evaluation
15 posts tagged here.
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Before widening agent authority, review reversals and overrides by commitment type
AI rollback is not just a go-or-stop decision. If an agent is creating real commitments, teams need to study what had to be reversed, what humans overrode, and which authority boundary should change next.
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The next agent platform decision is evidence portability
As agent platforms become the way work is run, the important question is not only which system can coordinate agents. It is whether the evidence of that work stays usable when the platform changes.
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The agent operating review should combine the evidence, not repeat the dashboard
Progress reports, exception logs, audit packets, authority maps, and evidence ledgers only matter if they come together in one operating review that changes what the agent is allowed to do next.
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The agent evidence ledger is the missing companion to the authority map
An authority map says what an agent may do. An evidence ledger says why it has earned that authority, where trust is still provisional, and what should change after real operating evidence appears.
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An agent exception log should change the workflow, not just judge the agent
The useful exception log is not a scorecard for the agent. It is the repair list for the workflow that produced the exception.
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Retiring an agent is an authority decision, not a cleanup task
When an AI agent stops earning trust, retirement should be a designed authority transition, not an informal deletion after everyone has moved on.
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Restoring agent authority should require remediation evidence
After an AI agent is demoted, authority should return because the operating evidence changed, not because enough time passed.
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Demotion criteria are part of agent authority design
If an AI agent can earn more authority, it should also have clear conditions for losing authority before failure becomes dramatic.
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The agent audit packet should exist before the next permission change
After an AI agent is deployed, do not wait for an incident to gather evidence. Build a small audit packet before changing its permissions.
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Agent promotion reviews should be operating reviews, not vibe checks
Before an AI agent gets more authority, review how it behaved in real work: exceptions, escalations, rollback evidence, and human review burden.
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The agent rollback plan should exist before the agent gets more authority
If an agent can change real work, the rollback plan is part of the authority design, not an afterthought for when something goes wrong.
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The agent exception log is more important than the success rate
Success rates tell you whether an agent works in normal cases. Exception logs tell you whether it deserves more authority.
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Before you expand an agent's authority, ask what it has earned
Agent adoption is moving faster than production trust. The practical answer is not to freeze autonomy or grant it on vibes, but to make authority expansion evidence-based.
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The real bottleneck in AI is pilot escape velocity
DeepSeek’s V4 preview will get plenty of attention, but the more important signal right now is that many agentic AI projects still cannot escape pilot mode. The practical bottleneck is shifting from model access to operational trust.
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Why most AI use cases are too vague to be useful
The real bottleneck in AI projects is often not the model. It is that the supposed use case is still too fuzzy to build, test, or judge properly.