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Start with the proposal–execution boundary
A reliability agent can perform two very different jobs. It can investigate telemetry and propose a fault-injection experiment, or it can actually change production state, generate load, disable a dependency, or route traffic. Treating those jobs as equivalent is the primary safety mistake.
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The agent should first produce a plan that a person and a control system can inspect. A separate execution service should decide whether any action is permitted. The model’s explanation, confidence score, or apparent good intent must never override that service’s rules.
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Define the experiment envelope before granting access
Write the boundaries as machine-readable policy and as a human-readable run sheet. The envelope should identify:
- Target: the exact service, region, cluster, account, dependency or queue that may be touched.
- Faults: the permitted actions, such as injected latency, dropped requests, instance termination or dependency unavailability. List prohibited actions explicitly.
- Scope: maximum users, requests, hosts, partitions or traffic percentage that can be affected.
- Duration: a start time, an automatic expiry and the maximum wall-clock run time.
- Budget: ceilings for compute, API calls, generated load and monetary cost.
- Signals: the service-level indicators, business metrics and safety telemetry that must be watched.
- Abort rules: measurable conditions that stop the experiment, plus who owns the decision when a signal is ambiguous.
- Recovery: the rollback, fail-forward or compensation procedure and the person responsible for executing it.
Separate permissions for inspect, propose, approve and execute. An agent that can read dashboards does not automatically need access to deployment APIs, traffic controls or data stores. Microsoft guidance describes this as clear system purpose and boundaries, deterministic controls and least privilege; those controls should deny unlisted actions regardless of the prompt.
Use a distinct, short-lived identity
Google SRE guidance recommends that agents have identities distinct from human operators, use strong authentication and receive only the access needed for the approved task, on demand. Issue credentials for one experiment or short session, bind them to the approved target and expire them automatically. Do not share a person’s token with an agent, and do not use a broad service account for convenience.
Default-deny tool access. Permit individual operations, parameter ranges and resources. Log every token issuance, policy decision and tool invocation. A revoked identity should immediately prevent new actions while leaving enough audit data to reconstruct what happened.
Require a plan preview and deterministic execution gateway
Before a state-changing call, require a structured plan containing the target, fault type, parameters, sequence, expected effects, estimated blast radius, monitoring queries, stop conditions and recovery steps. The preview should show both the requested action and the resources it could affect, including indirect dependencies.
Provide a real dry-run mode. Google SRE’s production guidance specifically calls for dry_run=true support before production state is mutated. A dry run should resolve identifiers, validate permissions, calculate capacity and expose the exact calls that would be made without applying them. It is not sufficient for the agent to say that it “would” run a command.
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Route approved plans through a delegated control plane rather than allowing an investigation agent to execute a raw script. The gateway should deterministically check:
- that the plan matches the approved target and fault catalogue;
- that requested capacity, traffic and duration remain within the envelope;
- that global and agent-specific rate limits are available;
- that no conflicting experiment or maintenance action is running;
- that required telemetry, rollback artifacts and on-call coverage are present; and
- that every parameter is within an allowlisted range.
Reject malformed or out-of-policy plans without asking the model to reinterpret the rule. The gateway, not the language model, is the final authority on whether an action can run.
Choose autonomy by risk and evidence
Start with human-controlled approval. Increase autonomy only for scenarios that are narrow, reversible, well observed and supported by evaluation evidence. Compare each experiment on the following axes:
| Decision axis | Lower-risk characteristics | Higher-risk characteristics |
|---|---|---|
| Impact | Small, isolated test scope with no critical users | Customer-facing, cross-region or safety-critical impact |
| Reversibility | Immediate rollback or compensation is available | Irreversible deletion, migration or external side effect |
| Novelty | Previously exercised scenario with known limits | New fault, tool, model or untested dependency |
| Observability | Complete live signals and clear ownership | Delayed, missing or ambiguous telemetry |
| Enforcement | Deterministic gateway and narrow permissions | Manual scripts or model-selected commands |
| Recovery | Automated, rehearsed restoration | Unpracticed runbook or uncertain artifacts |
Google SRE describes a progression from monitoring and investigation assistance, through partial autonomy with human approval, to bounded high autonomy for well-defined scenarios and eventually more complex multi-step autonomy. The move upward should require robust safeguards and demonstrated precision and reliability, with stricter evidence for unsupervised actions.
