Before an AI agent earns another task, make it close the current one:
1. Return the evidence it used
2. Show the decision it made
3. Name what changed
4. Expose unresolved risk
5. Point to the outcome record
Agents should finish loops before they expand scope.
Agents fail organizationally when their work has no address. Every run should end in one place: a decision queue, an approved asset, a bounded action, or an outcome record. If the result lands in chat, Automation has created another inbox.
An AI agent needs a restart test.
Pause it mid-work. Can a human see what it learned, which decision is pending, what has been created, what action comes next, and what outcome closes the run?
If the agent cannot resume safely, it is not autonomous. It is session-dependent.
Operator question: which evidence should an AI agent show before it earns a wider job?
A. Fewer human rescues
B. Faster recovery
C. Cleaner audit trail
D. Better business outcome
Autonomy should expand because proof improved—not because the demo worked.
Your AI tools got their own computers this week. Ours got a parachute.
Fall from 100 km over the real Earth, catch the tools that are yours, land on a wonder of the world.
Only a clean run makes Architect. Reply with your drop zone and grade.
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Agent reliability is not the percentage of runs that finish. It is the cost of the runs that fail.
Track: damage radius, time to detect, time to recover, and whether the same failure repeats.
Automation earns scope when recovery gets cheaper—not when demos get smoother.
Agent workflow test: remove the chat window.
Can the system still show:
• current state
• next allowed action
• exception owner
• rollback path
• outcome receipt
If not, you have an answer generator with side effects—not an inspectable workflow.
A good first agent job is boring and inspectable:
Find maintenance debt → propose a fix → run tests → request review → record the result.
If success cannot be checked, the workflow is not ready for autonomy.
Creation standards should be versioned like code.
When a human fixes an AI draft, record the rule behind the edit—not just the revised sentence.
Examples become Intelligence. Rules guide Decisions. Better drafts reduce review. Outcomes should update the standard.
Brand voice is not a list of adjectives.
Build it from:
• approved examples
• forbidden claims
• evidence rules
• recurring human edits
The creation tool should learn the standard, not impersonate the founder.
We’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent.
It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.
Wednesday creation check: define what “done” must do before you generate. Name the audience, the required response, and one reason to reject the draft. Then make.
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Brand voice is not a list of adjectives.
Build it from:
• approved examples
• forbidden claims
• evidence rules
• recurring human edits
The creation tool should learn the standard, not impersonate the founder.
Before expanding AI authority, compare three cases: one it handled well, one a human corrected, and one it escalated. The point is not a perfect score. It is a better boundary: automate the clear case, escalate the uncertain one, and rewrite the rule after the miss.
AI delegation has two budgets: authority and uncertainty.
Authority: what the system may change.
Uncertainty: how much doubt it may carry into that change.
Spend either without a limit and review becomes cleanup. Set both before the first automated decision.