Update Redis for AI index with agent builder and context engine content

mich-elle-luna · claude · mich-elle-luna · commit 8e42d7475f1d · 2026-05-19T14:26:07.000-07:00
Add AI agents section, context engine services overview, how-to items, quickstarts, and best practices links for agent builder, how agents work, LangCache, Agent Memory, Context Retriever, and Data Integration.

Co-Authored-By: Claude Sonnet 4.6 &lt;noreply@anthropic.com&gt;
diff --git a/content/develop/ai/_index.md b/content/develop/ai/_index.md
@@ -23,24 +23,32 @@ Redis stores and indexes vector embeddings that semantically represent unstructu
 
 Use [Redis Feature Form]({{< relref "/develop/ai/featureform/" >}}) to define, manage, and serve machine learning features on top of your existing data systems. The Feature Form docs cover the Python SDK workflow from provider registration through feature serving.
 
-#### Overview
-
-This page is organized into a few sections depending on what you're trying to do:
-* **How to's** - The comprehensive reference section for every feature, API, and setting. It's your source for detailed, technical information to support any level of development.
-* **Concepts** - Explanations of foundational ideas and core principles to help you understand the reason behind the product's features and design.
-* **Quickstarts** - Short, focused guides to get you started with key features or workflows in minutes.
-* **Tutorials** - In-depth walkthroughs that dive deeper into specific use cases or processes. These step-by-step guides help you master essential tasks and workflows.
-* **Integrations** - Guides and resources to help you connect and use the product with popular tools, frameworks, or platforms.
-* **Video tutorials** - Watch our AI video collection featuring practical tutorials and demonstrations.
-* **Benchmarks** - Performance comparisons and metrics to demonstrate how the product performs under various scenarios. This helps you understand its efficiency and capabilities.
-* **Best practices** - Recommendations and guidelines for maximizing effectiveness and avoiding common pitfalls. This section equips you to use the product effectively and efficiently.
+## AI agents
+
+AI agents are autonomous systems that combine LLMs with memory, tools, and planning to accomplish complex, multi-step tasks. Redis powers the core capabilities agents need: fast vector search, persistent memory, real-time data streaming, and structured access to business data.
+
+- [AI agent builder]({{< relref "/develop/ai/agent-builder" >}}) — Use the interactive code generator to create a working agent in your preferred language with your choice of LLM.
+- [How agents work]({{< relref "/develop/ai/agent-builder/agent-concepts" >}}) — Learn the agent processing cycle, memory architecture, and why Redis is the foundation for production agents.
+- [Redis Context Engine]({{< relref "/develop/ai/context-engine" >}}) — The managed service suite that gives agents what they need: semantic caching, persistent memory, structured data access, and live data integration.
+
+### Context Engine services
+
+The [Redis Context Engine]({{< relref "/develop/ai/context-engine" >}}) (Redis Iris) includes four fully-managed services available on Redis Cloud:
+
+- [LangCache]({{< relref "/develop/ai/context-engine/langcache" >}}) — Semantic caching that reduces LLM API costs and improves response times by reusing cached responses for similar queries.
+- [Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) — Two-tier persistent memory (session and long-term) for agents, available as a REST API and Python SDK.
+- [Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}}) — Turns your business data into structured, governed tools that agents can reliably use, defined once and reused across all agents.
+- [Data Integration]({{< relref "/develop/ai/context-engine/data-integration" >}}) — Keeps your Redis Cloud database in sync with relational databases in near real time using Change Data Capture.
 
 ## How to's
 
 1. [**Create a vector index**]({{< relref "develop/ai/search-and-query/vectors#create-a-vector-index" >}}): Redis maintains a secondary index over your data with a defined schema (including vector fields and metadata). Redis supports [`FLAT`]({{< relref "develop/ai/search-and-query/vectors#flat-index" >}}) and [`HNSW`]({{< relref "develop/ai/search-and-query/vectors#hnsw-index" >}}) vector index types.
 1. [**Store and update vectors**]({{< relref "develop/ai/search-and-query/vectors#store-and-update-vectors" >}}): Redis stores vectors and metadata in hashes or JSON objects.
 1. [**Search with vectors**]({{< relref "develop/ai/search-and-query/vectors#search-with-vectors" >}}): Redis supports several advanced querying strategies with vector fields including k-nearest neighbor ([KNN]({{< relref "develop/ai/search-and-query/vectors#knn-vector-search" >}})), [vector range queries]({{< relref "develop/ai/search-and-query/vectors#vector-range-queries" >}}), and [metadata filters]({{< relref "develop/ai/search-and-query/vectors#filters" >}}).
-1. [**Configure vector queries at runtime**]({{< relref "develop/ai/search-and-query/vectors#runtime-query-parameters" >}}). Select the best filter mode to optimize query execution.
+1. [**Configure vector queries at runtime**]({{< relref "develop/ai/search-and-query/vectors#runtime-query-parameters" >}}): Select the best filter mode to optimize query execution.
+1. [**Build an AI agent**]({{< relref "/develop/ai/agent-builder" >}}): Use the interactive agent builder to generate complete working code for conversational assistants and recommendation engines.
+1. [**Add semantic caching**]({{< relref "/develop/ai/context-engine/langcache" >}}): Reduce LLM API calls by caching and reusing responses for semantically similar queries.
+1. [**Add agent memory**]({{< relref "/develop/ai/context-engine/agent-memory" >}}): Give your agent persistent session and long-term memory using the Agent Memory REST API.
 
