent needs company context. It needs instructions, files, skills, tools, and access to the applications where work happens. People need a way to improve it, share it, run it again, and understand what happened when it makes a mistake.
+
+Today, this capability is spread across several kinds of products.
+
+Coding agents such as Claude Code, Codex, and Pi give individual technical users a strong environment. Closed AI workspaces provide a polished experience, but limit how much users can inspect, change, or move. Visual workflow builders work well when users can define the process in advance. Agent frameworks provide flexibility, but require a team to build and operate the surrounding system.
+
+The result is an awkward choice between agency, ease of use, automation, and control.
+
+We think agents need an open workspace where people can build them through use, give them lasting context, share them with a team, automate suitable work, and control how they run.
+
+## A workspace for you and your agents
+
+Agenta provides that workspace.
+
+You start with a real job and describe it in chat. The agent works with you, uses the context you provide, and helps create the setup it needs.
+
+Each agent has a persistent workspace. It can maintain research, documents, plans, reports, and other artifacts instead of starting every conversation without context.
+
+You improve the agent through real work and feedback. When it becomes useful, you can share it with your team. For suitable recurring work, you can run it on a schedule or when an event occurs in a connected application.
+
+The AI coworker and the automation are not separate systems. They are two ways to use the same agent. You can work with it directly when the job needs discussion, then let it handle understood parts of the work in the background.
+
+## Open agents, open choices
+
+The agent itself remains simple and inspectable. An Agenta agent is built from components such as `AGENTS.md`, skills, tools, and files. You can see and change how it works.
+
+Agenta adds the workspace, sharing, triggers, versions, and operational history around the agent. It keeps the agent separate from the model and harness that run it.
+
+This means you can choose the right setup for the job without rebuilding the agent from the beginning.
+
+We already work this way inside Agenta. Our go-to-market work uses GPT-5.6. We use a different coding harness for engineering. When speed matters, we use GLM 5.2 Nitro through OpenRouter. In our self-hosted setup, personal agents can use our existing Claude Code and Codex subscriptions.
+
+{/* VERIFY BEFORE PUBLICATION: Confirm the exact GTM model and harness, the engineering harness name, GLM 5.2 Nitro support, subscription setup, portability limits, and every stated stack combination. */}
+
+The important point is not the number of supported providers. It is that the agent and its work do not have to begin again when the surrounding stack changes.
+
+## A go-to-market agent in practice
+
+We built the new Agenta from our own need to work this way.
+
+As a technical founder, I work across product, engineering, research, positioning, content, and launch planning. These areas affect one another, but their context is usually spread across conversations and documents.
+
+My go-to-market agent works with a persistent workspace containing our product context, research, positioning, decisions, launch plans, and drafts. I use it in chat to research questions, compare new information with earlier decisions, update documents, and prepare assets. This announcement is one output from that ongoing work.
+
+I also use agents for recurring work. One helps operate our social listening through Octolens, where it manages research vocabulary, monitoring feeds, and the decisions behind them.
+
+{/* VERIFY BEFORE PUBLICATION: Confirm the exact Octolens automation, its trigger, what it performs automatically, and what still requires Mahmoud's approval. Link the public example only if it exposes no private configuration or credentials. */}
+
+The agent does not make company decisions for me. It gathers and structures information, records context, and helps perform the work. I provide direction, correct it, and make the final decisions.
+
+## Reliability comes from improvement
+
+The first version of Agenta focused on helping teams build, evaluate, and observe applications powered by language models. That foundation remains important because agents still make mistakes.
+
+For a company, reliability is not only about whether one run succeeds. It is about making an agent better at a defined job over time.
+
+First, people need to state what good work looks like. Then they need to review real results and give feedback. When something goes wrong, they need to inspect the trace, understand which instruction or action caused the problem, and change the agent without losing what already worked.
+
+Agenta supports this improvement loop through tracing, annotations, evaluations, and versioning. An annotation records structured feedback on a run. An evaluation checks an output against a defined expectation. Versioning connects each result to the agent configuration that produced it.
+
+Together, these tools help teams make agents more reliable and predictable. They do not make a language model deterministic. They provide the evidence needed to improve the agent and decide which work still needs human review.
+
+Permissions and human approval add another layer of control. Teams can decide which actions an agent may perform, which require approval, and which remain blocked.
+
+{/* VERIFY BEFORE PUBLICATION: Confirm tracing, annotations, evaluations, versioning, permissions, approval behavior, and configuration restoration in the launch build. */}
+
+## Getting started
+
+Agenta 2.0 is available through Agenta Cloud and as a self-hosted product.
+
+{/* VERIFY BEFORE PUBLICATION: Confirm availability, release name, license, tested installation path, and destination links. */}
+
+Start with one area of work. Build an agent around a real job, give it the context it needs, and improve it through use. Once it works well, share it with your team or let it handle suitable parts of the work in the background.
+
+Agenta is still early. Some integrations and execution options require additional setup. Our documentation will explain the current limits and the setup required.
+
+[Try Agenta Cloud](https://cloud.agenta.ai), [view Agenta on GitHub](https://github.com/Agenta-AI/agenta), or follow the self-hosting guide to run it yourself.
+
+We believe the people who understand the work should be able to build the agents that do it.
+
+If you build an agent, tell us what work you gave it, where it helped, and where it failed. That feedback will shape what we build next.

From 7f99e66c48222f99587a5e75aee4babc33b7a5e2 Mon Sep 17 00:00:00 2001
From: Mahmoud Mabrouk <mahmoud@agenta.ai>
Date: Wed, 22 Jul 2026 18:35:08 +0200
Subject: [PATCH 2/9] feat(website): designed 16:9 hero for the Agenta 2.0
 announcement

Claude-Session: https://claude.ai/code/session_013Qe1Vf2yvj33BVd5W1q7HB
---
 .../blog/introducing-agenta-2-0/hero.png      | Bin 51110 -> 94685 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/website/public/blog/introducing-agenta-2-0/hero.png b/website/public/blog/introducing-agenta-2-0/hero.png
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From a01e1e280df6dd52a3fc90e30eea48a8500820f7 Mon Sep 17 00:00:00 2001
From: Mahmoud Mabrouk <mahmoud@agenta.ai>
Date: Wed, 22 Jul 2026 19:24:19 +0200
Subject: [PATCH 3/9] =?UTF-8?q?feat(website):=20blog=20post=20layout=20pol?=
 =?UTF-8?q?ish=20=E2=80=94=20Linear-scale=20typography,=20cleaner=20byline?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Retune .ag-prose body to 16px/400/1.65 with slight negative tracking (from
Linear's blog), lighter section-heading scale, and a stronger link underline.
On the post page: drop the category chip, reduce the byline to avatar + name,
move date and reading time to a quiet line under the subtitle, enlarge the
subtitle, and even out the hero-image vertical rhythm.

Claude-Session: https://claude.ai/code/session_013Qe1Vf2yvj33BVd5W1q7HB
---
 website/src/pages/blog/[slug].astro | 88 +++++++++++++++--------------
 website/src/styles/global.css       | 30 ++++++----
 2 files changed, 65 insertions(+), 53 deletions(-)

diff --git a/website/src/pages/blog/[slug].astro b/website/src/pages/blog/[slug].astro
index 3f60873790..bc12aae183 100644
--- a/website/src/pages/blog/[slug].astro
+++ b/website/src/pages/blog/[slug].astro
@@ -13,7 +13,7 @@ export const getStaticPaths = (async () => {
 }) satisfies GetStaticPaths;
 
 const { post } = Astro.props;
-const { title, description, category, date, heroImage, ogImage, readingTime } =
+const { title, description, date, heroImage, ogImage, readingTime } =
   post.data;
 
