AWS proposes the Agent DLC agent engineering methodology
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In its machine learning blog, AWS proposes the Agent Development Lifecycle (Agent DLC), which uses one lifecycle, two loops, and unified acceptance criteria to evaluate every change to an agent. Its recommendation is to select a well-bounded business task, aim to establish an initial evaluation baseline within a week, and prepare 20–30 golden samples covering common requests and important edge cases, with expected behavior confirmed by the business owner; build the agent on AWS using models provided by Amazon Bedrock. AWS states that in its HR policy assistant reference implementation, chunk-level retrieval precision was 1.00 on all three questions, but fact-level precision was only 0.59, 0.53, and 0.09, and retrieval coverage on the third question was 0.00. AWS uses this to show that looking only at aggregate metrics masks coverage gaps.
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- AWS Machine Learning BlogFrom prototype to production: Engineering agents with Agent DLC
AWS authors propose the Agent Development Lifecycle (Agent DLC), which ties six activities—Define, Build, Evaluate, Release, Observe, and Feed Back—to an explicit "gold standard" of criteria, thresholds, and approved examples, with an inner loop (score, inspect the path, change one thing, re-evaluate) and an outer loop that updates the standard from production feedback. A reference HR policy assistant shows chunk-level retrieval precision of 1.00 on all three test questions while fact-level precision was 0.59, 0.53, and 0.09, and a prompt-only change improved one question but worsened another, which the authors attribute to missing source coverage. The post also describes a benefits-enrollment answer that scored 0.90 groundedness yet made a false claim about the user's contact preference, and outlines a one-week AWS getting-started plan using Amazon Bedrock AgentCore Evaluations and Observability.
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