• Amazon Bedrock AgentCore Runtime is the execution engine within the Bedrock AgentCore platform, providing a low-latency, serverless environment to run AI agents. It handles the complex infrastructure of scaling, security, and session management, allowing you to focus on developing agent logic. The service supports everything from rapid prototyping to production-scale deployments.

Amazon Bedrock AgentCore Runtime Features

• Framework Agnostic
  • Runtime lets you transform any local agent code to cloud-native deployments with a few lines of code. It works seamlessly with popular frameworks like LangGraph, Strands, and CrewAI, as well as custom agents that don’t use a specific framework.
• Model and Protocol Flexibility
  • Agents in the Runtime can use any Large Language Model, including models from Amazon Bedrock, Anthropic Claude, Google Gemini, and OpenAI. For communication, it supports Model Context Protocol (MCP), Agent to Agent (A2A), and standard HTTP.
• Security and Isolation
  • Each user session runs in a dedicated microVM with isolated CPU, memory, and filesystem resources. This prevents cross-session data contamination. Post-session, the entire microVM is terminated and memory is sanitized.
• Enterprise-Grade Capabilities

  • Extended Execution: Supports long-running workloads up to 8 hours for complex, multi-agent tasks.

  • Built-in Authentication: Integrates with corporate identity providers like Okta, Microsoft Entra ID, or Amazon Cognito via AgentCore Identity.

  • Enhanced Observability: Provides specialized built-in tracing for agent reasoning steps, tool invocations, and model interactions.

  • Large Payloads: Can process payloads up to 100MB, enabling multi-modal processing (text, images, audio, video).

Amazon Bedrock AgentCore Runtime Use Cases

• Rapid Agent Prototyping and Iteration
  • Use the direct code deployment method to quickly test and refine agent logic without managing containers, accelerating development cycles from idea to working prototype.
• Production Multi-Agent Workflows
  • Deploy collaborative agents that work together on complex, long-running problems such as data analysis, content generation pipelines, or automated customer service triage.
• Secure and Auditable AI Operations
  • Run agents that handle sensitive data or require strict compliance, leveraging session isolation, detailed audit trails, and integration with enterprise authentication systems.

Amazon Bedrock AgentCore Runtime Implementation 

Deployment Methods
Runtime offers two primary paths to deploy your agent code:

Getting Started with Direct Code Deployment

1. Initialize: Set up a Python project (3.10-3.13) using the AgentCore starter toolkit.

2. Create Agent: Write your agent logic in an agent.py file using a supported framework.

3. Deploy: Use the toolkit command agentcore deploy. When prompted, select “Code Zip” as the deployment type.

4. Configure: Specify an S3 bucket for the toolkit to upload the deployment package.

Infrastructure as Code (CloudFormation)
You can define and provision a Runtime endpoint using AWS CloudFormation with the AWS::BedrockAgentCore::Runtime resource. Key configurable properties include the AgentRuntimeArtifact, AgentRuntimeName, NetworkConfiguration, RoleArn, and ProtocolConfiguration.

Amazon Bedrock AgentCore Runtime Security

• Session and Data Isolation
  • The microVM-per-session architecture provides hardware-enforced isolation, ensuring agent state and processed data never leak between different users or sessions.
• Integrated Identity and Access
  • Leverages Amazon Bedrock AgentCore Identity to assign distinct identities to agents and broker secure authentication flows to third-party services (e.g., Slack, GitHub) using OAuth or API keys.
• Network Security
  • Configure runtime endpoints within your Amazon VPC using NetworkConfiguration properties to control inbound and outbound traffic and utilize private connectivity.

Amazon Bedrock AgentCore Runtime Best Practices

• Choosing a Deployment Method

  • Start with Direct Code Deployment for prototyping Python agents to maximize developer speed.

  • Switch to Container-Based Deployment for production if your package exceeds 250MB, requires non-Python languages, or needs custom system dependencies.

• Optimizing for Performance and Cost

  • Structure long-running, asynchronous tasks to leverage the 8-hour execution limit.

  • Benefit from the consumption-based pricing model where you are typically not charged for I/O wait time (e.g., waiting for an LLM response).

• Development Lifecycle

  • Use the detailed agent-specific tracing for debugging complex reasoning and tool-use patterns.

  • Manage configuration and environment variables through the Runtime’s API or CloudFormation for consistency across deployments.

Amazon Bedrock AgentCore Runtime Pricing

• Consumption-Based Model
  • AgentCore Runtime uses a consumption-based pricing model. You are charged only for the resources (like compute and memory) your agents actually use during execution.
• Key Cost Differentiators

  • Unlike allocation-based models, you are not charged during I/O wait periods (e.g., while the agent waits for an LLM response), though your session state is maintained.

  • For Direct Code Deployment, you incur standard Amazon S3 storage costs for the deployment packages stored in the service-managed account.

  • For Container-Based Deployment, you incur standard Amazon ECR storage costs for images stored in your account.

Amazon Bedrock AgentCore Runtime Cheat Sheet References:

https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agents-tools-runtime.html
https://aws.amazon.com/blogs/machine-learning/iterate-faster-with-amazon-bedrock-agentcore-runtime-direct-code-deployment/
https://docs.aws.amazon.com/AWSCloudFormation/latest/TemplateReference/aws-resource-bedrockagentcore-runtime.html