 // Resolve every contributor (primary first, then co-authors) for the byline.
@@ -87,8 +87,8 @@ const sectionBorder = "border:1px solid rgba(255,255,255,0.07);margin-top:-1px;"
   <article style={`background:#0E0D0F;${sectionBorder}padding:0 0 24px;`}>
     <!-- header -->
     <div
-      class="ag-pad"
-      style="max-width:760px;margin:0 auto;padding:56px 24px 36px;text-align:center;display:flex;flex-direction:column;align-items:center;gap:20px;"
+      class="ag-pad ag-post-head"
+      style="max-width:760px;margin:0 auto;padding:56px 24px 44px;text-align:center;display:flex;flex-direction:column;align-items:center;gap:20px;"
     >
       <a
         href="/blog"
@@ -103,10 +103,6 @@ const sectionBorder = "border:1px solid rgba(255,255,255,0.07);margin-top:-1px;"
             stroke-linejoin="round"></path></svg
         >All blogs
       </a>
-      <span
-        style="display:inline-flex;align-items:center;height:24px;padding:0 13px;border-radius:999px;background:rgba(242,242,92,0.12);box-shadow:inset 0 0 0 1px rgba(242,242,92,0.3);font:var(--text-caption);letter-spacing:0.04em;color:var(--yellow-400);"
-        >{category}</span
-      >
       <h1
         class="ag-h1"
         style="margin:0;font:300 52px/1.08 var(--font-display);color:#F7F6F4;text-wrap:pretty;"
@@ -114,63 +110,61 @@ const sectionBorder = "border:1px solid rgba(255,255,255,0.07);margin-top:-1px;"
         {title}
       </h1>
       <p
-        style="margin:0;font:var(--text-body-md);color:rgba(255,255,255,0.6);max-width:600px;text-wrap:pretty;"
+        style="margin:0;font:400 19px/1.5 var(--font-sans);letter-spacing:-0.011em;color:rgba(255,255,255,0.62);max-width:600px;text-wrap:pretty;"
       >
         {description}
       </p>
 
-      <!-- byline: stacked avatars · author name(s) · role · date · reading time -->
+      <!-- date · reading time: quiet single line directly below the subtitle -->
+      <div
+        style="display:flex;align-items:center;justify-content:center;gap:9px;margin-top:-8px;font:var(--text-caption);letter-spacing:0.03em;color:rgba(255,255,255,0.45);"
+      >
+        <span>{dateLabel}</span>
+        {readingTime && (
+          <>
+            <span style="width:3px;height:3px;border-radius:50%;background:rgba(255,255,255,0.3);" />
+            <span>{readingTime}</span>
+          </>
+        )}
+      </div>
+
+      <!-- byline: stacked avatars · author name(s), vertically centered -->
       {
         primaryAuthor && (
           <div
             class="ag-author"
-            style="display:flex;align-items:center;justify-content:center;gap:14px;margin-top:4px;"
+            style="display:flex;align-items:center;justify-content:center;gap:12px;margin-top:4px;"
           >
             <div style="flex:0 0 auto;display:flex;align-items:center;">
               {authors.map((a, i) => (
                 <a
                   href={`/authors/${a.id}`}
                   aria-label={a.data.name}
-                  style={`display:block;${i > 0 ? "margin-left:-14px;" : ""}`}
+                  style={`display:block;${i > 0 ? "margin-left:-12px;" : ""}`}
                 >
                   <img
                     src={a.data.avatar}
                     alt={a.data.name}
-                    width="56"
-                    height="56"
-                    style="display:block;width:56px;height:56px;border-radius:50%;object-fit:cover;box-shadow:inset 0 0 0 2px #0E0D0F,inset 0 0 0 3px rgba(255,255,255,0.1);"
+                    width="40"
+                    height="40"
+                    style="display:block;width:40px;height:40px;border-radius:50%;object-fit:cover;box-shadow:inset 0 0 0 2px #0E0D0F,inset 0 0 0 3px rgba(255,255,255,0.1);"
                   />
                 </a>
               ))}
             </div>
-            <div style="display:flex;flex-direction:column;gap:3px;text-align:left;">
-              <span style="font:var(--text-label);color:#F7F6F4;">
-                {authors.map((a, i) => (
-                  <>
-                    {i > 0 && <span style="color:rgba(255,255,255,0.45);"> & </span>}
-                    <a
-                      href={`/authors/${a.id}`}
-                      style="color:#F7F6F4;text-decoration:none;"
-                    >
-                      {a.data.name}
-                    </a>
-                  </>
-                ))}
-              </span>
-              <div
-                style="display:flex;align-items:center;gap:9px;flex-wrap:wrap;font:var(--text-caption);letter-spacing:0.03em;color:rgba(255,255,255,0.45);"
-              >
-                <span>{primaryAuthor.data.role}</span>
-                <span style="width:3px;height:3px;border-radius:50%;background:rgba(255,255,255,0.3);" />
-                <span>{dateLabel}</span>
-                {readingTime && (
-                  <>
-                    <span style="width:3px;height:3px;border-radius:50%;background:rgba(255,255,255,0.3);" />
-                    <span>{readingTime}</span>
-                  </>
-                )}
-              </div>
-            </div>
+            <span style="font:var(--text-label);color:#F7F6F4;">
+              {authors.map((a, i) => (
+                <>
+                  {i > 0 && <span style="color:rgba(255,255,255,0.45);"> & </span>}
+                  <a
+                    href={`/authors/${a.id}`}
+                    style="color:#F7F6F4;text-decoration:none;"
+                  >
+                    {a.data.name}
+                  </a>
+                </>
+              ))}
+            </span>
           </div>
         )
       }
@@ -200,7 +194,7 @@ const sectionBorder = "border:1px solid rgba(255,255,255,0.07);margin-top:-1px;"
 
     <!-- body -->
     <div
-      class="ag-prose ag-pad"
+      class="ag-prose ag-pad ag-post-body"
       style="max-width:720px;margin:0 auto;padding:48px 24px 8px;"
     >
       <Content components={{ InlineCTA, img: BlogImage }} />
@@ -267,6 +261,14 @@ const sectionBorder = "border:1px solid rgba(255,255,255,0.07);margin-top:-1px;"
         padding-left: 20px !important;
         padding-right: 20px !important;
       }
+      /* Hero image breathes proportionally less on phones. */
+      .ag-post-head {
+        padding-top: 44px !important;
+        padding-bottom: 30px !important;
+      }
+      .ag-post-body {
+        padding-top: 32px !important;
+      }
     }
   </style>
 </Site>
diff --git a/website/src/styles/global.css b/website/src/styles/global.css
index 892a548a49..1dc7c801f3 100644
--- a/website/src/styles/global.css
+++ b/website/src/styles/global.css
@@ -562,40 +562,50 @@ html,
    Body is a muted warm white for comfortable reading; headings brighter; links
    carry the brand accent; code sits on a subtle raised surface.
 ============================================================================ */
+/* Body scale tuned against Linear's blog (linear.app/now): 15–16px regular 400
+   with a slight negative tracking reads far lighter than a heavier 17px block.
+   We keep Inter for body and GT Alpina for headings — only size/weight/leading
+   and tracking follow Linear. */
 .ag-prose {
   color: rgba(255, 255, 255, 0.72);
   font-family: var(--font-sans);
-  font-size: 17px;
-  line-height: 1.75;
+  font-size: 16px;
+  font-weight: 400;
+  line-height: 1.65;
+  letter-spacing: -0.011em;
 }
 .ag-prose > :first-child {
   margin-top: 0;
 }
 .ag-prose p {
-  margin: 0 0 1.15em;
+  margin: 0 0 1.25em;
 }
 .ag-prose h2 {
-  margin: 2em 0 0.6em;
-  font: 400 30px/1.2 var(--font-display);
+  margin: 2em 0 0.55em;
+  font: 400 26px/1.25 var(--font-display);
+  letter-spacing: -0.015em;
   color: var(--text-on-dark-heading);
   text-wrap: pretty;
 }
 .ag-prose h3 {
-  margin: 1.6em 0 0.5em;
-  font: 400 23px/1.3 var(--font-display);
+  margin: 1.7em 0 0.5em;
+  font: 400 20px/1.3 var(--font-display);
+  letter-spacing: -0.012em;
   color: var(--text-on-dark-heading);
   text-wrap: pretty;
 }
 .ag-prose h4 {
-  margin: 1.4em 0 0.4em;
-  font: 600 17px/1.4 var(--font-sans);
+  margin: 1.5em 0 0.4em;
+  font: 600 16px/1.4 var(--font-sans);
+  letter-spacing: -0.006em;
   color: var(--text-on-dark-heading);
 }
 .ag-prose a {
   color: var(--yellow-400);
+  font-weight: 400;
   text-decoration: underline;
   text-underline-offset: 2px;
-  text-decoration-color: rgba(242, 242, 92, 0.4);
+  text-decoration-color: rgba(242, 242, 92, 0.55);
 }
 .ag-prose a:hover {
   text-decoration-color: var(--yellow-400);

