framework
14 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 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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A process map becomes useful for AI when it becomes an authority map
Process maps are useful before agent roadmaps, but they become much more valuable when they show where authority changes hands: what an agent may observe, prepare, recommend, execute, escalate, or never touch.
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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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An agent progress report should be a control surface, not a status update
As AI agents become normal workflow participants, their progress reports need to help managers change authority, not just feel informed.
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The agent lifecycle is an authority lifecycle
AI agents should not be managed as isolated tools. They need a lifecycle for earning, expanding, reducing, restoring, and retiring authority.
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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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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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How to tell whether an AI workflow should be a workflow, an agent, or a hybrid
A practical way to choose between deterministic automation, agentic execution, and a mixed design — based on risk, ambiguity, exception load, and how much judgment the work actually needs.