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Build an AI Support Agent on Amazon Bedrock AgentCore (2026 Guide)

A practical walkthrough of Amazon Bedrock AgentCore — Runtime, Memory, Gateway and Knowledge Base — and how they fit together to build a production AI support agent.

By Himanshu Rana·June 10, 2026·3 min read

If you've tried to ship an AI agent past the demo stage, you already know the hard part isn't the model — it's everything around it: memory across sessions, grounding answers in your docs, calling real tools, and running it securely without babysitting servers. Amazon Bedrock AgentCore is AWS's answer to exactly that.

This post breaks down what AgentCore actually gives you and how the pieces fit, so you can decide whether it belongs in your stack.

What Bedrock AgentCore is (and isn't)

Classic Bedrock Agents are fully managed but rigid — you configure them and hope your use case fits the box. AgentCore is modular infrastructure you assemble from independent components. You bring your own agent code (any Python framework) and wire in only what you need.

The four building blocks:

  • Runtime — runs your agent code in a managed, secure sandbox with execution windows up to 8 hours. No servers, no scaling config.
  • Memory — short-term (within a session) and long-term (across sessions) memory. It automatically extracts durable facts from conversations, so your agent remembers the customer next time.
  • Gateway — connects the agent to tools: Lambda functions, MCP servers, and external APIs.
  • Knowledge Base — semantic (meaning-based) search over your documents, i.e. RAG without the plumbing.
ℹ️

AgentCore went generally available in October 2025 and works with any Python framework. The simplest on-ramp is the AWS Strands SDK.

A reference architecture

A typical support agent looks like this end to end:

Customer → API Gateway → Lambda → AgentCore Runtime
                                      ├── Memory (who asked what, across sessions)
                                      ├── Knowledge Base (RAG over your S3 docs)
                                      └── Gateway → Lambda tools (create ticket, escalate)

The agent reads your docs to answer accurately, remembers the customer between visits, and when it hits something genuinely novel it creates a ticket or escalates to a human.

Why this matters for ranking your own product docs

The same RAG pattern that powers a support agent is what makes your documentation actually usable. If you're investing in docs, an agent on top of them turns a static knowledge base into an answer engine — and dramatically cuts repetitive tickets.

Where to go next

Reading about agents only gets you so far. The fastest way to internalize AgentCore is to build the whole thing — Knowledge Base, Memory, tool-calling Lambdas and an API — from an identical working baseline.

That's exactly what the hands-on workshop below walks you through, step by step.

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