Evaluating AI Agents: A production blueprint with Strands and AgentCore

This article presents a production blueprint for evaluating AI agents using Strands Agents SDK and Amazon Bedrock AgentCore, demonstrated through Motorway's dealer stock search agent that improved tool selection accuracy from 87% to 98%.

• Two-phase evaluation strategy: build-time testing with strands-agents-evals and production monitoring with AgentCore Evaluations
• Three-layer assessment framework: tool usage (>95%), reasoning (>85%), and output quality (>90%) with pass/fail thresholds
• Three grader types: code-based deterministic for tool selection, LLM-as-judge for reasoning and output quality, human review for calibration
• Handle non-determinism using pass^k metric to measure reliability across consecutive trials, not single attempts
• Five-stage deployment pipeline: build-time evaluation, staging validation, shadow mode, A/B testing, production rollout with quality gates
• Production monitoring with AgentCore Evaluations: on-demand and online evaluation modes with built-in and custom evaluators
• Multi-turn conversation testing catches context drift and filter accumulation errors that single-turn tests miss
• Motorway achieved 96% task completion rate, 94% context retention, reduced incidents from 12 to 2 monthly, and detection time from hours to minutes

The blueprint demonstrates that fluent agent responses require verification of tool selection, parameter correctness, reasoning coherence, and consistency across repeated runs for production-ready AI agents.