AI Agent Evaluation Plan Template

An AI agent evaluation plan turns a broad question such as “Is this agent good enough?” into scoped claims, named measures, controlled evidence, and decision rules that a reviewer can inspect.

The plan sits above individual tests. A test run produces an observation about one case and configuration. The evaluation plan explains why that observation matters, how representative it is, how it combines with other evidence, which failures cannot be averaged away, and which decision the result can inform.

Download the AI agent evaluation plan template (Markdown)

The download contains 12 working sections plus reusable registers for claims, dimensions, datasets, graders, thresholds, results, and changes.

TL;DR

A sound evaluation plan should:

1. Name the exact decision and the claims the evidence may support.
2. Freeze the agent, model, instructions, context, tools, permissions, approvals, runtime, and evidence configuration.
3. Define the task population before selecting a convenient test set.
4. Separate quality, reliability, safety, security, authority, human-work, cost, and audit measures.
5. Version datasets and graders, then control contamination, leakage, and conflicts.
6. Precommit samples, repetitions, denominators, uncertainty methods, and stop rules.
7. Keep mandatory gates and prohibited outcomes outside aggregate scores.
8. Count failed, blocked, aborted, invalid, not-run, retried, and manually corrected work.
9. Preserve raw evidence separately from analysis and bind both to stable identifiers.
10. Connect pre-release evaluation to production sampling, regression, refresh, and retirement.

The evaluation plan does not grant runtime authority or approve production. It produces bounded evidence for an authorized decision maker.

What Is an AI Agent Evaluation Plan Template?

An AI agent evaluation plan template is a reusable structure for deciding what to evaluate, how to measure it, which evidence to trust, and how to interpret the result for one agent workflow.

It answers questions that a list of test cases cannot answer on its own:

• Which product, risk, or governance decision is this evaluation meant to inform?
• What population of users, tasks, languages, data, tools, and operating conditions does the claim cover?
• Which dimensions matter, and which failures block a positive result regardless of the average?
• Which datasets represent the intended population, and where are their blind spots?
• Which graders are fit for each measure, and how will the team check their error?
• How many observations count, and how will the team treat retries, missing evidence, and uncertainty?
• What changes invalidate the result or require a new evaluation?

NIST’s AI RMF Measure function calls for documented metrics, test sets, tools, deployment-like conditions, safety, security, resilience, limitations, and production monitoring. NIST’s TEVV-Athlon initial public draft proposes a configurable process for assessing AI systems, including agentic systems. Those sources support a plan tied to a real decision and operating context, not a benchmark score copied into a release memo.

Evaluation Plan, Test Plan, Assurance, and Release Decision

These records connect, but they do different work.

• A requirements document states what the workflow must do, must not do, and must prove.
• An AI agent test plan defines executable cases, environments, expected behavior, run rules, and evidence.
• An AI agent evaluation judges selected evidence against defined criteria.
• An evaluation plan defines those criteria, evidence sources, measures, graders, thresholds, and interpretation rules before the result is known.
• Assurance combines applicable evidence to decide whether a scoped claim is supported.
• A release decision records whether an authorized owner accepts the evidence, limits, and residual risk for a named release.

One evaluation may use several test suites, production samples, incident records, human reviews, security assessments, and outcome checks. A passing suite does not establish a broad claim if the cases omit a material population or if the grader rewards the wrong behavior.

Keep stable links among records. A claim should link to its evaluation questions and measures. Each measure should identify its data, grader, denominator, and threshold. Each result should identify the exact system and evidence versions. The final decision should identify the result set, open gaps, conditions, owner, and expiry.

How to Use This AI Agent Evaluation Plan Template

Start after the team has a bounded workflow, named owner, requirements baseline, risk assessment, and a system configuration worth evaluating. Record missing inputs instead of inventing scope inside the evaluation.

