Build reliable AI agents with Amazon Bedrock AgentCore Evaluations

2026-03-31 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, extended

Summary

Amazon Bedrock AgentCore Evaluations is a newly generally available, fully managed service designed to assess AI agent performance throughout the development lifecycle. It addresses the challenges of evaluating non-deterministic large language models (LLMs) by providing systematic measurement across varied outputs, moving beyond traditional software testing. The service supports two primary evaluation approaches: on-demand evaluation for development and CI/CD workflows, and online evaluation for continuous production monitoring. It utilizes OpenTelemetry (OTEL) traces with generative AI semantic conventions to capture full interaction context and offers 13 built-in evaluators across session, trace, and tool levels, alongside support for LLM-as-a-Judge, ground truth, and custom code evaluators. AgentCore Evaluations aims to reduce the overhead of building and maintaining evaluation tooling, allowing teams to focus on improving agent quality.

Key takeaway

For AI Architects and NLP Engineers deploying LLM-powered agents, Amazon Bedrock AgentCore Evaluations offers a critical solution to bridge the gap between expected and actual agent behavior. Your teams should integrate this service to establish evidence-driven development, conduct multi-dimensional assessments, and ensure continuous measurement of agent quality from development through production. This will enable you to make informed decisions on prompt changes, model updates, and tool integrations, ultimately reducing reactive debugging and improving user experience.

Key insights

Systematic, continuous evaluation is crucial for reliable AI agent performance in production.

Principles

• Evidence-driven development
• Multi-dimensional assessment
• Continuous measurement

Method

The service uses OpenTelemetry traces to capture agent interactions, then applies built-in, LLM-as-a-Judge, ground truth, or custom code evaluators to score performance across session, trace, and tool levels.

In practice

• Use on-demand evaluation for CI/CD and development testing.
• Implement online evaluation for continuous production monitoring.
• Prioritize evaluators aligned with your agent's core purpose.

Topics

• Amazon Bedrock AgentCore Evaluations
• AI Agent Performance
• LLM-as-a-Judge
• OpenTelemetry Tracing
• Ground Truth Evaluation

Code references

• awslabs/amazon-bedrock-agentcore-samples
• awslabs/agentcore-samples

Best for: AI Architect, NLP Engineer, CTO, AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Build a test suite that grows with your agent with dataset management in Amazon Bedrock AgentCore — Versioned datasets with ground truth are crucial for reliable, comparable AI agent evaluation across development and CI/CD.
• Build custom code-based evaluators in Amazon Bedrock AgentCore — Custom code-based evaluators in Amazon Bedrock AgentCore enable deterministic, domain-specific quality checks for agentic applications.
• Semantic search across MCP tools with Amazon Bedrock AgentCore Gateway — Amazon Bedrock AgentCore Gateway enables scalable LLM agents through built-in semantic search for tool selection.
• AWS Bedrock Agents: The Beginner’s Guide — AWS Bedrock Agents provide a managed platform for building and deploying AI agents with integrated orchestration and security.
• Whitepaper Companion Podcast - Agent Quality — Agent quality requires architectural integration, focusing on the entire execution trajectory, and continuous evaluation with robust observability.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.