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Do not approve an AI agent because it performs well in a demo. Evaluate it as a socio-technical system operating with your data, tools, permissions, people and failure-recovery procedures. Before adoption, define the exact jobs it may perform, prohibited actions, acceptable error rates and stop conditions; test routine, ambiguous and adversarial cases in a sandbox; measure both useful work and unsafe behavior; require human approval for high-impact actions; and deploy in stages with restricted access, continuous logging and a tested rollback or pause path. Expand its permissions only when documented results show that the remaining risk is acceptable in your environment.
What does reliability mean for an AI agent?
Reliability is the probability that an agent performs its assigned work correctly, safely and predictably under the conditions in which you will actually use it. That includes its model, prompts, memory, connected tools, identity, permissions, data quality, network services, human approvals and operating policies. A system can score highly on a public benchmark and still fail when your API returns an error, a document contains a malicious instruction or a permission changes midway through a task.
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Unlike a stand-alone language model, an agent can plan across several steps, call tools, retain state and take actions. Those capabilities add failure modes: an incorrect assumption can propagate through a workflow, a tool response can manipulate the next step, and a harmless-looking request can become a destructive action when the agent has write access. Reliability therefore has to cover usefulness, security, governance and recovery, not just answer quality.
The Canadian Centre for Cyber Security advises organizations to assume agentic systems may behave unexpectedly while security practices and evaluation standards mature. Treat that as a planning assumption: design controls so one bad decision cannot become an irreversible incident.
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How should I define reliability before testing?
Write a task specification before you run the first test. It turns a vague claim such as “works for customer support” into conditions that can be measured and audited.
Specify the operating contract
- Intended tasks: describe the inputs, outputs, business objective and maximum number of steps.
- Allowed tools and data: list each API, database, file store, browser, memory source and network destination the agent may use.
- Prohibited actions: state what it must never do, such as deleting records, changing production code, exporting personal data or disabling a security control.
- Identity and permissions: define the service account, role, environment and approval required for every action.
- Error budget: set tolerable rates for wrong answers, failed tasks, unnecessary escalations, latency and resource use.
- Impact and reversibility: classify outcomes as low, medium or high impact and record whether each action can be undone.
- Stop conditions: specify when the agent must pause, ask a person, retry, switch to a safe mode or terminate.
- Acceptance thresholds: write pass/fail limits before seeing results so the target cannot be moved after a disappointing test.
NIST’s AI Risk Management Framework (AI RMF) 1.0, published January 26, 2023, is a voluntary framework for managing AI risk and trustworthy characteristics through design, development, deployment, use and evaluation. Its structure is useful for organizing this contract, even when you do not formally adopt the entire framework.
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Which risks should be mapped before an evaluation?
Build a threat model that connects each capability to a plausible harm. Rank scenarios by impact, likelihood and reversibility, then use the ranking to decide test depth and approval requirements.
Operational and decision risks
- Wrong, incomplete or outdated answers that lead a person to make a consequential decision.
- Goal drift, in which the agent pursues an intermediate objective rather than the approved business goal.
- Long-horizon compounding errors, where a small early mistake contaminates later steps.
- Downtime, rate limits, stale indexes or malformed API responses that cause retries or skipped checks.
- Cascading failures when one agent, service or data source triggers actions in another system.
Security and privacy risks
- Prompt injection in an email, web page, document or tool response that attempts to override policy.
- Jailbreaks or crafted inputs that bypass safety instructions, monitors or input filters.
- Unauthorized tool calls, privilege escalation or use of a service account outside its role.
- Data leakage through prompts, memory, logs, generated files or network egress.
- Impersonation, altered identity claims or a permission change that the agent fails to notice.
For every high-impact path, identify the worst credible outcome and the control that should prevent it. A reversible draft saved in a sandbox needs a different gate from a payment, deletion, external message or production change.
What scenarios belong in a representative test set?
Use a balanced set that mirrors real work and deliberately stresses the boundaries. A small collection of happy-path prompts cannot reveal how the agent behaves when context is incomplete, contradictory or hostile.
Normal and edge cases
- Routine requests covering the common input formats, languages, data volumes and user roles.
- Ambiguous requests that require the agent to ask a clarifying question rather than guess.
- Missing, conflicting, duplicated or stale records, including an explicit source-of-truth rule.
- Long, multi-step tasks with checkpoints, dependencies and a partial failure in the middle.
- Unusual but legitimate cases that are rare in production and likely to be underrepresented in demos.
Adversarial and environmental variation
- Direct jailbreak attempts and indirect prompt injection in retrieved content or tool output.
- Malicious, malformed, delayed, empty or contradictory responses from connected tools.
- Changing permissions, expired credentials, revoked access and identity mismatches.
- Different autonomy levels: answer-only, draft, recommend, execute with approval and execute within a narrow policy.
- Different tool configurations, network conditions, model settings, context lengths and concurrent loads.
Canadian guidance recommends edge cases, best-of-N sampling, multi-step prompts, varied contexts and continuous capability evaluation. Best-of-N means repeatedly sampling a task when the agent is nondeterministic, then examining both the strongest and weakest outcomes rather than reporting only one run.