The downloadable template has 12 sections:

1. Decision, claims, and evaluation questions
2. System, configuration, and operating scope
3. Stakeholders, owners, and review independence
4. Population, task taxonomy, and coverage
5. Datasets, fixtures, provenance, and contamination control
6. Evaluation dimensions and metric definitions
7. Graders, calibration, and disagreement
8. Trial design, sampling, denominators, and uncertainty
9. Security, authority, failure, and human-work evaluation
10. Execution, evidence, and result accounting
11. Threshold hierarchy and decision rules
12. Production monitoring, regression, refresh, and retirement

Treat the approved plan as a versioned record. A status such as “approved” does not authorize execution against a system, user, tenant, or dataset. Record the separate authenticated execution decision, then check current technical authorization for every operation.

1. Start With the Decision and Scoped Claims

Write the decision first. “Evaluate the support agent” is an activity. “Decide whether release R-24 may enter a two-week internal pilot for English-language billing drafts under $500, with human approval before send” defines a boundary.

Then state claims that can be tested. For example:

• The agent completes supported billing-draft tasks within the named quality and latency thresholds.
• The approval control blocks every enumerated external-send attempt without a valid approval.
• The agent does not read records outside the initiating synthetic tenant in the cross-tenant suite.
• The context bundle includes the approved billing policy and excludes archived guidance.

Add non-claims. If the evaluation excludes other languages, autonomous sends, weekend operations, or accounts over $500, say so. A result should not travel beyond the population and conditions represented by its evidence.

Turn each claim into evaluation questions. A completion claim may require separate questions about task success, correction, latency, and outcome verification. A context claim may require questions about compiled versions, trusted host delivery, truncation, priority, and excluded content.

2. Freeze the System and Operating Scope

An agent configuration includes more than code. Record stable versions or digests for:

• Agent definition and orchestration
• Model deployment and settings
• System and developer instructions
• Published Knowledge and Skills
• Prepared Memory state
• Context routes and retrieval indexes
• Tools, schemas, and integrations
• Identity, permissions, limits, and approvals
• Runtime, network, persistence, and sandbox rules
• Monitoring, evidence, and grader configuration

State the users, tenants, tasks, data classes, languages, tools, effects, and dependency conditions in scope. Confirm the configuration before each run batch because a model alias, tool schema, route, grader prompt, or retrieval index can change the evaluation subject.

For Alignbase-managed context, keep repository permission separate from delivery. Permissions govern repository access, including who can discover, read, or change a Resource. Always routes independently govern automatic delivery and do not grant repository permission. Artifacts and messages have no instruction authority, Memory is working recall, and MCP tool results are Runtime context.

3. Define the Population Before the Dataset

The target population is the work the claim covers. Define it before selecting cases, or the available dataset will quietly become the scope.

Build a task taxonomy using dimensions that can change behavior or harm:

• User or initiating workflow
• Tenant and data class
• Task family and difficulty
• Language, locale, and accessibility need
• Tool and permission path
• Approval state
• Dependency and failure state
• Consequence and reversibility
• Common, rare, boundary, adversarial, and prior-failure cases

Record expected prevalence when known. A balanced benchmark can expose rare failures, but it does not estimate production frequency unless the weighting matches production. Report both controlled challenge-set results and population-weighted results when the decision needs both.

Coverage is more than a case count. Show which cells are represented, which are missing, why they are missing, and how each gap limits the decision.

4. Govern Datasets and Fixtures

For each dataset, record its purpose, owner, source, inclusion rules, exclusions, provenance, version, license or use basis, sensitive fields, retention, and known limits. Keep an immutable snapshot for the evaluation and a change record for later versions.

Use synthetic or masked data by default. When governed production data is necessary, minimize fields and records, use scoped identities, block unapproved external egress, set retention and deletion rules, and stop if data escapes the approved boundary. Never place credentials, authorization headers, secret values, unnecessary personal data, unrestricted customer records, or private model reasoning in fixtures or evidence.

Protect hidden cases, expected answers, grader rubrics, and solution keys from the system being evaluated. Keep them outside its retrieval sources, working directories, tools, logs, and Memory. Track overlap with training, tuning, prompt development, and prior debugging material. If contamination is possible, label the affected result instead of presenting it as a clean estimate.

