The operational test is simpler: would a plausible result change the recommendation, escalation destination, or urgency? AWS's Control and limit retry calls guidance recommends bounded retries and selecting an appropriate retry layer.
Run diagnostics that can change the decision path — record the question, expected result classes, and how each outcome affects the next step.
Evaluate diagnostics by their decision value, weighing expected reduction in loss against cost and delay rather than collecting information for its own sake.
Bind evidence to the exact resource, revision, and measurement window and use clear verdicts (PASS, FAIL, PENDING, UNKNOWN) rather than a single boolean.
Keep evidence, approval, execution, and service recovery as separate states. A supported diagnosis can justify a change proposal, but it cannot grant permission to execute it. That permission must remain bound to the exact action and valid when execution occurs.
An unauthorized query is not eligible simply because it might be informative. Introduction An AI assistant investigating a checkout outage has identified several plausible explanations. External checkout requests were timing out, node-health observations were normal, and a gateway configuration had recently changed.
Contract, established how to qualify observations before using them. Those facts narrowed the investigation without establishing a root cause. This article continues that illustrative scenario.
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We're Measuring the Wrong Thing in AI Agents Everyone seems focused on making AI agents smarter. Bigger models. Longer context windows. Better reasoning. More tools. More autonomy. Those things matter. But I think we're overlooking a different question. What happens after the AI decides to act? Imagine an AI agent with
Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it?
Why You Should Double-Check AI Output (Before It Ships) When AI output goes unchecked, the costs compound. A single hallucinated fact takes a few minutes to produce and hours, days, or weeks to fix once it reaches a customer, a regulator, or another system that trusted it. Most teams know they should verify AI output.
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A verification agent checks another system's work against evidence. Learn the architecture patterns, the generation-verification gap, and where each one fails.
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