From b27ba44cbdcc6d582ee917d90150835a9dc50902 Mon Sep 17 00:00:00 2001
From: Mahmoud Mabrouk <mahmoud@agenta.ai>
Date: Wed, 22 Jul 2026 23:11:51 +0200
Subject: [PATCH 4/9] Revise introducing-agenta-2-0.mdx for clarity and context

Updated text for clarity, improved phrasing, and added context throughout the document.
---
 .../content/posts/introducing-agenta-2-0.mdx  | 74 +++++++++----------
 1 file changed, 33 insertions(+), 41 deletions(-)

diff --git a/website/src/content/posts/introducing-agenta-2-0.mdx b/website/src/content/posts/introducing-agenta-2-0.mdx
index 9e08431398..27dc95d161 100644
--- a/website/src/content/posts/introducing-agenta-2-0.mdx
+++ b/website/src/content/posts/introducing-agenta-2-0.mdx
@@ -14,35 +14,37 @@ tags: ["Article"]
 
 Today, we are introducing Agenta 2.0, an open-source workspace for building and running agents.
 
-Agenta lets you build specialized agents for different areas of work. You can work with them as AI coworkers, share them with your team, and use them to automate recurring work.
+Agenta lets you build specialized agents for different areas of work. You can work with them as AI coworkers, share them with your team, and use them to automate recurring tasks.
 
 ## Agents are becoming a new building block for work
 
-Agents have reached a point where they can perform and organize substantial work.
+Agents have reached a point where they can help with a [large share of tasks across a company](https://www.anthropic.com/research/labor-market-impacts).
 
-Give an agent a goal, the right context, and access to tools, and it can decide how to approach the job. It can inspect information, use applications, write scripts, and adapt when the situation changes.
+Give an agent a goal, clear instructions, the right context, and access to tools, and it can complete many tasks autonomously. It can inspect information, use applications, write scripts, and adapt when the situation changes.
 
-Until now, automating work often meant defining every step in advance. You designed a workflow and specified what should happen at each branch. This remains the right approach when a process is fixed and every step must be predictable.
+Until now, automating work often meant defining every step in advance. You designed a deterministic workflow and specified what should happen at each branch. This remains the right approach in high-governance settings where every step must be predictable.
 
-But much of our work is not like that. Research, product decisions, customer support, sales, and company operations require context and judgment. The right next step depends on what the system finds.
+But much of our work is not like that. Research, product decisions, customer support, sales, and company operations require context and judgment. The right next step depends on what the system finds. Agents have become much better at handling this kind of work.
 
-An agent can become the basic unit that performs this work. At a technical level, we think of the agent as a new compute primitive. In practical terms, you describe the job, provide the context and tools, and let the agent create the logic it needs.
+An agent can become the basic unit that performs this work. At a technical level, we think of the agent as a new compute primitive. In practical terms, you describe the job, provide the context and tools, and let the agent reason through the problem.
 
-People still provide direction, feedback, and judgment. Agents take on more of the execution.
+People still provide direction, feedback, and judgment. They do not need to encode every path. Agents take on more of the execution.
 
 ## Building useful agents still requires too much work
 
-Most companies are not working this way yet.
+Most companies are not yet using agents to their full potential.
 
 A useful agent needs company context. It needs instructions, files, skills, tools, and access to the applications where work happens. People need a way to improve it, share it, run it again, and understand what happened when it makes a mistake.
 
+Most importantly, the people doing the work need to be able to create their own agents. They understand the work best.
+
 Today, this capability is spread across several kinds of products.
 
-Coding agents such as Claude Code, Codex, and Pi give individual technical users a strong environment. Closed AI workspaces provide a polished experience, but limit how much users can inspect, change, or move. Visual workflow builders work well when users can define the process in advance. Agent frameworks provide flexibility, but require a team to build and operate the surrounding system.
+Coding agents such as Claude Code and Codex are very powerful, but still require technical skills. Closed AI workspaces provide a polished experience, but limit how much users can inspect, change, or move and lock them into one provider. Visual workflow builders work well when users can define the process in advance, but they do not fully use what this new agent paradigm makes possible.
 
 The result is an awkward choice between agency, ease of use, automation, and control.
 
-We think agents need an open workspace where people can build them through use, give them lasting context, share them with a team, automate suitable work, and control how they run.
+We think teams need an open workspace where every team member can build agents by working with them, give them lasting context, share them with the team, automate suitable work, and control how they run.
 
 ## A workspace for you and your agents
 
@@ -52,23 +54,21 @@ You start with a real job and describe it in chat. The agent works with you, use
 
 Each agent has a persistent workspace. It can maintain research, documents, plans, reports, and other artifacts instead of starting every conversation without context.
 
-You improve the agent through real work and feedback. When it becomes useful, you can share it with your team. For suitable recurring work, you can run it on a schedule or when an event occurs in a connected application.
+You improve the agent through real work and feedback, then share it with your team. For recurring work, you can create loops that run on a schedule or when an event occurs in a connected application.
 
-The AI coworker and the automation are not separate systems. They are two ways to use the same agent. You can work with it directly when the job needs discussion, then let it handle understood parts of the work in the background.
+In other words, the AI coworker and the automation are not separate systems. They are two ways to use the same agent. You can work with it directly when the job needs discussion, then let it handle understood parts of the work in the background.
 
-## Open agents, open choices
+## Using open standards
 
-The agent itself remains simple and inspectable. An Agenta agent is built from components such as `AGENTS.md`, skills, tools, and files. You can see and change how it works.
+The agent itself remains simple and portable. An Agenta agent is built from standard components: `AGENTS.md`, skills, tools, and MCP servers. You can see and change how it works.
 
-Agenta adds the workspace, sharing, triggers, versions, and operational history around the agent. It keeps the agent separate from the model and harness that run it.
+This means two things. First, you can reuse compatible MCP servers and skills from the broader agent ecosystem.
 
-This means you can choose the right setup for the job without rebuilding the agent from the beginning.
+Second, you can switch supported models or harnesses without rebuilding the agent. You can choose the right setup for each task without being locked into one provider.
 
 We already work this way inside Agenta. Our go-to-market work uses GPT-5.6. We use a different coding harness for engineering. When speed matters, we use GLM 5.2 Nitro through OpenRouter. In our self-hosted setup, personal agents can use our existing Claude Code and Codex subscriptions.
 
-{/* VERIFY BEFORE PUBLICATION: Confirm the exact GTM model and harness, the engineering harness name, GLM 5.2 Nitro support, subscription setup, portability limits, and every stated stack combination. */}
-
-The important point is not the number of supported providers. It is that the agent and its work do not have to begin again when the surrounding stack changes.
+Our vision is to let you run your agents almost anywhere: locally, in Agenta Cloud, or through execution environments such as Daytona. Over time, we plan to support more environments, including E2B, Vercel, and AWS.
 
 ## A go-to-market agent in practice
 
@@ -78,40 +78,32 @@ As a technical founder, I work across product, engineering, research, positionin
 
 My go-to-market agent works with a persistent workspace containing our product context, research, positioning, decisions, launch plans, and drafts. I use it in chat to research questions, compare new information with earlier decisions, update documents, and prepare assets. This announcement is one output from that ongoing work.
 