 #### Learn how to index and query vector embeddings
 * [redis-py (Python)]({{< relref "/develop/clients/redis-py/vecsearch" >}})
@@ -51,13 +59,13 @@ This page is organized into a few sections depending on what you're trying to do
 
 ## Concepts
 
-Learn to perform vector search and use gateways and semantic caching in your AI/ML projects.
+Learn to perform vector search, build AI agents, and use semantic caching and memory in your AI/ML projects.
 
 <div class="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-5 gap-4 my-8">
   {{< image-card image="images/ai-search.svg" alt="AI Redis icon" title="Vector search guide" url="/develop/ai/search-and-query/query/vector-search" >}}
+  {{< image-card image="images/ai-brain.svg" alt="AI agent icon" title="How AI agents work with Redis" url="/develop/ai/agent-builder/agent-concepts" >}}
   {{< image-card image="images/ai-LLM-memory.svg" alt="LLM memory icon" title="Store memory for LLMs" url="https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/" >}}
   {{< image-card image="images/ai-brain-2.svg" alt="AI Redis icon" title="Semantic caching for faster, smarter LLM apps" url="https://redis.io/blog/what-is-semantic-caching" >}}
-  {{< image-card image="images/ai-semantic-routing.svg" alt="Semantic routing icon" title="Semantic routing chooses the best tool" url="https://redis.io/blog/level-up-rag-apps-with-redis-vector-library/" >}}
   {{< image-card image="images/ai-model.svg" alt="AI Redis icon" title="Deploy an enhanced gateway with Redis" url="https://redis.io/blog/ai-gateways-what-are-they-how-can-you-deploy-an-enhanced-gateway-with-redis/" >}}
 </div>
 
@@ -86,13 +94,26 @@ Additional resources:
 
 #### Agents
 AI agents can act autonomously to plan and execute tasks for the user.
-* [Redis Notebooks for LangGraph](https://github.com/redis-developer/langgraph-redis/tree/main/examples)
+
+* [Build your first AI agent]({{< relref "/develop/ai/agent-builder" >}}) — Use the interactive agent builder to generate production-ready agent code.
+* [How agents work]({{< relref "/develop/ai/agent-builder/agent-concepts" >}}) — Learn the agent processing cycle, memory architecture, and Redis data structures for agents.
+* [Redis Notebooks for LangGraph](https://github.com/redis-developer/langgraph-redis/tree/main/examples) — End-to-end agent examples using LangGraph and Redis.
+
+#### Context Engine
+The Redis Context Engine provides managed services for agent memory and data access.
+
+* [Get started with LangCache]({{< relref "/develop/ai/context-engine/langcache" >}}) — Add semantic caching to reduce LLM costs in minutes.
+* [Get started with Agent Memory]({{< relref "/develop/ai/context-engine/agent-memory" >}}) — Add persistent two-tier memory to any agent using the REST API.
+* [Get started with Data Integration]({{< relref "/develop/ai/context-engine/data-integration" >}}) — Keep Redis in sync with your primary database so agents always have fresh data.
 
 ## Tutorials
 Need a deeper-dive through different use cases and topics?
 
-#### RAG
+#### Agents
 * [Agentic RAG](https://github.com/redis-developer/agentic-rag) - A tutorial focused on agentic RAG with LlamaIndex and Amazon Bedrock
+* [Redis Notebooks for LangGraph](https://github.com/redis-developer/langgraph-redis/tree/main/examples) - Working with LangGraph agents and Redis memory
+
+#### RAG
 * [RAG on Vertex AI](https://github.com/redis-developer/gcp-redis-llm-stack/tree/main) - A RAG tutorial featuring Redis with Vertex AI
 * [RAG workbench](https://github.com/redis-developer/redis-rag-workbench) - A development playground for exploring RAG techniques with Redis
 * [ArXiv Chat](https://github.com/redis-developer/ArxivChatGuru) - Streamlit demo of RAG over ArXiv documents with Redis & OpenAI
@@ -127,8 +148,10 @@ See how we stack up against the competition.
 * [1 billion vectors](https://redis.io/blog/redis-8-0-m02-the-fastest-redis-ever/)
 
 ## Best practices
-See how leaders in the industry are building their RAG apps.
+See how leaders in the industry are building their AI apps.
 * [Get better RAG responses with Ragas](https://redis.io/blog/get-better-rag-responses-with-ragas/)
+* [What is semantic caching](https://redis.io/blog/what-is-semantic-caching) — When and how to apply semantic caching in your AI applications.
+* [How agents work]({{< relref "/develop/ai/agent-builder/agent-concepts" >}}) — Agent memory patterns, data structure selection, and production deployment considerations.
 
 ## Continue learning with Redis University
 
