Unleashing the Power of AI Agents: Amazon Bedrock AgentCore's New Features
AI agents are the future, but how do we ensure they're trustworthy? Today, we're diving into the exciting world of Amazon Bedrock AgentCore and its latest innovations, designed to empower organizations to deploy AI agents with confidence.
But here's where it gets controversial: while AI agents offer immense potential, their autonomy can be a double-edged sword. How do we ensure they act within acceptable boundaries and maintain quality standards? That's where AgentCore's new capabilities come into play.
Let's explore how these features are revolutionizing the way we interact with AI agents and discover the endless possibilities they unlock.
Real-World Success Stories:
- PGA TOUR: Revolutionizing Sports Content with AgentCore - PGA TOUR, a sports innovation leader, built a multi-agent system on AgentCore, achieving a 1,000% increase in content writing speed and a 95% cost reduction. Talk about a game-changer!
- Workday: Financial Planning Made Intuitive - Workday's Planning Agent, powered by AgentCore, enables users to analyze financial data through natural language queries, saving approximately 100 hours per month on routine planning analysis.
- Grupo Elfa: Transforming Retail Operations - Grupo Elfa, a Brazilian retailer, uses AgentCore Observability for complete audit traceability, achieving 100% agent decision traceability and a 50% reduction in problem resolution time.
The Challenge of Scaling Agent Deployments:
As organizations scale their AI agent deployments, they face the challenge of implementing robust quality checks and boundaries. AgentCore's new features aim to address this, providing a solution that balances agent autonomy with necessary controls.
Introducing AgentCore's New Capabilities:
- Policy in AgentCore (Preview): Define clear boundaries for agent actions using policies with fine-grained permissions. Intercept tool calls before they run, ensuring agents operate within defined parameters.
- AgentCore Evaluations (Preview): Continuously monitor agent quality based on real-world behavior. Built-in evaluators assess dimensions like correctness and helpfulness, while custom evaluators cater to specific business requirements.
Expanding Agent Capabilities:
- Episodic Functionality in AgentCore Memory: Help agents learn from experiences and adapt solutions across similar situations, improving consistency and performance.
- Bidirectional Streaming in AgentCore Runtime: Deploy voice agents that can listen and adapt while users speak, creating natural, dynamic conversations.
Policy in AgentCore: Precise Agent Control:
Policy gives you control over agent actions, treating them as autonomous actors. Integrating with AgentCore Gateway, policies intercept tool calls, ensuring operational speed and responsiveness.
Create policies using natural language or Cedar, an open-source policy language. This approach simplifies policy creation, making it accessible to development, security, and compliance teams.
Policies are independent of agent-building methods and models. Define which tools and data agents can access, the actions they can perform, and the conditions under which they operate.
With policies in place, organizations can deploy agents with confidence, knowing they'll stay within defined boundaries and compliance requirements.
Using Policy in AgentCore:
Start by creating a policy engine in the AgentCore console and associate it with AgentCore gateways. Choose to enforce policy results or emit logs for testing and validation.
Define policies with granular control over tool access. Use natural language descriptions or edit Cedar code directly.
Natural language-based policy authoring provides an accessible way to create fine-grained policies. The system interprets your intent, generates candidate policies, and validates them against tool schema, ensuring safety conditions are met.
This feature understands tool structure, generating syntactically correct and semantically aligned policies. It's also available as an MCP server, allowing policy authoring and validation in your preferred AI-assisted coding environment.
AgentCore Evaluations: Continuous Quality Intelligence:
AgentCore Evaluations is a fully managed service that continuously monitors and analyzes agent performance. Built-in evaluators assess common quality dimensions, while custom model-based scoring systems cater to specific business needs.
All results are visualized in Amazon CloudWatch alongside AgentCore Observability insights, providing a unified monitoring platform. Set up alerts and alarms to proactively monitor agent quality.
Use AgentCore Evaluations during testing and in production for continuous improvement. When quality metrics drop below defined thresholds, immediate alerts are triggered, helping detect and address issues promptly.
Using AgentCore Evaluations:
Create online evaluations in the AgentCore console, using AgentCore agent endpoints or CloudWatch log groups. Select evaluators, including custom ones defined from templates or built from scratch.
For example, for a customer support agent, select metrics like correctness, faithfulness, helpfulness, harmfulness, and stereotyping. Evaluators for tool selection and parameter accuracy help understand if agents are choosing the right tools and extracting correct parameters.
Complete the evaluation creation by choosing sampling rates, optional filters, and AWS IAM service roles.
Results are published on Amazon CloudWatch in the AgentCore Observability dashboard, providing deeper insights into requests and responses.
Custom Evaluators in AgentCore Evaluations:
Define business-specific quality metrics with custom evaluators. Provide the model to use as a judge, including inference parameters, and a tailored prompt with judging instructions.
Define the output scale, either numeric values or custom text labels. Configure whether the evaluation is computed on single traces, full sessions, or for each tool call.
AgentCore Memory: Experience-Based Learning:
AgentCore Memory, a fully managed service, now includes a new long-term memory strategy. Agents can learn from past experiences and apply those lessons to provide more helpful assistance.
Consider an agent booking travel. Over time, it learns your booking patterns and proactively suggests flexible return options based on these patterns. Agents with episodic memory can now recognize and adapt to individual needs.
When enabling the new episodic functionality, AgentCore Memory captures structured episodes, recording context, reasoning, actions, and outcomes. A reflection agent analyzes these episodes to extract insights and patterns.
Agents can retrieve these learnings to improve decision-making consistency and reduce processing time. This reduces the need for custom instructions, including only specific learnings an agent needs to complete a task.
AgentCore Runtime: Natural Conversations:
AgentCore Runtime simplifies deploying conversational experiences with bidirectional streaming. Voice agents can listen and adapt while users speak, creating natural, flowing conversations.
Building these experiences requires significant engineering effort. Bidirectional streaming simplifies this by managing the infrastructure needed for agents to process input and output, handle interruptions, and maintain context.
Deploy agents that naturally adapt to the fluid nature of human conversation, supporting mid-thought interruptions, context switches, and clarifications without losing interaction threads.
Availability and Pricing:
Amazon Bedrock AgentCore, including the Policy preview, is available in multiple AWS Regions. AgentCore Evaluations preview is available in select Regions.
With AgentCore, you pay for what you use with no upfront commitments. Visit the Amazon Bedrock pricing page for detailed information.
These new features work with any open-source framework and foundation model. AgentCore services can be used together or independently, and you can get started with the AgentCore open-source MCP server.
Conclusion:
Amazon Bedrock AgentCore's new features unlock the potential of AI agents, providing organizations with the tools to deploy them confidently and securely. With these innovations, the future of AI-agent-powered experiences is brighter than ever.
What do you think? Are you ready to embrace the power of AI agents with AgentCore? Share your thoughts in the comments!