Use dedicated synthetic tenant pairs for cross-tenant tests. Never probe an uninvolved tenant, user, account, or external system.

5. Define Dimensions and Measures

Avoid one undefined “quality” score. Create a measure register with a name, dimension, definition, unit, population, numerator, denominator, direction, threshold, evidence source, owner, and known limits.

Typical dimensions include:

• Task completion and verified business outcome
• Factual and procedural correctness
• Context selection, freshness, authority, and exclusion
• Tool choice, parameters, side effects, and reconciliation
• Policy, authorization, approval, and refusal behavior
• Security, privacy, and tenant isolation
• Reliability, recovery, and repeatability
• Human review load, correction, escalation, and time
• Latency, tokens, compute, and cost per completed task
• Audit completeness and evidence trust

Measure paths as well as outputs. A polished response still fails if the agent read the wrong tenant, called an unapproved tool, skipped approval, or left the target system unchanged.

Define the denominator before execution. “Ninety-eight percent success” is not interpretable if blocked cases, retries, and manually corrected runs disappeared from the count.

6. Version and Calibrate Graders

A grader may be a deterministic rule, target-system check, human reviewer, model-based judge, or combination. Match it to the claim. Use protected target-system state for consequential effects and deterministic checks for schemas, permissions, required fields, and exact policy gates where possible.

For every grader, record its version, inputs, output scale, rubric, calibration set, blinded fields, expected error, and disagreement process. Human review needs training and inter-reviewer checks. Model-based grading needs trusted examples, false-pass and false-fail review, and protection from prompt injection in the material it judges.

NIST’s work on evaluation probes for agentic AI describes structured verdicts tied to source evidence. That can improve traceability, but a grader remains a measurement method. It does not approve the system or prove that the underlying evidence is complete.

Define what happens when graders disagree. Do not choose the favorable score after seeing both. Precommit adjudication, tie handling, reviewer independence, and whether disagreement itself blocks a claim.

7. Plan Samples, Repetitions, and Uncertainty

Model-dependent behavior varies, so one clean run rarely supports a population claim. For every measure, specify:

• Target population and sampling frame
• Sample method and any weights
• Required repetitions per case
• Numerator and denominator rules
• Invalid-run criteria
• Retry and manual-correction treatment
• Confidence or uncertainty method
• Early-stop and harm-stop rules
• Segment and subgroup reporting

Choose sample size from the decision, expected rate, acceptable uncertainty, and failure cost. A rare prohibited action may need targeted adversarial coverage and a zero-tolerance gate even when a statistical estimate would require more observations than the project can run.

Report distributions and tail failures. An average can hide severe errors in one tenant, language, tool, permission path, or affected group. Never let a high score in common low-risk tasks offset a successful cross-tenant read or unauthorized write.

8. Evaluate Security, Authority, and Human Work

Security evaluation should cover direct and indirect prompt injection, Memory poisoning, unsafe retrieved content, tool-result injection, data exposure, privilege escalation, approval replay, confused-deputy behavior, unsafe code or browser actions, and multi-agent propagation. OWASP’s AI Agent Security Cheat Sheet provides a practical list of agent-specific threats and controls.

Treat injected instructions as untrusted data in evidence. Encode them for each log, Markdown, HTML, terminal, or dashboard sink so an evaluation record cannot become a second execution path.

Evaluate authorization at each operation. A refusal in the final response is weak evidence if a tool already completed the prohibited action. Bind approvals to the exact principal, tenant, operation, parameters, target, policy, expiry, and unique nonce, then test missing, denied, expired, altered, and replayed approvals.

Measure human work too. Track review time, correction rate, disagreement, queue age, escalation, and whether the reviewer receives enough information. An agent that appears accurate by sending every difficult case to an unstaffed queue has not met an operational claim.

