Why This Session Instantly Hooked Me

I spent my Saturday at the AWS User Group Chennai meetup, and one session really caught my attention: a detailed look at Amazon Bedrock AgentCore and how it helps in creating real AI agents.

The speaker, Muthukumar Oman, who is the VP – Head of Engineering at Intellect Design Arena and an AWS Community Builder, explained how to take an AI model from a basic demo to a fully working AI agent in a clear and organized way.

There were other good talks that day, but this one stood out because it addressed a question many of us have been thinking about: How can we go beyond simple chatbots and actually build a dependable AI agent that works with our systems?

What Is Amazon Bedrock AgentCore?

Making Sense of AgentCore in Simple Terms

AgentCore acts as the main control center for your AI agents on AWS.

It helps you:

Deploy and operate agents securely at scale
Ensure trust and reliability when agents call tools and APIs
Use built-in tools like a code interpreter and browser
Stay framework- and model-agnostic, so you can bring your favorite stack
Test and monitor agents in a structured way

Imagine it this way: if a regular LLM is like a smart intern, AgentCore is like the IT, security, and support team that helps that intern use different apps, keeps track of their work, and makes sure everything stays secure.

Where AgentCore Fits in the AI Stack

One of the slides showed the full AI structure on AWS: applications at the top, followed by AI and agent development tools and services, then Amazon Bedrock (which includes models, features, and AgentCore), and finally the underlying infrastructure such as Amazon SageMaker and AI compute resources like Trainium, Inferentia, and GPUs.

In other words:

Infrastructure = raw compute and ML tooling
Bedrock = models and agent building blocks
AgentCore = runtime, memory, gateway, observability, and identity for agents
Applications = what your users actually interact with (like support bots, internal copilots, etc.)

Core Building Blocks of AgentCore

AgentCore Runtime – The Engine Behind the Agent

The AgentCore Runtime slide explained what happens when your agent starts working.

Key points that stood out:

Framework agnostic – you’re not locked into a specific agent framework
Model flexibility – you can plug in different models
Protocol support, extended execution time, and enhanced payload handling
Session isolation, built-in authentication, and agent-specific observability
Unified set of agent-specific capabilities

There was also a diagram showing how your agent or tool code, like a Python framework, is structured.

Packaged as a container
Pushed to ECR
Exposed via an AgentCore endpoint
Connected to a model and the Bedrock AgentCore runtime

Imagine deploying a microservice: you package your code into a container, send it out, and AgentCore connects it to models and tools.

Memory – Short-Term vs Long-Term

This part really caught my attention. The speaker divided AgentCore Memory into different parts.

Short-term memory

Immediate context
In-session knowledge accumulation

Long-term memory

User preferences
Semantic facts
Summary

In the architecture view, short-term memory stored things like chat messages and session details, while long-term memory kept semantic data, user preferences, and summaries.

Another slide showed how long-term memory functions:

Short-term memory = raw storage
Long-term memory = vector storage
A memory extraction module finds relevant information based on events and strategies, combines it, and then creates an embedded version that can be searched.

Imagine your agent is like a person:

Short-term memory is about the conversation you’re currently having.
Long-term memory is about what I’ve learned about you from previous chats and over time.

For a banking or e-commerce assistant, this could mean remembering:

Your preferred language
The kind of products you usually buy
Important facts like “this user prefers digital invoices”

Built-In Tools: Code Interpreter and Browser

Code Interpreter – Let the Agent Safely Run Code

The Code Interpreter slides explained how an agent can safely run code within a sandbox environment.

The architecture was roughly:

User sends a query to the agent
Agent invokes the LLM
LLM selects the Code Interpreter tool and creates a session
Code runs inside a sandboxed environment with a file system and shell
Telemetry flows into observability
Results are returned to the user

The Code Interpreter capabilities listed included:

Secure sandbox execution
Multi-language support
Scalable data processing
Enhanced problem-solving
Structured data formats
Ability to handle complex workflows

Imagine giving your agent a temporary, secure laptop where it can execute scripts, handle CSV files, or process data, while you keep a close watch on everything.

Browser Tool – Let the Agent Navigate the Web or Apps

Another built-in tool is the Browser Tool.

