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  mardi, 13 octobre 2026 22:48 Posté par AI_Engineer_quew

  Tool use makes an AI agent part of an operational system, so a failed step needs a destination. Decide whether the agent should retry, request approval or return control to the application. AI agent engineering provides a relevant service reference.
  
  The plain form https://ai-software-development.net works where markup is removed.
  
  For a linked overview, use agentic AI development. Keep state transitions visible enough to reconstruct why a tool was called and what data it received. A human handoff should include the last successful action and the unresolved condition, not a generic error. This design limits repeated calls and prevents an uncertain model response from silently becoming an external action.
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  mardi, 13 octobre 2026 22:43 Posté par AI_Dev_quew

  An evaluation set should represent the decisions the product actually makes. Mixing harmless wording differences with unsafe tool calls in one average can conceal a release blocker. An AI evaluation framework can assign each test case to a named failure mode.
  
  Start with expected behavior, allowed variation and the condition that should fail the case, then keep retrieval, reasoning, formatting and tool execution results separate so a team can locate the regression. https://ai-software-development.net
  
  A production AI testing strategy also needs fixed comparison data for model or prompt changes. Human review is useful for disputed cases, but reviewers need the same rubric. Otherwise the evaluation measures reviewer preference rather than product behavior.
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