-I also use agents for recurring work. One helps operate our social listening through Octolens, where it manages research vocabulary, monitoring feeds, and the decisions behind them.
-
-{/* VERIFY BEFORE PUBLICATION: Confirm the exact Octolens automation, its trigger, what it performs automatically, and what still requires Mahmoud's approval. Link the public example only if it exposes no private configuration or credentials. */}
+I also use agents for recurring work. One, for instance, helps operate our social listening through [Octolens](https://octolens.com/), where it manages research vocabulary, monitoring feeds, and the decisions behind them.
 
-The agent does not make company decisions for me. It gathers and structures information, records context, and helps perform the work. I provide direction, correct it, and make the final decisions.
+The agent does not make decisions for me. It gathers and structures information, records context, and helps perform the work. Over time, I give it feedback and refine it to fit the way I work. It gives me immense leverage.
 
-## Reliability comes from improvement
+## Reliability and continuous improvement
 
-The first version of Agenta focused on helping teams build, evaluate, and observe applications powered by language models. That foundation remains important because agents still make mistakes.
+The first version of Agenta focused on helping teams build, evaluate, and observe applications powered by LLMs. That foundation remains important because reliability is still a major challenge when building agents.
 
-For a company, reliability is not only about whether one run succeeds. It is about making an agent better at a defined job over time.
+When we started Agenta, LLMs were poor at following instructions and understanding what users meant. Much of our time went into prompt engineering and complex workflows designed to compensate. Today, agents are far better at following instructions. Many workflows can now be expressed as instructions to an agent instead of being defined step by step.
 
-First, people need to state what good work looks like. Then they need to review real results and give feedback. When something goes wrong, they need to inspect the trace, understand which instruction or action caused the problem, and change the agent without losing what already worked.
+Reliability today is about explaining the job clearly. Say you are building an agent to reach out to prospects. How do you define your ideal customer profile? What style should the agent follow? What should it focus on when researching a prospect, and what should it ignore?
 
-Agenta supports this improvement loop through tracing, annotations, evaluations, and versioning. An annotation records structured feedback on a run. An evaluation checks an output against a defined expectation. Versioning connects each result to the agent configuration that produced it.
+You cannot build a reliable agent that performs a job successfully without answering these questions. There is no shortcut. You need to review real results, give feedback, and iterate.
 
-Together, these tools help teams make agents more reliable and predictable. They do not make a language model deterministic. They provide the evidence needed to improve the agent and decide which work still needs human review.
+Agenta provides the tools to improve agents continuously. Every run is logged and traced with its inputs and outputs. Every agent configuration is versioned. A user can give feedback, and the agent can inspect what happened, identify why it made a mistake, and propose configuration changes.
 
-Permissions and human approval add another layer of control. Teams can decide which actions an agent may perform, which require approval, and which remain blocked.
+Over time, you build more capable agents that become part of your company's operating DNA and differentiation. They encode your best practices, standard operating procedures, and ways of working.
 
-{/* VERIFY BEFORE PUBLICATION: Confirm tracing, annotations, evaluations, versioning, permissions, approval behavior, and configuration restoration in the launch build. */}
+The second layer of reliability comes from permissions and human approval. We built Agenta with a detailed permission system and human-in-the-loop controls. Teams can decide which actions an agent may perform, which require approval, and which remain blocked.
 
 ## Getting started
 
-Agenta 2.0 is available through Agenta Cloud and as a self-hosted product.
-
-{/* VERIFY BEFORE PUBLICATION: Confirm availability, release name, license, tested installation path, and destination links. */}
-
-Start with one area of work. Build an agent around a real job, give it the context it needs, and improve it through use. Once it works well, share it with your team or let it handle suitable parts of the work in the background.
-
-Agenta is still early. Some integrations and execution options require additional setup. Our documentation will explain the current limits and the setup required.
+Agenta 2.0 is available through Agenta Cloud and as an open-source, self-hosted product.
 
-[Try Agenta Cloud](https://cloud.agenta.ai), [view Agenta on GitHub](https://github.com/Agenta-AI/agenta), or follow the self-hosting guide to run it yourself.
+Start with a template. Ask an agent to customize it, then give it the context and access to the tools it needs. Improve it through use. Over time, you can automate parts of the work and build a system that gives your whole team far more leverage.
 
-We believe the people who understand the work should be able to build the agents that do it.
+[Try Agenta Cloud](https://cloud.agenta.ai), [star Agenta on GitHub](https://github.com/Agenta-AI/agenta), or follow the self-hosting guide to run it yourself.
 
-If you build an agent, tell us what work you gave it, where it helped, and where it failed. That feedback will shape what we build next.
+We believe the people who understand the work should be able to build the agents that do it. We want to help build the next million agents.

From 929c3c918471447778fb384f39e5d7a3cfbb0853 Mon Sep 17 00:00:00 2001
From: Mahmoud Mabrouk <mahmoud@agenta.ai>
Date: Wed, 22 Jul 2026 23:12:08 +0200
Subject: [PATCH 5/9] Update featured status to true for Agenta 2.0 post

---
 website/src/content/posts/introducing-agenta-2-0.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/src/content/posts/introducing-agenta-2-0.mdx b/website/src/content/posts/introducing-agenta-2-0.mdx
index 27dc95d161..d70e9ec766 100644
--- a/website/src/content/posts/introducing-agenta-2-0.mdx
+++ b/website/src/content/posts/introducing-agenta-2-0.mdx
@@ -8,7 +8,7 @@ readingTime: "6 min read"
 heroImage: /blog/introducing-agenta-2-0/hero.png
 ogImage: /blog/introducing-agenta-2-0/hero.png
 author: mahmoud-mabrouk
-featured: false
+featured: true
 tags: ["Article"]
 ---
 

From 1cbfa82e455dadd9c2fa2b26c3967aec63dce9a2 Mon Sep 17 00:00:00 2001
From: Mahmoud Mabrouk <mahmoud@agenta.ai>
Date: Wed, 22 Jul 2026 23:13:39 +0200
Subject: [PATCH 6/9] Revise links in introducing-agenta-2-0.mdx

Updated links in the Agenta 2.0 introduction post for clarity.
---
 website/src/content/posts/introducing-agenta-2-0.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/website/src/content/posts/introducing-agenta-2-0.mdx b/website/src/content/posts/introducing-agenta-2-0.mdx
index d70e9ec766..02f2184d33 100644
--- a/website/src/content/posts/introducing-agenta-2-0.mdx
+++ b/website/src/content/posts/introducing-agenta-2-0.mdx
@@ -104,6 +104,6 @@ Agenta 2.0 is available through Agenta Cloud and as an open-source, self-hosted
 
 Start with a template. Ask an agent to customize it, then give it the context and access to the tools it needs. Improve it through use. Over time, you can automate parts of the work and build a system that gives your whole team far more leverage.
 
-[Try Agenta Cloud](https://cloud.agenta.ai), [star Agenta on GitHub](https://github.com/Agenta-AI/agenta), or follow the self-hosting guide to run it yourself.
+[Try Agenta Cloud](https://cloud.agenta.ai), or copy the prompt in the [self-hosting guide](https://agenta.ai/docs/self-host/overview) to run it yourself.
 
 We believe the people who understand the work should be able to build the agents that do it. We want to help build the next million agents.

From df24b07ffd60f7b01e72c1bd6442435b85c55e0d Mon Sep 17 00:00:00 2001
From: Mahmoud Mabrouk <mahmoud@agenta.ai>
Date: Wed, 22 Jul 2026 23:27:34 +0200
Subject: [PATCH 7/9] Revise content in introducing-agenta-2-0.mdx

Updated text for clarity and added links to resources.
---
 .../content/posts/introducing-agenta-2-0.mdx   | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/website/src/content/posts/introducing-agenta-2-0.mdx b/website/src/content/posts/introducing-agenta-2-0.mdx
index 02f2184d33..7cb1a5d81a 100644
--- a/website/src/content/posts/introducing-agenta-2-0.mdx
+++ b/website/src/content/posts/introducing-agenta-2-0.mdx
@@ -62,13 +62,13 @@ In other words, the AI coworker and the automation are not separate systems. The
 
 The agent itself remains simple and portable. An Agenta agent is built from standard components: `AGENTS.md`, skills, tools, and MCP servers. You can see and change how it works.
 
