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04 · Risk-tiered autonomy

Govern the tier and the action Recommendation

One governance standard applied uniformly to every agent is a common way these programs stall. A read-only summarizer waits behind the same review board as a payment agent, so teams route around the board. Tier the autonomy, then tier the individual action, and let regulatory classification and data sensitivity cap the result rather than dissolve into it.

AUTONOMY TIER ACTION RISK CLASS R0 assisted R1 read-only R2 supervised R3 bounded R4 delegated A1 read A2 draft A3 change A4 irreversible CEILING FALLS AS IMPACT RISES overlays cap it further, they never average it away
R0
Assisted · the human does the work

The model drafts, suggests or summarizes. Nothing leaves the session and nothing is written anywhere. A person types the final action themselves.

Self-service onboardingAcceptable-use policyBasic content filtersUsage telemetry
R1
Read-only agent · it fetches and reasons

The agent calls read APIs and search, chains several steps and returns an answer with citations. It cannot change any system state.

Registered identityPermission-filtered retrievalGrounding evalsFull tracingStep and token ceiling
R2
Supervised action · it proposes, a human commits

The agent prepares a concrete change: a ticket, a draft email, a config diff, a code pull request. A named person approves before anything lands.

Approval queue with SLADiff-level previewReversibility testNamed accountable ownerApproval-rate monitoring
R3
Bounded autonomy · it acts inside a fence

The agent writes to systems without per-action approval, but only within a declared envelope: named tools, value limits, tenant scope, time window and step budget. Anything outside the fence escalates.

Signed action envelopeIdempotent, reversible writesGuardian agent monitoringAutomatic rollbackReal-time spend capsPeriodic re-certification
R4
Delegated autonomy · it runs a process

The agent owns an outcome end to end, coordinates other agents and adapts its own plan. Reserve this for processes where the failure cost is understood, bounded and insurable.

Independent control reviewAdversarial red team before releaseContinuous eval in productionDual kill switchBoard-visible risk acceptanceIncident rehearsal

Autonomy is one axis. The action is the other. Author opinion

A single customer-service agent can do four things of very different consequence in the same conversation. Registering one tier against the agent and stopping there hides that. Score the use case, then score each action class inside it.

A1Retrieve and inform

Look up the policy terms, summarize the claim history, answer a coverage question with citations. Nothing changes.

Ceiling up to R3 · reversible, no state change, entitlement-filtered reads

A2Draft and propose

Compose the customer reply, prepare the case note, assemble the settlement recommendation. A person still sends or commits it.

Ceiling up to R3 · reversible before commit, review is the control

A3Change bounded state

Update an address, reschedule an appointment, reopen a case, apply a small goodwill credit. Real change, contained and undoable.

Ceiling R2, or R3 with a proven envelope · needs compensation and rollback

A4High impact or irreversible

Issue a large payment, terminate a service, decline a claim, send an external communication. Money, entitlements or reputation.

Ceiling R2, or R3 only after independent review · approval and dual control

Three overlays that cap the result

These do not average into the score. They sit on top of it, and any one of them can lower the ceiling the autonomy tier would otherwise allow.

Regulatory classification

If the process falls under a high-risk regime, the obligations attach regardless of how reversible the action looks. Determine whether you act as provider or deployer before you scope controls, because the duties differ.

Data sensitivity

Special-category, payment and regulated data raise the floor on isolation, retention and evidence even for a read-only action. An A1 lookup over sensitive records is still an A1 action with A3 data handling.

Affected-person impact

Where an outcome changes someone's money, access, employment or care, add outcome monitoring across relevant segments and a route to challenge, whatever the technical reversibility says.

Detectability

An error nobody would notice without an audit deserves more control than an equally costly error that is obvious within the hour. Silent failure modes are systematically under-governed.

How a use case gets its class. Score reversibility, blast radius, data sensitivity, affected-person impact, regulatory exposure, step autonomy and detectability. The highest single dimension sets the class, never the average, because averaging is how a payment action hides behind four harmless ones. The conversation takes about twenty minutes and surfaces more disagreement about risk appetite than any workshop. The worksheet is artifact 02.
How an agent earns more autonomy. On evidence, against exit criteria you set in advance: enough production runs for the observed error rate to carry a usable confidence interval, no unresolved high-severity failures, no open policy violations, a rollback demonstrated in the target environment, and red-team findings closed. Write the criteria down before the pilot starts, so promotion is a checklist a team can run rather than a negotiation.

Where would you start?

If you are standing up an agent platform, tightening the controls on one you already have, or preparing for an audit that now includes agents, I am happy to look at it with you.