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Which metrics should an agent pass?
Define metrics that expose unsafe behavior as well as useful output. Report results by scenario type, autonomy level, tool set and impact class; an overall average can hide a severe failure in a small but important category.
| Measure | What to record | Why it matters |
|---|---|---|
| Task success and quality | Completion, correctness, required fields, source use and human-rated quality | Shows whether the agent actually achieves the intended job |
| Factuality and uncertainty | Verified claims, abstentions, confidence or uncertainty signals, and calibration against outcomes | Reveals when the agent should defer instead of sounding certain |
| Unsafe or unauthorized actions | Blocked and successful violations, attempted privilege escalation, data exposure and policy breaches | Captures risk that a usefulness score cannot show |
| Tool-call correctness | Right tool, arguments, sequence, scope, retries and handling of errors | Tests the action layer rather than only generated text |
| Prompt-injection resistance | Whether hostile instructions change goals, permissions, data handling or approval behavior | Measures a common agent-specific attack path |
| Escalation and override | When it asks a person, whether the request contains enough context, and whether a human can stop it | Tests the human-control boundary |
| Recovery | Safe behavior after tool failure, timeout, bad data, revoked access or partial completion | Shows whether faults remain contained |
| Performance limits | Latency, token or compute use, rate-limit behavior and runtime ceilings | Prevents an apparently accurate system from becoming unusable or expensive under load |
NIST measurement guidance calls for uncertainty reporting, benchmark comparisons, formal results and documentation. Australian National AI Centre guidance likewise says acceptance criteria and test methods must reflect intended use, context and potential risks. Record the test version, date, scenario identifiers, evaluator, configuration and evidence for every pass or fail.
How do I test safety and security separately from usefulness?
Run a dedicated safety and security campaign in an isolated environment, even when the agent performs well on normal tasks. A system can be useful and still be unsafe to connect to sensitive tools.
- Clone the workflow in a sandbox. Use synthetic or properly de-identified data, test credentials, fake destinations and reversible records. Block production network paths by default.
- Probe instruction boundaries. Try direct jailbreaks, indirect instructions in documents, conflicting system and user requests, encoded text, and requests to reveal hidden prompts or secrets.
- Attack the tool boundary. Return malicious, malformed, delayed and contradictory tool responses. Check whether the agent validates fields, limits retries and ignores instructions that arrive as data.
- Test permissions. Attempt read-versus-write violations, cross-tenant access, expired credentials, role changes and requests to use an unapproved API.
- Exercise controls. Try to bypass approval dialogs, monitoring, rate limits, content filters and emergency stops. Verify that a denied action remains denied after retries or a changed prompt.
- Conduct a red-team review. Have people who did not build the workflow search for paths to data leakage, privilege escalation, goal manipulation and unsafe completion.
Keep safety scores distinct from task-quality scores. Combining them into one number can allow a high volume of correct, low-risk tasks to mask a single catastrophic behavior.
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What evidence should a vendor provide?
Ask for evidence that can be matched to your task specification, not a generic “responsible AI” statement. Require enough detail to reproduce the relevant evaluation or understand why reproduction is impossible.
- Model, agent, prompt, tool and system versions, including release dates and dependencies.
- Evaluation scope, datasets, scenario construction, sampling method, best-of-N procedure and human-review protocol.
- Results for normal, edge, adversarial, privacy, security, tool-use and recovery tests, with uncertainty or limitations.
- Known failure modes, excluded cases, residual risks and the conditions under which results do not apply.
- Safety and security findings, red-team summaries, incident history and remediation status.
- Change-management policy for model, prompt, retrieval, tool, policy and permission updates.
- Logging fields, retention options, access controls, alerting and post-deployment monitoring support.
- Procedures for incident notification, emergency suspension, rollback, data deletion and replacement.
Align provider evidence with your own acceptance criteria and obtain an independent review when the agent can affect health, finances, safety, legal rights, security controls or large populations. Missing evidence is itself a risk signal; it should reduce the initial scope rather than be filled with assumptions.
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When must a human approve an agent’s action?
Set the approval boundary in policy before deployment. The agent must not decide for itself which actions deserve approval.
Require a human confirmation for deletion, financial commitments, external communications, production changes, security-control changes, network egress, permission changes and any action that is difficult to reverse. The approval screen should show the proposed action, affected records or recipients, evidence used, uncertainty, tool arguments and a clear cancel option.
Human-in-the-loop is not a magic safety label. Test whether reviewers receive enough context, can detect a wrong recommendation, have time to intervene and can stop execution after a partial failure. Track overrides, approvals later reversed and approvals made without opening the supporting evidence. If reviewers become a rubber stamp, reduce autonomy or redesign the workflow.
How should a reliable agent be deployed?
Use progressive deployment: start with low-risk work and expand only after predefined gates are met.
- Observe only. Let the agent analyze or draft without write access. Compare its proposed actions with expert decisions and collect failure examples.
- Constrain the environment. Isolate it in a sandbox or separate tenant; use synthetic or least-sensitive data; restrict APIs, destinations, rate, context and runtime.
