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What is agent accountability?
Agent accountability ensures every agent action is traceable, auditable, and owned — logged with role identity, inputs, decision, outcome, and a named human supervisor. The answer to a mistake is never 'the AI did it.'
In depth
Accountability is the difference between a demo and a workforce. When an agent sends the wrong refund, publishes bad pricing, or reads customer data out of scope — you need a receipt: who acted, with what connectors, under which skill version, and who approved external writes.
Neuro OS embeds accountability in the stack:
- Run logs — timestamp, role, inputs read, tools called, outputs produced - Ask approvals — human identity on every external send, post, or payment - Named owners — each role has a human keeper on the org chart - Quality sampling — periodic review of live outputs against rubrics (evaluation)
Proactive accountability beats forensic-only audits: track acceptance rate and escalation rate weekly; retrain skills when error categories repeat.
Examples
- Wrong refund — log shows role, Stripe read, calculation, approver
- Bad social post — output verification flags; post held in Ask queue
- SOC 2 audit — export connector access log by role and purpose
- Invoice role 15% error rate — skill patch after vendor-format gap found
- Low-confidence task escalated — logged as correct boundary, not failure
Related terms
FAQ
Who is legally responsible when an agent errs?
The operating company — as with employee actions. Audit trails demonstrate due diligence and speed remediation.
How do quality thresholds work?
Set rubric scores per role category; rolling averages below threshold trigger review, reduced autonomy, or pause until skill fix.
Run governed agent roles on a company OS — not only definitions in a glossary.