CUSTOM CLOUD AI AGENT USING AWS AGENTCORE
OVERVIEW
To support use cases requiring greater flexibility and engineering control than a low-code platform can provide, we built a custom AI agent using AWS AgentCore, an Amazon Bedrock service for creating, deploying, and monitoring full-sentence conversational AI assistants. AgentCore enabled a modular, extensible architecture with developer-level visibility into security, authentication, memory, and tool integration. The result was a scalable foundation for future, cloud-ready agent workflows.
THE CHALLENGE
Low-code tools simplify agent creation, but they impose guardrails: minimal debugging capability, limited control over behavior, and constraints on integrations. For more sophisticated applications—custom authentication, cloud deployment, cross-session memory, tool orchestration, and standardized workflows—the team needed a framework that exposed the underlying agent architecture.
This introduced engineering challenges including:
• Setting up secure authentication so the agent could be deployed into its designated AWS environment
• Debugging deployment issues tied to cloud infrastructure
• Managing access control and user identity
• Orchestrating modular components instead of relying on a single managed environment
OUR SOLUTION
We implemented the chatbot using AWS AgentCore, a modular Bedrock service designed to support AI agents end-to-end. AgentCore is model-agnostic, scalable, and compatible with most open-source agent frameworks (e.g., Strands, LangGraph, CrewAI, LlamaIndex). Key concepts incorporated into the implementation included:
1. Core AgentCore Modules
• Runtime: Serverless, secure deployment environment that manages the cloud infrastructure and security model.
• Memory: Persistent, cross-session memory supporting long-term context retention.
• Gateway: Converts APIs and AWS Lambda functions into agent-compatible tools and integrates with external MCP servers.
• Identity: Controls access policies and security permissions for AWS resources and third-party tools.
• Observability: Provides monitoring capabilities for debugging, performance, and behavior tracking.
• Browser: Allows the agent to interact with websites inside a controlled, cloud-based browser environment.
• Code Interpreter: Lets the agent write and execute Python code in a sandbox—useful for complex workflows, KPI analysis, and data transformations.
These modules allowed us to build an agent with fine-grained control over infrastructure, authentication, memory, and tool execution.
2. MCP (Model Context Protocol) Integration
AgentCore leverages MCP, an AWS-created standard for connecting AI agents to external services, trusted data sources, and developer-defined tools. MCP enables:
• A consistent way for the agent to call APIs and tools
• Structured, reliable task execution
• Developer-side control over how an agent should interact with the world
• Clear definition of what information or tools are trustworthy
MCP acts like a system-level “playbook,” ensuring the agent follows deterministic workflows rather than generating arbitrary responses.
3. Development and Setup
• Built an MCP server using FastMCP, a lightweight Python framework with tool-discovery functions.
• Deployed the MCP server to AgentCore Runtime with authentication configured via Amazon Cognito.
• Assigned an Agent ARN (unique identifier) and generated bearer tokens to securely access the MCP server.
• Created an Agent Client using Bedrock and the Strands framework, enabling natural-language interaction with in-session memory and modular toolsets.
KEY FEATURES
• Fully modular agent architecture assembled from AgentCore modules.
• Custom authentication and security configuration for cloud deployment.
• MCP-driven tool and API integration for structured, repeatable tasks.
• Compatibility with multiple open-source agent frameworks.
• Ability to support complex analytical workflows using Code Interpreter and browser-based interactions.
GLOBAL IMPACT/RESULTS
• A far more flexible architecture than low-code tools, enabling deeper customization and advanced capabilities.
• Scalable deployment through serverless infrastructure.
• Improved observability, debugging, and developer control over agent behavior.
• Foundation for long-term AI agent capabilities such as generating multi-year KPI tables or orchestrating multi-step analytical tasks.
• A clear blueprint for building enterprise-grade AI assistants capable of evolving with business needs.
TECHNOLOGIES & SERVICES
AWS AgentCore (Bedrock) — modular agent development and cloud deployment.
FastMCP — Python framework for building MCP servers.
Cognito — authentication and identity management.
Strands — agent framework used for runtime interactions.
MCP — tool and service integration protocol.
CONCLUSION
The AgentCore project demonstrated the power and flexibility of modular, developer-controlled AI agents. While higher in engineering effort, AgentCore enables capabilities far beyond those possible in low-code environments—unlocking custom integrations, secure cloud deployment, standardized workflows, and scalable infrastructure. This positions the organization to build enterprise-grade AI assistants tailored to complex analytical, operational, and automation use cases.