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Require eight controls before an AI agent can change a system or inject a fault: a distinct least-privilege identity, a written experiment envelope, an inspectable dry-run plan, a deterministic execution gateway, risk-tiered human approval, layered evaluation, hard runtime and blast-radius limits, and live intervention plus rehearsed recovery. Let the agent suggest and analyze experiments before it is allowed to execute them. Increase autonomy only for narrow, reversible scenarios after evidence shows that the agent behaves reliably under normal, edge-case, and adversarial conditions.
No official guidance establishes one universal error-rate, traffic, or duration cutoff. Set those numbers from the service’s SLOs, error budget, failure modes, regulatory obligations, and tolerance for customer impact.
Start with an explicit experiment envelope
Write the boundaries before granting the agent any actuation permission. The envelope is the contract between the reliability team and the agent, and it should be machine-readable where possible.
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- Target services, environments, regions, clusters, accounts, and datasets.
- Permitted fault types, such as latency, dependency failure, instance termination, packet loss, or resource exhaustion.
- Maximum duration, affected users or traffic, resource quantity, and cost.
- Signals that must be collected, the expected range for each signal, and the owner watching them.
- Abort conditions, who can invoke the stop control, and the recovery procedure.
Separate three permissions: inspect (read telemetry and configuration), propose (draft a plan), and execute (make a state change). An agent may receive the first two while the third remains disabled until every gate passes.
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Record exclusions
List systems and actions that are always out of scope: identity infrastructure, payment paths, irreversible data deletion, safety-critical controls, and any production surface without tested rollback. A deny list is not a substitute for least privilege, but it provides a clear final barrier when a request is ambiguous.
Constrain identity and authority
Give each agent a separate, strongly authenticated identity rather than reusing a human account or a shared service credential. Google SRE guidance recommends distinct agent identities, on-demand access, agent-specific rate limits, and circuit breakers. The identity should appear in every authorization decision and log entry.
Use least privilege and least action
- Grant only the APIs and resources required for the approved experiment.
- Issue short-lived credentials just before execution and revoke them afterward.
- Deny unlisted tools, network destinations, shell access, and data classes by default.
- Constrain parameters at the policy layer: resource names, regions, maximum counts, and allowable fault modes.
- Keep investigation credentials separate from actuation credentials.
Do not rely on the model to respect a textual instruction such as “do not terminate more than two instances.” Enforce the limit in an authorization or execution service that evaluates every call, regardless of the agent’s explanation or confidence.
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Make delegation visible
If a human approves a plan and the agent executes it, retain the approval decision, approver identity, plan hash or version, credential grant, and exact tool calls. This prevents an agent from quietly expanding a delegated task and lets an incident investigator reconstruct responsibility.
Require a plan preview and deterministic execution gateway
Before any mutation, require a dry run that exposes the proposed operation and its consequences. Google SRE’s production guidance explicitly describes a dry_run=true capability before production state is changed.
What the preview must contain
- Target resources and selection query, including the resolved resource list.
- Fault type, intensity, schedule, and all parameters that will be sent to tools.
- Predicted blast radius: traffic, users, dependencies, capacity, regions, and cost.
- Preconditions, expected telemetry, and the evidence supporting those expectations.
- Rollback or compensation steps, required permissions, and estimated recovery time.
- Abort thresholds and the exact signal that will trigger each one.
Render the preview in a form a reviewer can understand, then store the immutable version that was approved. A natural-language summary is useful, but it must be accompanied by structured parameters that a policy engine can inspect.
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Put a policy gateway in front of tools
Route every action through a delegated control plane instead of allowing an investigation agent to run an arbitrary script. The gateway should deterministically check authorization, capacity, global and agent-specific rate limits, concurrent experiments, maintenance windows, and current incidents. It should reject a call when any check fails; the model cannot override that result by rephrasing the request.
The gateway should also reserve capacity and acquire a per-target experiment lock. Without those controls, two individually safe plans can combine into an unsafe outage.
