Agentic systems are increasingly described through their loops: plan, act, observe, reflect, repeat.
The loop is attractive because it feels like progress — iteration implies learning, and tool use implies contact with reality. But in production, the loop is not what gets adopted, audited, or blamed. Outputs do.
This is where “deep search” quietly turns into “deep risk.” Not because iteration is inherently dangerous, but because iteration without contracts creates ambiguity: ambiguous cost, ambiguous stopping, ambiguous evidence, and ambiguous responsibility. The system may produce impressive narratives, but operational environments require something stricter: agentic outputs that are verifiable, bounded, and signable.
A formal evaluation lens begins by shifting attention away from internal storytelling and toward external commitments:
- What exactly is the output claiming?
- What evidence supports it?
- What did it cost (time, money, exposure)?
- Under what constraints was it produced?
- Could another operator reproduce the same result under the same inputs?
If these questions have no structured answers, the loop may still be useful — but it is not yet governable. And if it is not governable, it will fail in the ways that matter most: through drift, leakage, unpredictable spend, or silent policy violations.
Moreover: https://www.linkedin.com/pulse/when-deep-search-becomes-risk-formal-evaluation-lens-figurelli-4yyrf/?trackingId=vK0N7XGrSxeeTeqPh1bBBQ%3D%3D