- Add supervised execution. Permit narrow, reversible actions with explicit human approval and a complete audit trail.
- Run a limited pilot. Select a small user group and workload. Define stop thresholds for unsafe actions, security alerts, latency, cost and escalation quality.
- Expand by capability. Grant one additional tool, data class or action only after the relevant scenarios pass again.
- Keep a practical escape route. Maintain rollback, fail-safe defaults, graceful degradation, an emergency pause and a documented owner who can invoke them.
Do not treat a successful pilot as permanent certification. New data, tools, policies, permissions, model versions or operating conditions can change behavior.
What should be monitored after deployment?
Continuous monitoring turns reliability from a one-time claim into an operating discipline. Log enough context to reconstruct an incident while applying privacy and access controls to the logs themselves.
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- Prompts, retrieved sources, tool calls, arguments, responses, retries and final actions.
- Memory writes and reads, active goals, plans, decisions, identity and privilege changes.
- Approvals, denials, overrides, escalations, blocked actions and emergency pauses.
- Source checks, policy decisions, alerts, latency, rate limits, resource use and partial failures.
- Model, prompt, tool, data, policy and environment versions for each run.
Use anomaly detection for unusual destinations, volume, action sequences, privilege changes, repeated failures and attempts to bypass controls. Compare the agent’s active goals with the approved task specification to identify drift, impersonation or misconfiguration. Establish an incident process that names the responder, preserves evidence, contains access, informs affected parties when required, and feeds lessons back into the test set.
NIST measurement guidance says AI systems should be tested before deployment and regularly while operating. UK guidance recommends scaling evaluation intensity to capability, novelty, domain and affected population. In practice, a model update and a new production integration deserve more testing than a cosmetic interface change, but both should be recorded and assessed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I compare two agents for the same job?
Compare them in your target environment with the same scenarios, permissions, workload and acceptance thresholds. A feature checklist or vendor benchmark is not a substitute for an apples-to-apples risk comparison.
| Comparison axis | Questions to ask |
|---|---|
| Task performance | Does it complete the intended work accurately with your data, tools and edge cases? |
| Failure severity | When it fails, what is the worst outcome, and is the action reversible? |
| Robustness | How does it handle ambiguity, adversarial inputs, changing context and long tasks? |
| Tool and permission control | Can you enforce least privilege, argument validation, rate limits and destination allow-lists? |
| Transparency | Are sources, uncertainty, plans, tool calls and decisions visible enough to audit? |
| Human approval | Can policy require approval for specific actions, and can a person pause execution? |
| Monitoring and response | Are logs, alerts, incident workflows and emergency suspension available? |
| Data protection | Where does data travel, persist and appear in memory or logs, and who can access it? |
| Realistic performance | Does latency, resource use and rate-limit behavior remain acceptable at expected load? |
| Change management | Will you be notified of changes, and can you repeat tests before accepting them? |
| Rollback or replacement | Can you disable the agent, restore state and continue manually if it fails? |
Select the agent with the lower residual risk for the intended use, not automatically the one with the highest average task score. If neither meets a high-impact threshold, narrow the use case or keep the work manual.
What is a defensible adoption decision?
Approve adoption only when the evidence answers five questions: Does the agent perform the defined job? Does it remain within security and permission boundaries? Are uncertainty, escalation and human approval effective? Can failures be detected and contained? Can the organization pause, roll back or replace it without losing control?
Document the scope, test configurations, results, exceptions, owners, approval policy, monitoring plan, review date and conditions that trigger reevaluation. Re-run the relevant tests after any model, prompt, tool, data, policy, permission or environment change, and at regular intervals during production. Expand access only when the updated evidence shows that residual risk remains acceptable and recovery is practical.
Frequently Asked Questions
Can a high benchmark score prove that an AI agent is reliable?
No. Benchmarks usually cover a narrow task distribution and do not show how the agent behaves with your permissions, APIs, data quality, prompt injections or operational outages. Treat benchmark results as one input, then require scenario-based testing in the intended environment.
How can a small team evaluate an agent without a large test lab?
Start with a read-only or draft-only pilot using synthetic or de-identified data and a short list of high-value scenarios. Capture every failure, add those cases to a regression set, and expand tools or data only after a responsible owner reviews the evidence.
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What should we do if a vendor will not disclose evaluation details?
Record the missing methodology, scope and limitations as an evidence gap rather than assuming the results are favorable. Restrict the agent to low-impact tasks, require independent testing where feasible, and do not grant sensitive data or irreversible tool access until your acceptance criteria can be verified.
Does human approval make every agent action safe?
No. Approval can fail when reviewers lack context, misunderstand uncertainty or approve automatically under time pressure. Test the approval interface and reviewer behavior, show the proposed tool arguments and evidence, and retain an emergency pause that works after approval.
When should an agent be removed instead of tuned?
Remove or replace it when it repeatedly violates a hard safety boundary, cannot provide the logs needed to investigate incidents, or lacks a practical rollback path. A lower-performing system that is observable and controllable may be safer than a more capable system whose failures cannot be contained.
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