The flow looked like this:

User sends a query (e.g., “Buy shoes on Amazon”)
Agent invokes the LLM
LLM chooses the browser tool
Commands like “click left at (x, y)” are generated
A library (e.g., browser automation) translates these into real actions
The browser executes them and sends screenshots/results back to the agent

The Browser Tool capabilities mentioned:

Resource and session management
Rendering live view using AWS DCV web client
Observability and session replay

In simple terms: your agent can actually interact with a user interface, not just describe it. This is really important when dealing with older systems inside a company that might not have APIs available.

Gateway, Identity, and Observability – Production-Ready Concerns

AgentCore Gateway – One Door for All Tools

The AgentCore Gateway shows how agents connect to tools and APIs in a single, unified way.

Key ideas from the slides:

Simplified tool development and integration
Unified tools access and semantic tool selection
Security guard and serverless infrastructure
Tool types: OpenAPI specs, Lambda functions, Smithy models

Architecturally, the gateway:

Sits between agents and APIs/tools
Handles inbound authentication (via tokens)
Routes to different targets: Smithy, OpenAPI, AWS Lambda
Integrates with Identity for credentials and CloudWatch for observability

If you’ve ever connected an LLM to many APIs by hand, you know how frustrating that can be. The gateway acts like a main router and enforces rules for using tools.

AgentCore Identity – Who Is This Agent, Really?

Identity is managed through AgentCore Identity, which focuses on:

Centralized agent identity management
Credentials storage
OAuth 2.0
Identity and access controls
SDK integration
Request verification security

It’s like IAM, but better suited for agents and their tools: agents don’t just randomly call APIs; they do so with proper authentication, limited access credentials, and verified requests.

AgentCore Observability – Seeing What Your Agent Is Doing

Observability was another big emphasis:

OTEL-compatible
Runtime metrics
Memory metrics
Gateway metrics
Tools metrics
Sessions, traces, spans

In short, you don’t have to guess what’s happening. You can track how an agent handled a user request, which tools it used, how long each step took, and where things went wrong.

Strands Agents vs Bedrock Agents vs AgentCore

One slide compared Strands Agents, Bedrock Agents, and AgentCore based on several different factors.

So, if you’re:

Experimenting quickly → Strands may be fine.
Shipping something fast → Bedrock Agents are convenient.
Building enterprise-grade, highly customized agents → AgentCore gives you more control while still leaning on AWS-managed pieces.

How All of This Comes Together for Real-World Apps

From “Toy Chatbot” to Production Agent

The speaker used several diagrams showing how an app communicates with AgentCore Runtime, which then interacts with:

Models
Memory
Gateway
Identity
Observability

In real situations, this allows you to create use cases like:

A customer support agent that keeps track of past conversations and user preferences.
A financial assistant that uses browser tools to access internal systems and retrieves data safely.
A developer assistant that runs code using the code interpreter and records all actions for review.

Why This Matters for Builders Like Us

If you’re creating a startup product or working in a team within a large company, the usual challenges are similar:

“How do I handle sessions and memory in a reliable way?”
“How can I link agents to different tools without causing serious security issues?”
“How do I figure out what went wrong when something doesn’t work as expected?”

AgentCore helps solve these problems by:

Structured runtimes and memory
Gateway and identity for secure tool access
Deep observability for traces and metrics

In the end, it takes AI agents from being a makeshift side project to something that operations, security, and compliance teams can really trust and use.

Conclusion

Amazon Bedrock AgentCore showed me that creating strong AI agents isn’t just about making another chatbot. It’s more about getting the basics right, like memory, tools, security, and the ability to track what’s happening. When runtime, gateway, identity, and built-in tools all work together, they form a solid base. This helps move from quick weekend projects to real, reliable AI experiences that teams can trust and grow.

About the Author

As an AWS Community Builder*, I enjoy sharing the things I’ve learned through my own experiences and events, and I like to help others on their path. If you found this helpful or have any questions, don’t hesitate to get in touch!* 🚀

🔗 Connect with me on LinkedIn

References

Event: AWS User Group Chennai Meetup

Topic: Building Practical AI Agents with Amazon Bedrock AgentCore

Date: September 27, 2025

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