diff --git a/content/develop/ai/_index.md b/content/develop/ai/_index.md
index c6e709c202..9e8569474b 100644
--- a/content/develop/ai/_index.md
+++ b/content/develop/ai/_index.md
@@ -6,7 +6,7 @@ categories:
 - operate
 - rs
 - rc
-description: An overview of Redis for AI and search documentation
+description: An overview of Redis for AI and search documentation, including vector search, AI agents, and the Context Engine (Redis Iris) managed services.
 linkTitle: Redis for AI and search
 weight: 40
 hideListLinks: true
@@ -23,24 +23,34 @@ 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
+## AI agents
 
-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 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.
+- [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 [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.
+1. [**Access structured business data**]({{< relref "/develop/ai/context-engine/context-retriever" >}}): Use Context Retriever to define your business data as governed tools that any agent can query reliably.
+1. [**Sync live data to Redis**]({{< relref "/develop/ai/context-engine/data-integration" >}}): Use Data Integration to keep your Redis Cloud database in sync with your primary relational database using Change Data Capture.
 
 #### Learn how to index and query vector embeddings
 * [redis-py (Python)]({{< relref "/develop/clients/redis-py/vecsearch" >}})
@@ -51,13 +61,13 @@ 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 +96,34 @@ 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 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 Context Retriever]({{< relref "/develop/ai/context-engine/context-retriever" >}}) — Expose your business data as governed tools that agents can reliably query.
+* [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
+* [Build a LangGraph travel agent with Redis Agent Memory](https://redis.io/tutorials/redis-agent-memory-with-langgraph/) - Build a LangGraph agent with short-term session memory and long-term persistent memory using Redis Agent Memory
+* [Build a real-time AI agent with Redis Iris](https://redis.io/tutorials/redis-iris-call-agent/) - Combine Redis Agent Memory and Context Retriever to build a wealth advisor agent with persistent memory and structured data access
+* [Build a car dealership agent with Google ADK and Redis Agent Memory](https://redis.io/tutorials/build-a-car-dealership-agent-with-google-adk-and-redis-agent-memory/) - Build a persistent AI agent using Google ADK and Redis Agent Memory Server with working and long-term memory
+* [Build Google ADK agents with persistent, real-time memory on Redis](https://redis.io/en/blog/build-google-adk-agents-with-persistent-real-time-memory-on-redis/) - Use the `adk-redis` package to integrate Google ADK with Redis for persistent memory, sessions, and semantic caching in production agents
+
+#### Context Engine
+* [Semantic caching with Redis LangCache](https://redis.io/tutorials/semantic-caching-with-redis-langcache/) - Build a FastAPI app with semantic caching using LangCache to reduce LLM costs and improve response times
+
+#### 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
@@ -102,6 +133,10 @@ Need a deeper-dive through different use cases and topics?
 * [Redis product search](https://github.com/redis-developer/redis-product-search) - Build a real-time product search engine using features like full-text search, vector similarity, and real-time data updates
 * [ArXiv Search](https://github.com/redis-developer/redis-arxiv-search) - Full stack implementation of Redis with React FE
 
+#### Vector sets
+* [Getting started with vector sets](https://redis.io/tutorials/howtos/vector-sets-basics/) - Learn the fundamentals of Redis vector sets for similarity search using the `VADD` and `VSIM` commands
+* [Face similarity search with Redis vector sets](https://redis.io/tutorials/face-similarity-search-with-redis-vector-sets/) - Build a celebrity lookalike app using Redis vector sets and a Vision Transformer model for face embedding and similarity search
+
 ## Ecosystem integrations
 
 Explore our comprehensive [ecosystem integrations page]({{< relref "/develop/ai/ecosystem-integrations" >}}) to discover how Redis works with popular AI frameworks, platforms, and tools including:
@@ -127,7 +162,24 @@ 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.
+
+#### Agents and architecture
+* [AI Agent vs Chatbot: Key Differences Explained](https://redis.io/en/blog/ai-agent-vs-chatbot/) — Understand the architectural differences between chatbots and agents and when to use each based on task complexity, cost, and latency.
+* [Agentic AI Architecture: 5 Patterns Explained](https://redis.io/en/blog/agentic-ai-architecture-examples/) — Learn five production agentic patterns and the data layer requirements needed to support them.
+* [AI Agents vs Workflows: When to Use Each](https://redis.io/en/blog/agents-vs-workflows/) — Understand the distinction between deterministic workflows and autonomous agents and how to combine them in production.
+* [How agents work]({{< relref "/develop/ai/agent-builder/agent-concepts" >}}) — Agent memory patterns, data structure selection, and production deployment considerations.
+
+#### Memory and context
+* [Context Engineering for AI: What It Is & How to Build It](https://redis.io/en/blog/context-engineering-ai/) — Learn the discipline of designing what an LLM receives at inference time, including the four core operations and how Redis provides the infrastructure.
+* [Long-Term Memory Architectures for AI Agents](https://redis.io/en/blog/long-term-memory-architectures-ai-agents/) — Design persistent memory systems that retain information across sessions, with guidance on memory types and design tradeoffs.
+* [Context Pruning: Cut LLM Tokens Without Losing Quality](https://redis.io/en/blog/context-pruning-llm-tokens/) — Selectively remove low-value tokens from LLM input to reduce costs and improve quality, with benchmarks and failure modes.
+
+#### Performance
+* [What is semantic caching](https://redis.io/blog/what-is-semantic-caching) — When and how to apply semantic caching in your AI applications.
+* [Streaming LLM Responses: Make Your AI App Feel Fast](https://redis.io/en/blog/streaming-llm-responses/) — Deliver tokens incrementally via Server-Sent Events and combine streaming with caching and context optimization in production.
+
+#### RAG
 * [Get better RAG responses with Ragas](https://redis.io/blog/get-better-rag-responses-with-ragas/)
 