-This means two things. First, you can reuse compatible MCP servers and skills from the broader agent ecosystem.
+This means three things. First, you can reuse compatible MCP servers and skills from the broader agent ecosystem.
 
 Second, you can switch supported models or harnesses without rebuilding the agent. You can choose the right setup for each task without being locked into one provider.
 
-We already work this way inside Agenta. Our go-to-market work uses GPT-5.6. We use a different coding harness for engineering. When speed matters, we use GLM 5.2 Nitro through OpenRouter. In our self-hosted setup, personal agents can use our existing Claude Code and Codex subscriptions.
+We already work this way inside Agenta. Our go-to-market work uses [pi](https://pi.dev) with GPT-5.6. We use Claude Code with Opus 4.8 for engineering. For day to day tasks, where speed matters, we use [GLM 5.2 Nitro](https://openrouter.ai/z-ai/glm-5.2:nitro) through [OpenRouter](https://openrouter.ai/). In our personal local self-hosted setup, our personal agents use our existing Claude Code and ChatGPT subscriptions.
 
-Our vision is to let you run your agents almost anywhere: locally, in Agenta Cloud, or through execution environments such as Daytona. Over time, we plan to support more environments, including E2B, Vercel, and AWS.
+Last, open standards allow you to run your agents almost anywhere. Right now we support Agenta Cloud, local in docker containers or through [Daytona](https://daytona.io/) sandboxes. Over time, we plan to support more environments, including [E2B](https://e2b.dev/), [Vercel sandboxes](https://vercel.com/docs/sandbox), and [Modal](.
 
 ## A go-to-market agent in practice
 
@@ -86,24 +86,24 @@ The agent does not make decisions for me. It gathers and structures information,
 
 The first version of Agenta focused on helping teams build, evaluate, and observe applications powered by LLMs. That foundation remains important because reliability is still a major challenge when building agents.
 
-When we started Agenta, LLMs were poor at following instructions and understanding what users meant. Much of our time went into prompt engineering and complex workflows designed to compensate. Today, agents are far better at following instructions. Many workflows can now be expressed as instructions to an agent instead of being defined step by step.
+When we started Agenta, LLMs were poor at following instructions and understanding what users meant. Much of our users time went into prompt engineering and building complex workflows designed to compensate. Today, agents are far better at following instructions. Most workflows can now be expressed as instructions to an agent instead of being defined step by step.
 
 Reliability today is about explaining the job clearly. Say you are building an agent to reach out to prospects. How do you define your ideal customer profile? What style should the agent follow? What should it focus on when researching a prospect, and what should it ignore?
 
-You cannot build a reliable agent that performs a job successfully without answering these questions. There is no shortcut. You need to review real results, give feedback, and iterate.
+You cannot build a reliable agent that performs a job successfully without answering these questions. To achieve tht, there is no shortcut; you need to review the agent results, give feedback, and iterate.
 
 Agenta provides the tools to improve agents continuously. Every run is logged and traced with its inputs and outputs. Every agent configuration is versioned. A user can give feedback, and the agent can inspect what happened, identify why it made a mistake, and propose configuration changes.
 
-Over time, you build more capable agents that become part of your company's operating DNA and differentiation. They encode your best practices, standard operating procedures, and ways of working.
+Over time, you build more capable agents that become part of your company's operating DNA and differentiation. They encode your best practices, standard operating procedures, and your ways of working.
 
 The second layer of reliability comes from permissions and human approval. We built Agenta with a detailed permission system and human-in-the-loop controls. Teams can decide which actions an agent may perform, which require approval, and which remain blocked.
 
 ## Getting started
 
-Agenta 2.0 is available through Agenta Cloud and as an open-source, self-hosted product.
+Agenta 2.0 is today available through Agenta Cloud and as an open-source, self-hosted product.
 
 Start with a template. Ask an agent to customize it, then give it the context and access to the tools it needs. Improve it through use. Over time, you can automate parts of the work and build a system that gives your whole team far more leverage.
 
-[Try Agenta Cloud](https://cloud.agenta.ai), or copy the prompt in the [self-hosting guide](https://agenta.ai/docs/self-host/overview) to run it yourself.
+Get started for free in [Agenta Cloud](https://cloud.agenta.ai), or copy the prompt in the [self-hosting guide](https://agenta.ai/docs/self-host/overview) to run it yourself in minutes.
 
-We believe the people who understand the work should be able to build the agents that do it. We want to help build the next million agents.
+We believe the people who understand the work should be able to build the agents that do it. We want to help them build the next million agents.

From 364ee3b85eaea442902706020aadb23f371271ca Mon Sep 17 00:00:00 2001
From: Mahmoud Mabrouk <mahmoud@agenta.ai>
Date: Wed, 22 Jul 2026 23:33:06 +0200
Subject: [PATCH 8/9] feat(website): feature the Agenta 2.0 announcement as the
 primary blog card

Announcement takes featuredRank 1, the prompt-management guide moves to
rank 3, the chunking guide returns to the grid. The rank swap rides this
branch so the featured row never points at an unpublished post.

Claude-Session: https://claude.ai/code/session_013Qe1Vf2yvj33BVd5W1q7HB
---
 .../content/posts/introducing-agenta-2-0.mdx  | 77 +++++++++++--------
 ...ive-guide-to-prompt-management-systems.mdx |  2 +-
 ...ultimate-guide-for-chunking-strategies.mdx |  3 +-
 3 files changed, 45 insertions(+), 37 deletions(-)

diff --git a/website/src/content/posts/introducing-agenta-2-0.mdx b/website/src/content/posts/introducing-agenta-2-0.mdx
index 7cb1a5d81a..a6791a3b94 100644
--- a/website/src/content/posts/introducing-agenta-2-0.mdx
+++ b/website/src/content/posts/introducing-agenta-2-0.mdx
@@ -9,42 +9,41 @@ heroImage: /blog/introducing-agenta-2-0/hero.png
 ogImage: /blog/introducing-agenta-2-0/hero.png
 author: mahmoud-mabrouk
 featured: true
+featuredRank: 1
 tags: ["Article"]
 ---
 
 Today, we are introducing Agenta 2.0, an open-source workspace for building and running agents.
 
-Agenta lets you build specialized agents for different areas of work. You can work with them as AI coworkers, share them with your team, and use them to automate recurring tasks.
+Agenta lets you build specialized agents for different areas of work. You can work with them as AI coworkers, share them with your team, and use them to automate recurring work.
 
 ## Agents are becoming a new building block for work
 
-Agents have reached a point where they can help with a [large share of tasks across a company](https://www.anthropic.com/research/labor-market-impacts).
+Agents have reached a point where they can perform and organize substantial work.
 
-Give an agent a goal, clear instructions, the right context, and access to tools, and it can complete many tasks autonomously. It can inspect information, use applications, write scripts, and adapt when the situation changes.
+Give an agent a goal, the right context, and access to tools, and it can decide how to approach the job. It can inspect information, use applications, write scripts, and adapt when the situation changes.
 
-Until now, automating work often meant defining every step in advance. You designed a deterministic workflow and specified what should happen at each branch. This remains the right approach in high-governance settings where every step must be predictable.
+Until now, automating work often meant defining every step in advance. You designed a workflow and specified what should happen at each branch. This remains the right approach when a process is fixed and every step must be predictable.
 
-But much of our work is not like that. Research, product decisions, customer support, sales, and company operations require context and judgment. The right next step depends on what the system finds. Agents have become much better at handling this kind of work.
+But much of our work is not like that. Research, product decisions, customer support, sales, and company operations require context and judgment. The right next step depends on what the system finds.
 
-An agent can become the basic unit that performs this work. At a technical level, we think of the agent as a new compute primitive. In practical terms, you describe the job, provide the context and tools, and let the agent reason through the problem.
+An agent can become the basic unit that performs this work. At a technical level, we think of the agent as a new compute primitive. In practical terms, you describe the job, provide the context and tools, and let the agent create the logic it needs.
 
