Decision Desk · Issue 09 · complete public learning edition
What Should Your AI Agent Be Allowed to Do?
Decide when an AI agent may send, spend, change records, move data, schedule work, or act through another system—and when it must remain propose-only.
Authority chain
Purpose → action → limit → approval → execution → reversal.
Agent autonomy is not one switch. It is a separate, enforceable decision for every action class, target, consequence, system, and environment.
Inventory direct and downstream actions
Deny by default and enforce least privilege
Place informed approval before consequence
Observe execution and reconcile state
Stop, reverse, repair, and review
Complete issue curriculum
Twelve cumulative lessons.
Use supplied fictional workflows only. Never enter live credentials, personal records, security configurations, financial information, or executable instructions.
01Issue 09Separate recommendations from actions
Open
Separate recommendations from actions
Generating, proposing, drafting, approving, executing, and confirming are different powers. Name the highest consequential step the agent can actually perform.
02Issue 09Define one actor and purpose
Open
Define one actor and purpose
Bind authority to one agent identity, version, owner, initiating user, environment, workflow, affected population, and purpose. A general-purpose agent has no general-purpose mandate.
03Issue 09Inventory every action class
Open
Inventory every action class
List messages, purchases, commitments, record changes, data movement, scheduling, deletion, access changes, external calls, and downstream triggers—including actions hidden inside tools.
04Issue 09Apply least privilege and deny by default
Open
Apply least privilege and deny by default
Grant only the smallest roles, scopes, systems, records, fields, targets, amounts, volumes, and time windows necessary. Unlisted actions and destinations remain denied.
05Issue 09Place human approval before consequence
Open
Place human approval before consequence
Approval must show the proposed action, target, data, amount, uncertainty, source, and downstream effect before execution—not a vague confirmation after the action is queued.
06Issue 09Enforce limits outside the model
Open
Enforce limits outside the model
Prompts are instructions, not access controls. Enforce spend, volume, target, rate, time, data, and action limits in permissions, policy layers, transaction controls, and receiving systems.
07Issue 09Control credentials and delegation
Open
Control credentials and delegation
Use agent-specific credentials, short lifetimes, narrow scopes, secure storage, rotation, revocation, and separation from the initiating person. Never let an agent acquire broader authority by asking another tool.
08Issue 09Design for partial failure and duplication
Open
Design for partial failure and duplication
Assume timeouts, retries, stale state, conflicting updates, repeated sends, partial transactions, and downstream success without confirmation. Use idempotency, state checks, and reconciliation.
09Issue 09Prove reversibility before autonomy
Open
Prove reversibility before autonomy
Distinguish true undo from compensating action and irreversible consequences. Test rollback, fallback, restoration, notification, financial reconciliation, and affected-person repair.
10Issue 09Make action observable
Open
Make action observable
Log the initiator, model, tools, inputs, retrieved sources, proposed action, approval, execution, system response, state change, and errors without exposing secrets or unnecessary personal data.
11Issue 09Install stop and incident control
Open
Install stop and incident control
Provide a reachable kill switch, credential revocation, queue pause, containment, evidence preservation, affected-party notice, incident ownership, and safe human continuation.
12Issue 09Issue an action-specific disposition
Open
Issue an action-specific disposition
Do not connect, keep propose-only, repair controls, or evaluate one bounded human-approved action. No authority transfers across actions, systems, users, environments, or versions.
Critical stop conditions
Capability is not authority.
- the agent, owner, initiating user, purpose, or affected population is unresolved
- an action or downstream tool capability is absent from the authority inventory
- a prompt or model refusal is the only limit on consequential action
- approval happens after execution or hides the actual target, amount, data, or effect
- the agent uses shared, persistent, overbroad, or nonrevocable credentials
- duplicate, stale-state, partial-failure, or cross-system conflict behavior is untested
- a material consequence cannot be reversed, compensated, reconciled, or clearly disclosed
- logs, monitoring, kill switch, incident owner, or safe human fallback is missing
Interactive instrument · device-local
Agent Action Authority and Reversibility Matrix
Use fictional workflows only. Do not enter credentials, account details, security configurations, personal records, live instructions, or confidential information.
Each action class needs explicit authority, enforced limits, observable execution, tested reversal, and a human stop path.
| Fictional action class | Purpose allowed | Inputs authorized | Approval required | Limit enforced | Rollback proven | Audit + stop works |
|---|---|---|---|---|---|---|
| Send an external message | ||||||
| Spend or commit funds | ||||||
| Create or change a system record | ||||||
| Move data between systems | ||||||
| Schedule, cancel, or trigger downstream work |
Restoring your local matrix…
Completion boundary
The educational product and reusable Authority and Reversibility Matrix are complete.
Real use still requires exact system and configuration evidence, security and legal review, enforced permissions, representative failure testing, verified approval and rollback, monitoring, incident rehearsal, affected-party safeguards, and organizational authorization.