 ## Continue learning with Redis University
diff --git a/content/develop/ai/ai-videos.md b/content/develop/ai/ai-videos.md
index 8b65f7601d..5b6a224423 100644
--- a/content/develop/ai/ai-videos.md
+++ b/content/develop/ai/ai-videos.md
@@ -22,6 +22,17 @@ Explore our collection of video tutorials and demonstrations showcasing how Redi
 | The ins and outs of AI agents: understand their role in breaking down tasks into manageable components for better performance. Learn how to architect AI agents using AWS, LlamaIndex, and Redis. | This series of videos dives into the integration between LangChain and Redis to power AI applications that need runtime speed, scalability, and intelligent data management. | This video shows which resources you can use to learn AI with Redis and build powerful AI applications. |
 | [**What Is RAG? Retrieval-Augmented Generation Explained Simply**](https://www.youtube.com/watch?v=xPMQ2cVbUTI) | [**Chunking Strategies Explained**](https://www.youtube.com/watch?v=ZTOtxiWb2bE) | [**What is an embedding model?**](https://www.youtube.com/watch?v=0U1S0WSsPuE) |
 | Retrieval-Augmented Generation (RAG) is one of the most powerful architectural patterns in GenAI today—combining the strengths of large language models (LLMs) with real-time, external context from your own data. In this session, learn why it matters and how each component—from query rewriting to dense retrieval to semantic chunking—works behind the scenes to power more accurate, grounded, and up-to-date responses. | Are you interested in building LLM applications that actually work? Your chunking strategy makes all the difference. In this video, get a  break down of the science of text chunking so your embeddings can start answering the right questions to your users. | Everyone’s talking about embedding models lately—but what do they actually do, and why does it matter? This video breaks it down in simple terms and shows how embeddings power search, recommendations, and AI features behind the scenes. |
+| [**Exact vs Approximate (HNSW) Nearest Neighbors in Vector Databases**](https://www.youtube.com/watch?v=9NvO-VdjY80) | [**What is Hybrid Search?**](https://www.youtube.com/watch?v=ZcZGwS1Pgq0) | [**Vector Database Search: HNSW Algorithm Explained**](https://www.youtube.com/watch?v=cZyTZ-EMskI) |
+| Explore the trade-offs between exact and approximate nearest neighbor search using the HNSW algorithm, with a focus on precision vs. performance in vector databases. | Learn how hybrid search combines vector (semantic) search with traditional keyword search to improve retrieval quality in AI applications. | A deep-dive into the HNSW (Hierarchical Navigable Small World) algorithm and how it powers efficient similarity search in vector databases. |
+
+### AI Agents and Context Engine
+
+| | | |
+|---|---|---|
+| [**Redis Agent Memory Basics**](https://www.youtube.com/watch?v=OWsCEs8_Wt4) | [**Build with Rowan: The real-time context engine**](https://www.youtube.com/watch?v=7JOnwT1v2Do) | [**Redis LangCache Setup**](https://www.youtube.com/watch?v=UOGhMZlZLko) |
+| Learn the fundamentals of agent memory using Redis as a memory layer, covering how to give AI agents the ability to store and retrieve information. | A hands-on build session demonstrating how to construct applications that use the Redis Context Engine for real-time agent context. | Walk through how to configure Redis LangCache for semantic caching of LLM responses to reduce latency and cost. |
+| [**Getting Started with the MCP Server for Redis Cloud using Docker**](https://www.youtube.com/watch?v=ziPu9TZD8hg) | | |
+| Learn how to set up and run the MCP Server for Redis Cloud inside a Docker container to connect AI agents to your Redis data. | | |
 
 ### Additional Resources