bedrock-agentcore

Deploy and manage AI agents with Amazon Bedrock AgentCore services.

37|20|Updated Jun 8, 2025

Search Tags:#deployment #scalability #ai agents #observability #infrastructure #bedrock #agentcore

Ready to run

npx skills add https://github.com/aws-samples/sample-ai-possibilities --skill bedrock-agentcore

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bedrock-agentcore
Source: https://github.com/aws-samples/sample-ai-possibilities/tree/main/agentic-football-coach/kiro-football-coach/.kiro/skills/bedrock-agentcore
Command: npx skills add https://github.com/aws-samples/sample-ai-possibilities --skill bedrock-agentcore

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the deployment, management, and operation of AI agents at scale by providing fully-managed services that handle infrastructure complexities.

Core Features & Use Cases

Runtime: Securely deploy and run agents with automatic scaling and session isolation.
Memory: Enable context-aware agents with short-term and long-term memory capabilities.
Gateway: Securely manage and discover tools for agents, transforming APIs and services into agent-compatible tools.
Identity: Manage secure agent identities and access permissions.
Observability: Trace, debug, and monitor agent performance in production.
Code Interpreter: Securely execute Python code in isolated sandbox environments.
Browser: Provide cloud-based browser runtime for AI agents.
Use Case: Deploy a football agent that can analyze game states, access player statistics, and make real-time tactical decisions during a match.

Quick Start

Deploy your agent using the AgentCore CLI by running the command agentcore deploy.

Frequently Asked Questions about bedrock-agentcore

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I deploy and manage AI agents at scale without managing infrastructure?▼

To deploy AI agents at scale, you can use managed services like Amazon Bedrock AgentCore that handle infrastructure complexities, providing automatic scaling, session isolation, and secure agent runtime environments without manual server management.

How does AgentCore handle observability and tracing for AI agents in production?▼

AgentCore observability allows you to trace, debug, and monitor AI agent performance in production environments. It provides built-in tools to track agent execution paths, identify bottlenecks, and ensure reliable operation of deployed agents.

Can I use Amazon Bedrock AgentCore with existing agent frameworks and foundation models?▼

Yes, Amazon Bedrock AgentCore integrates with various agent frameworks and foundation models. This allows you to bring your existing AI agent logic and leverage managed runtime, memory, and gateway services without being locked into a single framework.

What's the best way to securely execute Python code for AI agents in isolated environments?▼

The best way to securely execute Python code is using isolated sandbox environments. The Code Interpreter feature provides this capability, ensuring that dynamically generated or external code runs safely without risking the host infrastructure.

How do AI agents maintain context during long-running sessions or across multiple interactions?▼

AI agents maintain context using memory capabilities that support both short-term and long-term retention. This enables context-aware interactions where agents can reference previous session data and historical context for more accurate responses.

How can I securely manage and discover external tools for my AI agents?▼

You can securely manage and discover tools using a tool gateway. This gateway transforms external APIs and services into agent-compatible tools, allowing centralized management of tool access, permissions, and identity for secure agent operations.