Keep high-impact or irreversible actions behind qualified human approval. The UK Home Office engineering standard SEGAS-00020, last updated 20 March 2026, states: “AI-assisted outputs MUST be reviewed and approved by a human before reaching production.” That is a conservative precedent, not a universal legal rule; your organization must define its own governance obligations.
Prove readiness with layered evaluation
Test both the agent and the experiment. A passing unit test for a tool wrapper says little about behavior when the model receives incomplete telemetry, a malicious instruction or a production-shaped data set.
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| Layer | What to test | Typical failure revealed |
|---|---|---|
| Unit | Policy functions, parameter validation, tool adapters and parsing | Incorrect allow/deny decisions or malformed calls |
| Integration | Identity, gateway, telemetry, rate limits and rollback interfaces together | Permission mismatches, missing signals or broken hand-offs |
| End-to-end | Complete plan, approval, execution, monitoring and recovery in a safe environment | Unexpected sequencing, state drift or unrecoverable transitions |
| Shadow | Agent observes live conditions and proposes actions without applying them | Distribution shift, noisy reasoning or excessive intervention |
| Adversarial and fault injection | Prompt injection, privilege escalation, tool abuse, latency, errors and timeouts | Unsafe behavior under hostile or degraded conditions |
AWS’s Agentic AI Lens recommends this testing pyramid and continuous evaluation of quality, safety, efficiency and business alignment, with metrics tracked across agent versions. Unit tests alone miss edge cases, adversarial inputs and production data distributions.
Build an evaluation set from incidents, near misses, operator feedback and representative traffic. Re-run it whenever the model, prompt, tools, permissions, data sources or agent configuration changes. Record the version associated with every score so a regression cannot be hidden by averaging results across incompatible versions.
The Microsoft Agent Governance Toolkit tutorial demonstrates useful test ideas: inject latency, tool errors and timeouts, then try prompt injection, privilege escalation and tool abuse. These examples illustrate coverage; they do not establish that a particular framework is production-ready.
Set runtime limits and explicit abort conditions
Every run needs an automatic expiry and a way to interrupt an in-flight action. Apply agent-specific rate limits and circuit breakers, and cap:
- the number of actions per minute and concurrently running actions;
- affected requests, users, hosts, partitions or regions;
- duration and retry count;
- resource consumption and monetary cost; and
- the number of dependent systems that may be touched.
Define abort conditions before launch using the service’s own objectives and risk tolerance. Examples include a sustained increase in error rate, latency above an SLO budget, loss of a critical business-success signal, queue growth, replica health deterioration or a monitoring blind spot. The condition must map to an automatic stop or a named human who can stop the run immediately.
Do not copy tutorial values as universal standards. One Microsoft toolkit example uses a 50% success-rate threshold and another sample affects 30% of traffic; those numbers demonstrate code paths, not recommended cutoffs. There is no single reliability threshold suitable for every service. Derive limits from SLOs, remaining error budget, failure modes and the impact your organization can tolerate.
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Make actions interruptible at safe checkpoints. A circuit breaker should block new actions and, where possible, return the system to a known state. If an operation cannot be interrupted safely, it belongs in a higher approval tier or outside the agent’s authority.
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Make decisions and actions auditable
Maintain an append-only record that links the agent identity to the approved plan and final outcome. At minimum capture:
- model, prompt, policy and tool versions;
- identity issuance, permissions and data sources;
- the original plan, dry-run result and human approval;
- every policy check, tool call, parameter and response;
- timestamps, affected resources and relevant telemetry; and
- abort events, operator interventions, rollback steps and residual impact.
Google SRE emphasizes observability of decisions and deterministic actuation traces. The Home Office standard requires AI-assisted changes to be traceable and auditable, while Microsoft guidance says users should be able to see what the agent plans, does and which tools or data it used. Preserve enough context to reproduce a decision without retaining sensitive data unnecessarily; apply your normal access controls and retention policy to the logs.