Match autonomy to risk and evidence
Start with human-controlled approval. Google SRE describes a progression from monitoring and investigation assistance, through partial autonomy with approval, to bounded high autonomy for well-defined scenarios and, eventually, multi-step autonomy. Moving upward requires robust safeguards and demonstrated precision and reliability, with greater rigor for unsupervised actions that could cause more harm.
| Autonomy level | Typical authority | Minimum gate | Appropriate use |
|---|---|---|---|
| Observe | Read telemetry and configuration only | Identity, data-access policy, complete audit trail | Baseline collection and anomaly investigation |
| Propose | Generate a plan and dry-run result | Schema validation, policy simulation, reviewer visibility | Routine, reversible experiments awaiting approval |
| Execute with approval | Run an approved plan through the gateway | Qualified human approval, capacity checks, stop control, rollback | Most production experiments at initial adoption |
| Bounded autonomous | Run only pre-authorized playbooks within strict limits | Strong evaluation evidence, deterministic limits, live monitoring, rehearsed recovery | Low-impact, reversible, well-understood scenarios |
| Multi-step autonomous | Chain several permitted actions | Highest evidence standard and independent oversight | Rare cases with narrow scope and proven failure handling |
Keep high-impact, irreversible, novel, or poorly observed actions behind approval. The UK Home Office engineering standard SEGAS-00020, last updated 20 March 2026, states that AI-assisted outputs must be reviewed and approved by a human before reaching production. That is a conservative governance precedent; your organization should document any stricter requirement.
Test both the agent and the experiment
A reliability experiment can be safe while the agent is unsafe, and an agent can be well-behaved while the experiment design is invalid. Evaluate both layers.
Build a testing pyramid
- Unit tests: Test parsers, parameter validators, policy rules, credential handling, and rollback functions with deterministic fixtures.
- Integration tests: Exercise the agent’s tool adapters, authorization gateway, telemetry queries, lock service, and notification paths together.
- End-to-end scenarios: Run complete proposal, approval, execution, abort, and recovery flows in an isolated environment.
- Shadow tests: Let the agent observe live-like events and produce decisions without granting mutation rights; compare its proposed actions with expert decisions.
AWS’s Agentic AI Lens recommends this layered approach and continuous evaluation of quality, safety, efficiency, and business alignment. Unit tests alone miss edge cases, adversarial inputs, and the data distributions found in operation.
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Include adversarial and fault-injection cases
- Prompt injection in logs, tickets, dashboards, and service responses.
- Privilege-escalation requests and attempts to call an unlisted tool.
- Malformed or stale telemetry, missing dependencies, and conflicting signals.
- Tool errors, latency, timeouts, partial execution, duplicate requests, and lost acknowledgements.
- Concurrent experiments that compete for the same capacity.
- Rollback failures, expired credentials, unavailable artifacts, and version mismatches.
The Microsoft Agent Governance Toolkit tutorial demonstrates fault injection for latency, tool errors, and timeouts, along with tests for prompt injection, privilege escalation, and tool abuse. Those are useful test ideas, not proof that any particular framework is production-ready.
Re-evaluate after every material change
Rerun the relevant suites when the model, system prompt, tools, policy rules, data sources, retrieval index, or agent configuration changes. Keep versioned metrics so a new release can be compared with the last approved one. Add cases from incidents, near misses, reviewer feedback, and newly observed attack patterns.
Set runtime, cost, and blast-radius limits
Limits must be enforced during execution, not merely stated in the plan. Use agent-specific rate limits and circuit breakers, and cap duration, affected traffic, resources, and spend. Make every action interruptible.
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Choose abort values from the service’s own success rate, latency objectives, availability target, error budget, dependency limits, and customer-impact analysis. A threshold that is safe for an internal batch service may be disastrous for a payment API. Record the rationale and owner for each number.
Do not copy tutorial examples as universal standards. The Microsoft toolkit tutorial shows a sample 50% success-rate abort condition and a sample experiment affecting 30% of traffic; those values illustrate code, not generally safe cutoffs.
Use layered stopping mechanisms
- Pre-flight stop: Reject plans that exceed policy, capacity, concurrency, or budget limits.
