Amazon Bedrock AgentCore launches recommendations and two ways to validate performance (batch evaluations and A/B tests). This completes the observe, evaluate, improve loop for AI agents in production. Until now, translating evaluation findings into concrete, validated improvements required manual developer intervention and intuition rather than a systematic approach. With recommendations, batch evaluations and A/B tests, developers now have the tools to act on what evaluations surface.
As models evolve and user behavior shifts, agent quality degrades quietly over time. The recommendations capability analyzes production traces and evaluation outputs generated by AgentCore to create optimized system prompts and tool descriptions tailored to your specific workload. Batch evaluations are then used for validating the recommendations against pre-defined test cases. A/B tests further validate those recommendations through controlled A/B testing against pre-defined test sets or live production traffic, with statistical significance reported before any change is promoted. Every recommendation requires your approval before it ships. Together, these capabilities complete the performance improvement cycle for agents. Agents don’t just run, they get better, on your terms.
You can use optimization capabilities in all AWS Regions where AgentCore Evaluations is available. To learn more, visit the AgentCore documentation.
Categories: marketing:marchitecture/artificial-intelligence,general:products/amazon-bedrock
Source: Amazon Web Services
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