9. Preserve Evidence and Honest Result Accounting

Create an evaluation run record for each planned unit of work. Count passed, failed, blocked, aborted, invalid, not run, retried, rejected, escalated, and manually corrected outcomes under precommitted rules.

Preserve raw evidence separately from conclusions. Evidence should record the tenant, principal, agent and integration identities, system and plan versions, task and case IDs, dataset and fixture versions, grader version, run identity, time, exact context versions and bundle digest, direct or Group route source and effective delivery mode, authorization outcome and policy version, available runtime references, target-state checks, and provenance. Use access controls, retention rules, redaction, and tamper-evident storage appropriate to the data.

A digest can show that stored bytes differ after an authenticated commitment. It does not prove that capture was complete, accurate, authorized, or tied to the real run. Claims about evidence need authenticated provenance, trusted capture, and completeness checks.

Keep delivery stages distinct. Context compilation, response issuance, integration acknowledgment, host-confirmed injection, and authenticated model-consumption attestation are different evidence. One does not prove the next. Alignbase can record compiled and issued context; it cannot claim host injection or consumption without trusted downstream evidence.

10. Set Threshold Hierarchy and Decision Rules

Define thresholds before running the evaluation. Use a hierarchy:

1. Prohibited outcomes, such as cross-tenant access or unauthorized external action
2. Mandatory control gates, such as approval enforcement and audit creation
3. Dimension thresholds, such as minimum task success and maximum correction rate
4. Segment thresholds for material users, workflows, languages, tools, and risk classes
5. Aggregate scores for summary and comparison

The higher rule wins. An aggregate score cannot cancel a prohibited outcome, mandatory-gate failure, missing evidence, or severe segment failure.

For each rule, define pass, conditional pass, fail, insufficient evidence, and invalid result. Name the authorized decision maker, required independent reviewers, open-defect treatment, expiry, and re-evaluation triggers. The plan author cannot self-approve an exception where policy requires independence.

Do not weaken a threshold, remove a case, change a denominator, or switch graders after seeing results without recording a new plan version and rerunning the affected work. NIST has documented the risk of systems adapting to evaluation conditions in its work on cheating in AI agent evaluations, so protect hidden material and monitor for evaluation-aware behavior.

11. Connect the Plan to Production

Pre-release evaluation samples expected work. Production reveals distribution shifts, new users, new attacks, dependency changes, and long-running effects. Define the bridge before release.

The plan should name:

• Production measures and sampling rates
• Outcome verification sources
• Alert and rollback thresholds
• Owners and response targets
• Privacy, access, retention, and deletion rules
• Comparison baselines and drift checks
• Incident and near-miss intake
• Regression suite promotion rules
• Review cadence and expiry

An AI agent monitoring program should use the same measure definitions where possible. Production failures should become versioned regression cases after safe review. Changes to the model, prompts, Knowledge, Skills, Memory behavior, routes, tools, permissions, approval policy, runtime, grader, or target population should trigger a scoped impact review and, when material, a new evaluation.

Common Evaluation Plan Failures

Watch for these plan defects:

• The decision remains vague, so a narrow result gets used as broad approval.
• The dataset is convenient but does not represent the stated population.
• One score combines quality, security, and authority failures.
• The grader rewards style while missing wrong actions or target state.
• Samples, retries, denominators, or thresholds change after results appear.
• Hidden cases or rubrics leak into the agent’s context.
• Only successful runs remain in the final report.
• Production monitoring uses different definitions, so drift cannot be compared.
• Evidence proves compilation or response issuance but is described as model consumption.
• A passing result is treated as technical authorization or production approval.

The plan should make each of these choices explicit before execution. That is what turns evaluation from a demo into evidence an accountable reviewer can use.

Use the Template as a Living Record

Download the AI agent evaluation plan template, assign stable IDs, and keep it with the exact datasets, graders, results, evidence references, decisions, and change history it governs.

Update the plan when the decision, population, system, measures, data, graders, or operating conditions change. Preserve old versions because later reviews need to know what the team actually planned, measured, and decided at the time.