- Automatic stop: Trip a circuit breaker when a declared signal crosses its threshold for the specified observation window.
- Operator stop: Provide a system-level pause control that does not depend on the agent cooperating.
- Time stop: Expire the experiment and credentials automatically at the maximum duration.
- Post-stop containment: Prevent retries or queued actions from restarting the fault after cancellation.
Make observability and accountability complete
Capture enough information to answer what the agent knew, why it acted, what it changed, and what happened next. At minimum, log:
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- Agent identity, model and configuration version, prompt or policy version, and credential grant.
- Approved plan, dry-run output, reviewer decision, and policy-check results.
- Every tool call, parameter, target, response, retry, timeout, and resulting state change.
- Telemetry before, during, and after the experiment, including abort signals.
- Notifications, operator interventions, rollback actions, final outcome, and residual damage.
Protect logs from alteration, synchronize timestamps, redact secrets and unnecessary personal data, and retain records for the period required by your incident and compliance policies. The Home Office standard calls for traceable, auditable AI-assisted changes; Google SRE emphasizes observability of decisions and actuation traces; Microsoft recommends visibility into what an agent plans, does, and accesses.
Prove that stopping and recovery work
A stop button that has never been exercised is a hope, not a control. The recovery path must be executable with the permissions and artifacts available during an incident.
Rehearse rollback and compensation
- Run the approved experiment in a non-production environment and invoke the stop control while actions are in flight.
- Verify that new actions cease, queued work is cancelled, and credentials expire.
- Execute rollback or compensation, including partial-failure branches.
- Confirm service health, data integrity, capacity, and alert recovery.
- Measure recovery time and record missing permissions, artifacts, or operator steps.
AWS guidance recommends automated rollback and regular exercises, noting that an unpracticed runbook can fail because of missing permissions, unavailable artifacts, or version mismatches. Assign a named recovery owner and keep a manual fallback for cases in which the agent, gateway, or control plane is unavailable.
Use a launch checklist
Before enabling execution for a new agent or playbook, require a recorded sign-off that all of these statements are true:
- The target, fault, scope, duration, budget, signals, abort criteria, and recovery owner are documented.
- The agent has a distinct authenticated identity and only on-demand permissions for this task.
- The dry run resolves exact targets and passes deterministic policy, capacity, rate, and concurrency checks.
- The risk tier determines the required reviewer and approval evidence.
- Unit, integration, end-to-end, shadow, edge-case, and adversarial tests have passed at the approved versions.
- Live dashboards, alerts, audit logging, pause controls, and circuit breakers have been tested.
- Rollback or compensation has been rehearsed with current credentials and artifacts.
- Stakeholders know the schedule, expected signals, escalation path, and stop authority.
Common failure modes and corrective actions
The agent proposes a plausible but over-broad target
Require the preview to list resolved resources and have the gateway enforce an allowlisted scope. Reject wildcard selectors and require a fresh capacity check immediately before execution.
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Use global concurrency limits and per-resource locks. Make the gateway aware of maintenance windows, incidents, and other active experiments instead of evaluating each request in isolation.
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The agent retries after an operator cancels
Give each run a cancellation token checked by every tool call, revoke its credentials, and drain queued work. Test cancellation during timeouts and partial failures.
Telemetry is delayed or misleading
Define freshness requirements for each signal, fail closed when critical data is stale, and include independent health indicators. Do not let the agent infer safety from a missing metric.
Rollback is technically available but operationally unusable
Exercise it on a schedule, verify permissions and artifact versions, and keep a human-operated fallback. Treat an untested recovery path as a reason to lower the autonomy level.
Reassess governance as evidence changes
Review autonomy after incidents, near misses, model or tool changes, policy changes, and major architecture changes. Keep high-risk actions approval-gated even when lower-risk playbooks run autonomously. The evidence supports a graduated model, not a permanent promise that an agent is safe in every context.
The most defensible rule is simple: the agent may plan broadly only when the system can enforce narrowly. Grant execution authority when identity, policy, evaluation, limits, observability, intervention, and recovery are all demonstrably stronger than the consequences of a mistake.
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