-People still provide direction, feedback, and judgment. They do not need to encode every path. Agents take on more of the execution.
+People still provide direction, feedback, and judgment. Agents take on more of the execution.
 
 ## Building useful agents still requires too much work
 
-Most companies are not yet using agents to their full potential.
+Most companies are not working this way yet.
 
 A useful agent needs company context. It needs instructions, files, skills, tools, and access to the applications where work happens. People need a way to improve it, share it, run it again, and understand what happened when it makes a mistake.
 
-Most importantly, the people doing the work need to be able to create their own agents. They understand the work best.
-
 Today, this capability is spread across several kinds of products.
 
-Coding agents such as Claude Code and Codex are very powerful, but still require technical skills. Closed AI workspaces provide a polished experience, but limit how much users can inspect, change, or move and lock them into one provider. Visual workflow builders work well when users can define the process in advance, but they do not fully use what this new agent paradigm makes possible.
+Coding agents such as Claude Code, Codex, and Pi give individual technical users a strong environment. Closed AI workspaces provide a polished experience, but limit how much users can inspect, change, or move. Visual workflow builders work well when users can define the process in advance. Agent frameworks provide flexibility, but require a team to build and operate the surrounding system.
 
 The result is an awkward choice between agency, ease of use, automation, and control.
 
-We think teams need an open workspace where every team member can build agents by working with them, give them lasting context, share them with the team, automate suitable work, and control how they run.
+We think agents need an open workspace where people can build them through use, give them lasting context, share them with a team, automate suitable work, and control how they run.
 
 ## A workspace for you and your agents
 
@@ -54,21 +53,23 @@ You start with a real job and describe it in chat. The agent works with you, use
 
 Each agent has a persistent workspace. It can maintain research, documents, plans, reports, and other artifacts instead of starting every conversation without context.
 
-You improve the agent through real work and feedback, then share it with your team. For recurring work, you can create loops that run on a schedule or when an event occurs in a connected application.
+You improve the agent through real work and feedback. When it becomes useful, you can share it with your team. For suitable recurring work, you can run it on a schedule or when an event occurs in a connected application.
+
+The AI coworker and the automation are not separate systems. They are two ways to use the same agent. You can work with it directly when the job needs discussion, then let it handle understood parts of the work in the background.
 
-In other words, the AI coworker and the automation are not separate systems. They are two ways to use the same agent. You can work with it directly when the job needs discussion, then let it handle understood parts of the work in the background.
+## Open agents, open choices
 
-## Using open standards
+The agent itself remains simple and inspectable. An Agenta agent is built from components such as `AGENTS.md`, skills, tools, and files. You can see and change how it works.
 
-The agent itself remains simple and portable. An Agenta agent is built from standard components: `AGENTS.md`, skills, tools, and MCP servers. You can see and change how it works.
+Agenta adds the workspace, sharing, triggers, versions, and operational history around the agent. It keeps the agent separate from the model and harness that run it.
 
-This means three things. First, you can reuse compatible MCP servers and skills from the broader agent ecosystem.
+This means you can choose the right setup for the job without rebuilding the agent from the beginning.
 
-Second, you can switch supported models or harnesses without rebuilding the agent. You can choose the right setup for each task without being locked into one provider.
+We already work this way inside Agenta. Our go-to-market work uses GPT-5.6. We use a different coding harness for engineering. When speed matters, we use GLM 5.2 Nitro through OpenRouter. In our self-hosted setup, personal agents can use our existing Claude Code and Codex subscriptions.
 
-We already work this way inside Agenta. Our go-to-market work uses [pi](https://pi.dev) with GPT-5.6. We use Claude Code with Opus 4.8 for engineering. For day to day tasks, where speed matters, we use [GLM 5.2 Nitro](https://openrouter.ai/z-ai/glm-5.2:nitro) through [OpenRouter](https://openrouter.ai/). In our personal local self-hosted setup, our personal agents use our existing Claude Code and ChatGPT subscriptions.
+{/* VERIFY BEFORE PUBLICATION: Confirm the exact GTM model and harness, the engineering harness name, GLM 5.2 Nitro support, subscription setup, portability limits, and every stated stack combination. */}
 
-Last, open standards allow you to run your agents almost anywhere. Right now we support Agenta Cloud, local in docker containers or through [Daytona](https://daytona.io/) sandboxes. Over time, we plan to support more environments, including [E2B](https://e2b.dev/), [Vercel sandboxes](https://vercel.com/docs/sandbox), and [Modal](.
+The important point is not the number of supported providers. It is that the agent and its work do not have to begin again when the surrounding stack changes.
 
 ## A go-to-market agent in practice
 
@@ -78,32 +79,40 @@ As a technical founder, I work across product, engineering, research, positionin
 
 My go-to-market agent works with a persistent workspace containing our product context, research, positioning, decisions, launch plans, and drafts. I use it in chat to research questions, compare new information with earlier decisions, update documents, and prepare assets. This announcement is one output from that ongoing work.
 
-I also use agents for recurring work. One, for instance, helps operate our social listening through [Octolens](https://octolens.com/), where it manages research vocabulary, monitoring feeds, and the decisions behind them.
+I also use agents for recurring work. One helps operate our social listening through Octolens, where it manages research vocabulary, monitoring feeds, and the decisions behind them.
+
+{/* VERIFY BEFORE PUBLICATION: Confirm the exact Octolens automation, its trigger, what it performs automatically, and what still requires Mahmoud's approval. Link the public example only if it exposes no private configuration or credentials. */}
 
-The agent does not make decisions for me. It gathers and structures information, records context, and helps perform the work. Over time, I give it feedback and refine it to fit the way I work. It gives me immense leverage.
+The agent does not make company decisions for me. It gathers and structures information, records context, and helps perform the work. I provide direction, correct it, and make the final decisions.
 
-## Reliability and continuous improvement
+## Reliability comes from improvement
 
-The first version of Agenta focused on helping teams build, evaluate, and observe applications powered by LLMs. That foundation remains important because reliability is still a major challenge when building agents.
+The first version of Agenta focused on helping teams build, evaluate, and observe applications powered by language models. That foundation remains important because agents still make mistakes.
 
-When we started Agenta, LLMs were poor at following instructions and understanding what users meant. Much of our users time went into prompt engineering and building complex workflows designed to compensate. Today, agents are far better at following instructions. Most workflows can now be expressed as instructions to an agent instead of being defined step by step.
+For a company, reliability is not only about whether one run succeeds. It is about making an agent better at a defined job over time.
 
-Reliability today is about explaining the job clearly. Say you are building an agent to reach out to prospects. How do you define your ideal customer profile? What style should the agent follow? What should it focus on when researching a prospect, and what should it ignore?
+First, people need to state what good work looks like. Then they need to review real results and give feedback. When something goes wrong, they need to inspect the trace, understand which instruction or action caused the problem, and change the agent without losing what already worked.
 
-You cannot build a reliable agent that performs a job successfully without answering these questions. To achieve tht, there is no shortcut; you need to review the agent results, give feedback, and iterate.
+Agenta supports this improvement loop through tracing, annotations, evaluations, and versioning. An annotation records structured feedback on a run. An evaluation checks an output against a defined expectation. Versioning connects each result to the agent configuration that produced it.
 
-Agenta provides the tools to improve agents continuously. Every run is logged and traced with its inputs and outputs. Every agent configuration is versioned. A user can give feedback, and the agent can inspect what happened, identify why it made a mistake, and propose configuration changes.
+Together, these tools help teams make agents more reliable and predictable. They do not make a language model deterministic. They provide the evidence needed to improve the agent and decide which work still needs human review.
 
-Over time, you build more capable agents that become part of your company's operating DNA and differentiation. They encode your best practices, standard operating procedures, and your ways of working.
+Permissions and human approval add another layer of control. Teams can decide which actions an agent may perform, which require approval, and which remain blocked.
 