Dashboards should distinguish agent-generated changes from human changes and show the remaining experiment budget, current blast radius, active safeguards and stop status. A green agent-health indicator must never substitute for service-level telemetry.
Make stopping and recovery operational, not theoretical
Provide a system-level pause and stop control outside the agent’s process. It should work when the model is looping, the tool API is degraded or the normal control channel is unavailable. Restrict who can invoke it, but ensure on-call staff can do so without waiting for the agent.
Prepare rollback or compensation artifacts before approval. Verify that the recovery identity has the necessary permissions, that versions and configuration snapshots are available, and that recovery does not depend on the same failed component as the experiment. For destructive actions, design compensating operations rather than assuming a perfect rollback exists.
AWS guidance recommends automated rollback and regular exercises, warning that an unpracticed runbook can fail because of missing permissions, unavailable artifacts or version mismatches. Rehearse recovery in a representative environment and record the elapsed time, data needed and any manual decisions. Treat an unsuccessful exercise as a release blocker for higher autonomy.
Use a staged release procedure
- Classify the scenario. Rate impact, reversibility, novelty, observability and recovery readiness. Assign an approval tier.
- Write the envelope. Specify target, faults, scope, duration, budget, signals, abort rules and owner.
- Issue constrained identity. Bind a short-lived agent identity to the envelope and deny all unlisted tools.
- Run the preview. Require a structured plan and
dry_run=true; inspect predicted changes and blast radius. - Pass deterministic checks. Validate capacity, policy, concurrency, telemetry and recovery artifacts in the gateway.
- Obtain approval. Require a qualified human for high-risk or irreversible actions; record the decision.
- Execute in a bounded stage. Start with the smallest safe scope and enforce expiry, rate limits and circuit breakers.
- Observe and intervene. Watch service and business signals, invoke abort rules automatically and keep the stop control available.
- Recover and review. Roll back or compensate, verify the service, preserve the audit record and update evaluations with what was learned.
Only after repeated runs meet your locally defined reliability and safety criteria should the same scenario move to less supervision. A change in model, prompt, tool, data, target system or permissions sends it back through evaluation.
Common failure modes and the corresponding guardrail
The agent has broad credentials “for flexibility”
Replace the shared account with a distinct, short-lived identity and an allowlist of exact operations. Require a new approval when the target or parameter range changes.
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The dry run is only a text explanation
Implement a gateway preview that resolves resources, validates limits and emits the calls that would execute. A natural-language summary cannot prove what the tool will do.
Operators approve a plan but cannot see live impact
Link approval to dashboards showing affected resources, SLO indicators, remaining budget and active abort rules. Pause the experiment when telemetry is delayed or incomplete.
Rollback exists in documentation but fails under pressure
Run recovery drills, verify permissions and artifacts, and measure restoration time. Keep the stop control independent of the agent.
Evaluation passes, then a model update changes behavior
Version prompts, tools, models and policies; rerun unit, integration, end-to-end, shadow and adversarial suites after every material change. Do not inherit autonomy from an earlier version automatically.
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Minimum approval checklist
- Is the target and permitted fault unambiguous?
- Is the agent identity distinct, strongly authenticated and least-privileged?
- Does the preview expose exact actions, parameters and blast radius?
- Will a deterministic gateway enforce policy, capacity and concurrency limits?
- Has the scenario passed layered and adversarial evaluation?
- Are duration, traffic, resource, cost and rate limits enforced automatically?
- Are measurable abort conditions tied to live service signals?
- Can an authorized operator pause or stop the run independently?
- Are rollback or compensation steps executable and rehearsed?
- Will the complete decision and actuation trail be auditable?
Bottom line
Grant an AI agent authority to run a reliability experiment only when its identity, tools, plan, limits, approval, telemetry and recovery path are independently enforceable. Keep the model in the planner role and place mutation behind deterministic controls. Begin with human approval and small, reversible experiments; earn broader autonomy through repeatable evaluation and successful recovery drills. The right thresholds come from your service objectives, error budget and impact tolerance—not from a generic percentage copied from a tutorial.
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