AWS Introduces Agent Development Lifecycle for AI Agents
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AWS has introduced the Agent Development Lifecycle (Agent DLC), a framework it describes as having one lifecycle, two loops, and a single explicit standard for judging every agent change. AWS says the framework targets three properties of AI agents that break conventional CI/CD: non-determinism, prompts treated as source code, and dependencies that move independently. In a reference HR policy assistant, AWS reports chunk-level retrieval precision of 1.00 across all three test questions, while fact-level precision fell to 0.09 and retrieval coverage hit 0.00 on one question. These figures come from AWS's own example and illustrate how the framework's standard is applied, not a general performance result.
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- AWS Machine Learning BlogAgent DLC: Engineering AI Agents from Prototype to Production
AWS introduces the Agent Development Lifecycle (Agent DLC), a framework with one lifecycle, two loops, and a single explicit standard for judging every agent change. It addresses three agent properties that break conventional CI/CD: non-determinism, prompts as source code, and independently moving dependencies. A reference HR policy assistant shows chunk-level retrieval precision of 1.00 across all three test questions while fact-level precision fell to 0.09 and retrieval coverage hit 0.00 on one question.
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