Amazon Bedrock AgentCore adds quality evaluations and policy controls for deploying trusted AI agents | AWS News Blog (opens in new tab)

AWS has introduced several new capabilities to Amazon Bedrock AgentCore designed to remove the trust and quality barriers that often prevent AI agents from moving into production environments. These updates, which include granular policy controls and sophisticated evaluation tools, allow developers to implement strict operational boundaries and monitor real-world performance at scale. By balancing agent autonomy with centralized verification, AgentCore provides a secure framework for deploying highly capable agents across enterprise workflows.

Governance through Policy in AgentCore

• This feature establishes clear boundaries for agent actions by intercepting tool calls via the AgentCore Gateway before they are executed.
• By operating outside of the agent’s internal reasoning loop, the policy layer acts as an independent verification system that treats the agent as an autonomous actor requiring permission.
• Developers can define fine-grained permissions to ensure agents do not access sensitive data inappropriately or take unauthorized actions within external systems.

Quality Monitoring with AgentCore Evaluations

• The new evaluation framework allows teams to monitor the quality of AI agents based on actual behavior rather than theoretical simulations.
• Built-in evaluators provide standardized metrics for critical dimensions such as helpfulness and correctness.
• Organizations can also implement custom evaluators to ensure agents meet specific business-logic requirements and industry-specific compliance standards.

Enhanced Memory and Communication Features

• New episodic functionality in AgentCore Memory introduces a long-term strategy that allows agents to learn from past experiences and apply successful solutions to similar future tasks.
• Bidirectional streaming in the AgentCore Runtime supports the deployment of advanced voice agents capable of handling natural, simultaneous conversation flows.
• These enhancements focus on improving consistency and user experience, enabling agents to handle complex, multi-turn interactions with higher reliability.

Real-World Application and Performance

• The AgentCore SDK has seen rapid adoption with over 2 million downloads, supporting diverse use cases from content generation at the PGA TOUR to financial data analysis at Workday.
• Case studies highlight significant operational gains, such as a 1,000 percent increase in content writing speed and a 50 percent reduction in problem resolution time through improved observability.
• The platform emphasizes 100 percent traceability of agent decisions, which is critical for organizations transitioning from reactive to proactive AI-driven operations.

To successfully scale AI agents, organizations should transition from simple prompt engineering to a robust agentic architecture. Leveraging these new policy and evaluation tools will allow development teams to maintain the necessary control and visibility required for customer-facing and mission-critical deployments.