-The second layer of reliability comes from permissions and human approval. We built Agenta with a detailed permission system and human-in-the-loop controls. Teams can decide which actions an agent may perform, which require approval, and which remain blocked.
+{/* VERIFY BEFORE PUBLICATION: Confirm tracing, annotations, evaluations, versioning, permissions, approval behavior, and configuration restoration in the launch build. */}
 
 ## Getting started
 
-Agenta 2.0 is today available through Agenta Cloud and as an open-source, self-hosted product.
+Agenta 2.0 is available through Agenta Cloud and as a self-hosted product.
+
+{/* VERIFY BEFORE PUBLICATION: Confirm availability, release name, license, tested installation path, and destination links. */}
+
+Start with one area of work. Build an agent around a real job, give it the context it needs, and improve it through use. Once it works well, share it with your team or let it handle suitable parts of the work in the background.
+
+Agenta is still early. Some integrations and execution options require additional setup. Our documentation will explain the current limits and the setup required.
 
-Start with a template. Ask an agent to customize it, then give it the context and access to the tools it needs. Improve it through use. Over time, you can automate parts of the work and build a system that gives your whole team far more leverage.
+[Try Agenta Cloud](https://cloud.agenta.ai), [view Agenta on GitHub](https://github.com/Agenta-AI/agenta), or follow the self-hosting guide to run it yourself.
 
-Get started for free in [Agenta Cloud](https://cloud.agenta.ai), or copy the prompt in the [self-hosting guide](https://agenta.ai/docs/self-host/overview) to run it yourself in minutes.
+We believe the people who understand the work should be able to build the agents that do it.
 
-We believe the people who understand the work should be able to build the agents that do it. We want to help them build the next million agents.
+If you build an agent, tell us what work you gave it, where it helped, and where it failed. That feedback will shape what we build next.
diff --git a/website/src/content/posts/the-definitive-guide-to-prompt-management-systems.mdx b/website/src/content/posts/the-definitive-guide-to-prompt-management-systems.mdx
index 28a0fae614..c8cf7bd627 100644
--- a/website/src/content/posts/the-definitive-guide-to-prompt-management-systems.mdx
+++ b/website/src/content/posts/the-definitive-guide-to-prompt-management-systems.mdx
@@ -9,7 +9,7 @@ heroImage: /blog/the-definitive-guide-to-prompt-management-systems/hero.webp
 ogImage: /blog/the-definitive-guide-to-prompt-management-systems/hero.webp
 author: mahmoud-mabrouk
 featured: true
-featuredRank: 1
+featuredRank: 3
 tags: ["Article"]
 ---
 
diff --git a/website/src/content/posts/the-ultimate-guide-for-chunking-strategies.mdx b/website/src/content/posts/the-ultimate-guide-for-chunking-strategies.mdx
index 66058ff9da..37b7a6ea43 100644
--- a/website/src/content/posts/the-ultimate-guide-for-chunking-strategies.mdx
+++ b/website/src/content/posts/the-ultimate-guide-for-chunking-strategies.mdx
@@ -8,8 +8,7 @@ readingTime: "15 min read"
 heroImage: /blog/the-ultimate-guide-for-chunking-strategies/hero.webp
 ogImage: /blog/the-ultimate-guide-for-chunking-strategies/hero.webp
 author: ilyes-rezgui
-featured: true
-featuredRank: 3
+featured: false
 tags: ["Article"]
 ---
 

From 3299a4b654da1de368ca59d3788c9ef9f6019164 Mon Sep 17 00:00:00 2001
From: Mahmoud Mabrouk <mahmoud@agenta.ai>
Date: Wed, 22 Jul 2026 23:36:47 +0200
Subject: [PATCH 9/9] fix(website): restore founder content revisions clobbered
 by the rank commit; copy fixes

Restores the Revise-content/Revise-links copy (df24b07ffd) that the
featured-rank commit overwrote, re-applies featuredRank 1, and fixes the
review findings: day-to-day, users' time, 'that', and the unfinished
Modal link (now https://modal.com/).

Claude-Session: https://claude.ai/code/session_013Qe1Vf2yvj33BVd5W1q7HB
---
 .../content/posts/introducing-agenta-2-0.mdx  | 76 +++++++++----------
 1 file changed, 34 insertions(+), 42 deletions(-)

diff --git a/website/src/content/posts/introducing-agenta-2-0.mdx b/website/src/content/posts/introducing-agenta-2-0.mdx
index a6791a3b94..e772e075c1 100644
--- a/website/src/content/posts/introducing-agenta-2-0.mdx
+++ b/website/src/content/posts/introducing-agenta-2-0.mdx
@@ -15,35 +15,37 @@ tags: ["Article"]
 
 Today, we are introducing Agenta 2.0, an open-source workspace for building and running agents.
 
-Agenta lets you build specialized agents for different areas of work. You can work with them as AI coworkers, share them with your team, and use them to automate recurring work.
+Agenta lets you build specialized agents for different areas of work. You can work with them as AI coworkers, share them with your team, and use them to automate recurring tasks.
 
 ## Agents are becoming a new building block for work
 
-Agents have reached a point where they can perform and organize substantial work.
+Agents have reached a point where they can help with a [large share of tasks across a company](https://www.anthropic.com/research/labor-market-impacts).
 
-Give an agent a goal, the right context, and access to tools, and it can decide how to approach the job. It can inspect information, use applications, write scripts, and adapt when the situation changes.
+Give an agent a goal, clear instructions, the right context, and access to tools, and it can complete many tasks autonomously. It can inspect information, use applications, write scripts, and adapt when the situation changes.
 
-Until now, automating work often meant defining every step in advance. You designed a workflow and specified what should happen at each branch. This remains the right approach when a process is fixed and every step must be predictable.
+Until now, automating work often meant defining every step in advance. You designed a deterministic workflow and specified what should happen at each branch. This remains the right approach in high-governance settings where every step must be predictable.
 
-But much of our work is not like that. Research, product decisions, customer support, sales, and company operations require context and judgment. The right next step depends on what the system finds.
+But much of our work is not like that. Research, product decisions, customer support, sales, and company operations require context and judgment. The right next step depends on what the system finds. Agents have become much better at handling this kind of work.
 
-An agent can become the basic unit that performs this work. At a technical level, we think of the agent as a new compute primitive. In practical terms, you describe the job, provide the context and tools, and let the agent create the logic it needs.
+An agent can become the basic unit that performs this work. At a technical level, we think of the agent as a new compute primitive. In practical terms, you describe the job, provide the context and tools, and let the agent reason through the problem.
 
-People still provide direction, feedback, and judgment. Agents take on more of the execution.
+People still provide direction, feedback, and judgment. They do not need to encode every path. Agents take on more of the execution.
 
 ## Building useful agents still requires too much work
 
-Most companies are not working this way yet.
+Most companies are not yet using agents to their full potential.
 
 A useful agent needs company context. It needs instructions, files, skills, tools, and access to the applications where work happens. People need a way to improve it, share it, run it again, and understand what happened when it makes a mistake.
 
+Most importantly, the people doing the work need to be able to create their own agents. They understand the work best.
+
 Today, this capability is spread across several kinds of products.
 
-Coding agents such as Claude Code, Codex, and Pi give individual technical users a strong environment. Closed AI workspaces provide a polished experience, but limit how much users can inspect, change, or move. Visual workflow builders work well when users can define the process in advance. Agent frameworks provide flexibility, but require a team to build and operate the surrounding system.
+Coding agents such as Claude Code and Codex are very powerful, but still require technical skills. Closed AI workspaces provide a polished experience, but limit how much users can inspect, change, or move and lock them into one provider. Visual workflow builders work well when users can define the process in advance, but they do not fully use what this new agent paradigm makes possible.
 
 The result is an awkward choice between agency, ease of use, automation, and control.
 
-We think agents need an open workspace where people can build them through use, give them lasting context, share them with a team, automate suitable work, and control how they run.
+We think teams need an open workspace where every team member can build agents by working with them, give them lasting context, share them with the team, automate suitable work, and control how they run.
 
 ## A workspace for you and your agents
 
@@ -53,23 +55,21 @@ You start with a real job and describe it in chat. The agent works with you, use
 
 Each agent has a persistent workspace. It can maintain research, documents, plans, reports, and other artifacts instead of starting every conversation without context.
 
-You improve the agent through real work and feedback. When it becomes useful, you can share it with your team. For suitable recurring work, you can run it on a schedule or when an event occurs in a connected application.
-
-The AI coworker and the automation are not separate systems. They are two ways to use the same agent. You can work with it directly when the job needs discussion, then let it handle understood parts of the work in the background.
+You improve the agent through real work and feedback, then share it with your team. For recurring work, you can create loops that run on a schedule or when an event occurs in a connected application.
 
-## Open agents, open choices
+In other words, the AI coworker and the automation are not separate systems. They are two ways to use the same agent. You can work with it directly when the job needs discussion, then let it handle understood parts of the work in the background.
 
-The agent itself remains simple and inspectable. An Agenta agent is built from components such as `AGENTS.md`, skills, tools, and files. You can see and change how it works.
+## Using open standards
 
-Agenta adds the workspace, sharing, triggers, versions, and operational history around the agent. It keeps the agent separate from the model and harness that run it.
+The agent itself remains simple and portable. An Agenta agent is built from standard components: `AGENTS.md`, skills, tools, and MCP servers. You can see and change how it works.
 
-This means you can choose the right setup for the job without rebuilding the agent from the beginning.
+This means three things. First, you can reuse compatible MCP servers and skills from the broader agent ecosystem.
 
-We already work this way inside Agenta. Our go-to-market work uses GPT-5.6. We use a different coding harness for engineering. When speed matters, we use GLM 5.2 Nitro through OpenRouter. In our self-hosted setup, personal agents can use our existing Claude Code and Codex subscriptions.
+Second, you can switch supported models or harnesses without rebuilding the agent. You can choose the right setup for each task without being locked into one provider.
 
-{/* VERIFY BEFORE PUBLICATION: Confirm the exact GTM model and harness, the engineering harness name, GLM 5.2 Nitro support, subscription setup, portability limits, and every stated stack combination. */}
+We already work this way inside Agenta. Our go-to-market work uses [pi](https://pi.dev) with GPT-5.6. We use Claude Code with Opus 4.8 for engineering. For day-to-day tasks, where speed matters, we use [GLM 5.2 Nitro](https://openrouter.ai/z-ai/glm-5.2:nitro) through [OpenRouter](https://openrouter.ai/). In our personal local self-hosted setup, our personal agents use our existing Claude Code and ChatGPT subscriptions.
 
-The important point is not the number of supported providers. It is that the agent and its work do not have to begin again when the surrounding stack changes.
+Last, open standards allow you to run your agents almost anywhere. Right now we support Agenta Cloud, local in docker containers or through [Daytona](https://daytona.io/) sandboxes. Over time, we plan to support more environments, including [E2B](https://e2b.dev/), [Vercel sandboxes](https://vercel.com/docs/sandbox), and [Modal](https://modal.com/).
 
 ## A go-to-market agent in practice
 
@@ -79,40 +79,32 @@ As a technical founder, I work across product, engineering, research, positionin
 
 My go-to-market agent works with a persistent workspace containing our product context, research, positioning, decisions, launch plans, and drafts. I use it in chat to research questions, compare new information with earlier decisions, update documents, and prepare assets. This announcement is one output from that ongoing work.
 
-I also use agents for recurring work. One helps operate our social listening through Octolens, where it manages research vocabulary, monitoring feeds, and the decisions behind them.
-
-{/* VERIFY BEFORE PUBLICATION: Confirm the exact Octolens automation, its trigger, what it performs automatically, and what still requires Mahmoud's approval. Link the public example only if it exposes no private configuration or credentials. */}
+I also use agents for recurring work. One, for instance, helps operate our social listening through [Octolens](https://octolens.com/), where it manages research vocabulary, monitoring feeds, and the decisions behind them.
 
-The agent does not make company decisions for me. It gathers and structures information, records context, and helps perform the work. I provide direction, correct it, and make the final decisions.
+The agent does not make decisions for me. It gathers and structures information, records context, and helps perform the work. Over time, I give it feedback and refine it to fit the way I work. It gives me immense leverage.
 
-## Reliability comes from improvement
+## Reliability and continuous improvement
 
-The first version of Agenta focused on helping teams build, evaluate, and observe applications powered by language models. That foundation remains important because agents still make mistakes.
+The first version of Agenta focused on helping teams build, evaluate, and observe applications powered by LLMs. That foundation remains important because reliability is still a major challenge when building agents.
 
-For a company, reliability is not only about whether one run succeeds. It is about making an agent better at a defined job over time.
+When we started Agenta, LLMs were poor at following instructions and understanding what users meant. Much of our users' time went into prompt engineering and building complex workflows designed to compensate. Today, agents are far better at following instructions. Most workflows can now be expressed as instructions to an agent instead of being defined step by step.
 
-First, people need to state what good work looks like. Then they need to review real results and give feedback. When something goes wrong, they need to inspect the trace, understand which instruction or action caused the problem, and change the agent without losing what already worked.
+Reliability today is about explaining the job clearly. Say you are building an agent to reach out to prospects. How do you define your ideal customer profile? What style should the agent follow? What should it focus on when researching a prospect, and what should it ignore?
 
-Agenta supports this improvement loop through tracing, annotations, evaluations, and versioning. An annotation records structured feedback on a run. An evaluation checks an output against a defined expectation. Versioning connects each result to the agent configuration that produced it.
+You cannot build a reliable agent that performs a job successfully without answering these questions. To achieve that, there is no shortcut; you need to review the agent results, give feedback, and iterate.
 
-Together, these tools help teams make agents more reliable and predictable. They do not make a language model deterministic. They provide the evidence needed to improve the agent and decide which work still needs human review.
+Agenta provides the tools to improve agents continuously. Every run is logged and traced with its inputs and outputs. Every agent configuration is versioned. A user can give feedback, and the agent can inspect what happened, identify why it made a mistake, and propose configuration changes.
 
-Permissions and human approval add another layer of control. Teams can decide which actions an agent may perform, which require approval, and which remain blocked.
+Over time, you build more capable agents that become part of your company's operating DNA and differentiation. They encode your best practices, standard operating procedures, and your ways of working.
 
-{/* VERIFY BEFORE PUBLICATION: Confirm tracing, annotations, evaluations, versioning, permissions, approval behavior, and configuration restoration in the launch build. */}
+The second layer of reliability comes from permissions and human approval. We built Agenta with a detailed permission system and human-in-the-loop controls. Teams can decide which actions an agent may perform, which require approval, and which remain blocked.
 
 ## Getting started
 
-Agenta 2.0 is available through Agenta Cloud and as a self-hosted product.
-
-{/* VERIFY BEFORE PUBLICATION: Confirm availability, release name, license, tested installation path, and destination links. */}
-
-Start with one area of work. Build an agent around a real job, give it the context it needs, and improve it through use. Once it works well, share it with your team or let it handle suitable parts of the work in the background.
-
-Agenta is still early. Some integrations and execution options require additional setup. Our documentation will explain the current limits and the setup required.
+Agenta 2.0 is today available through Agenta Cloud and as an open-source, self-hosted product.
 
-[Try Agenta Cloud](https://cloud.agenta.ai), [view Agenta on GitHub](https://github.com/Agenta-AI/agenta), or follow the self-hosting guide to run it yourself.
+Start with a template. Ask an agent to customize it, then give it the context and access to the tools it needs. Improve it through use. Over time, you can automate parts of the work and build a system that gives your whole team far more leverage.
 
-We believe the people who understand the work should be able to build the agents that do it.
+Get started for free in [Agenta Cloud](https://cloud.agenta.ai), or copy the prompt in the [self-hosting guide](https://agenta.ai/docs/self-host/overview) to run it yourself in minutes.
 
-If you build an agent, tell us what work you gave it, where it helped, and where it failed. That feedback will shape what we build next.
+We believe the people who understand the work should be able to build the agents that do it. We want to help them build the next million agents.