How to use this checklist (and why it’s different)

This is not a generic overview of metrics or a high-level “framework.” It’s a build sheet you can follow in order. Each section includes a Definition, Why it matters, and a Checklist you can turn into tickets.

Intended outcome: you can answer, with evidence, “Is the agent better than last week?” and “Is it safe to deploy this change?”

• Personalization: adapt the checklist to your agent type (support, sales, recruiting, ops automation, etc.).
• Value prop: fewer incidents, faster iteration, clearer ROI.
• Niche: agent evaluation (not just single-turn LLM responses).
• Your goal: ship improvements without breaking production behavior.
• Your value prop: a repeatable agent evaluation framework you can automate.

Checklist 1: Define the agent’s job in measurable terms

Definition: Convert “the agent should be helpful” into a small set of measurable outcomes tied to business value and user experience.

Why it matters: If the job isn’t explicit, you’ll optimize the wrong things (e.g., verbosity) while missing the real failure modes (e.g., wrong actions, missed tool calls, policy violations).

• Write the agent’s primary objective in one sentence (e.g., “Resolve billing issues end-to-end without human escalation”).
• List 3–5 critical user journeys (happy path + common edge cases).
• Define success criteria per journey (e.g., “refund initiated,” “meeting booked,” “shortlist produced”).
• Define non-goals (what the agent must not do).
• Identify tooling boundaries: which tools can be called, which require confirmation, which are prohibited.
• Decide the evaluation unit: single turn, multi-turn conversation, or full task episode with tool calls.

Quick framework: Outcome + Constraints + Evidence

• Outcome: what gets done (task completion).
• Constraints: safety, policy, brand voice, compliance.
• Evidence: what artifacts prove it (tool logs, final message, CRM update).

Checklist 2: Choose evaluation dimensions (scorecard design)

Definition: A scorecard is the set of dimensions you grade on every test case.

Why it matters: Agents can “look good” in text while failing operationally. Your scorecard must include both language quality and action correctness.

• Task success: Did the agent complete the job? (binary + partial credit rubric)
• Tool correctness: Right tool, right arguments, right sequence, no hallucinated tools
• Policy & safety: PII handling, refusal behavior, restricted content
• Grounding: Uses provided context/data; avoids unsupported claims
• Conversation control: Asks for missing info; confirms before irreversible actions
• User experience: Clarity, brevity, tone, next steps
• Efficiency: Turns to completion, tool calls per completion, latency budget

Rubric tip: mix binary gates with graded scores

Use binary “must-pass” gates for safety and tool correctness (e.g., “No PII leakage”), and 1–5 rubrics for UX dimensions. This prevents high average scores from masking critical failures.

Checklist 3: Build a representative test set (not just prompts)

Definition: A test set is a curated collection of tasks, conversations, and tool contexts that represent real usage.

Why it matters: Agents fail on distribution shift: messy inputs, partial context, ambiguous intent, and tool errors. Your evaluation set must include those realities.

• Start with 50–150 cases for a v1 evaluation suite; expand over time.
• Include at least:

• Happy paths (30–40%)
• Common failures from production logs (30–40%)
• Edge cases (10–20%)
• Adversarial / jailbreak attempts relevant to your domain (5–10%)

• For each case, store inputs + context: user message(s), system/tool instructions, retrieved docs, CRM records, etc.
• Version your dataset: eval_set_v1.0, v1.1 (additions only), and track coverage tags.
• Tag each case by journey, difficulty, risk level, and tools involved.
• Add tool failure simulations: timeouts, empty results, permission denied.

Checklist 4: Decide how you will score (human, model, or hybrid)

Definition: Scoring is the method used to assign pass/fail and rubric values for each dimension.

Why it matters: If your scoring isn’t consistent, you’ll chase noise. If it isn’t scalable, you’ll stop evaluating once the sprint gets busy.

• Pick a primary scoring approach:

• Human-only: highest fidelity, slowest
• LLM-judge: scalable, needs calibration
• Hybrid: LLM-judge for most, humans audit a sample + high-risk cases

• Create golden examples (3–5 per dimension) showing what a 1 vs 3 vs 5 looks like.
• Define judge prompt inputs explicitly: conversation transcript, tool logs, ground-truth data, policy rules.
• Run a calibration round: 30 cases scored by 2 humans + judge; measure agreement and adjust rubric.
• Set an audit rate (e.g., 10–20% human review weekly; 100% for safety-critical flows).

Checklist 5: Add regression gates (what blocks a deploy)

Definition: Regression gates are thresholds that must be met before shipping changes to prompts, tools, models, or routing logic.

Why it matters: Agents are systems: small changes can break tool calling, safety behavior, or long-horizon planning. Gates turn “hope” into a release process.

• Define hard blockers (deploy fails if any occur):

• Safety/policy violation rate increases beyond a tight threshold (often 0 tolerance for certain categories).
• Tool correctness drops below baseline on critical journeys.
• High-risk cases fail (e.g., payments, account access, medical/legal domains).

• Define soft gates (deploy allowed with sign-off):

• UX score dips slightly but task success improves.
• Latency increases within budget.

• Track baseline vs candidate with confidence intervals where possible (or at least sample sizes and deltas).
• Require diff reports: show which cases changed outcome and why.

Checklist 6: Instrument the agent so evaluation matches reality

Definition: Instrumentation is the structured logging of traces, tool calls, retrieved context, and decisions.

Why it matters: Without traces, you can’t debug failures or validate that the agent used the right evidence. Evaluation becomes guesswork.

• Log conversation transcript with message roles and timestamps.
• Log tool calls: tool name, arguments, response payload, errors, retries.
• Log retrieval context: top-k docs, scores, snippets shown to the model.
• Log routing decisions: which agent/prompt/model was selected and why.
• Attach case IDs so production incidents can be converted into new eval cases.
• Redact or tokenize PII at ingestion; store sensitive fields separately with access controls.

Case study: recruiting intake agent (same-day shortlist) in 21 days

This example shows how a checklist-driven agent evaluation framework translates into measurable outcomes. Scenario: a recruiting team uses an agent to run candidate intake, score resumes, and produce a same-day shortlist for hiring managers.

• Baseline problem: inconsistent scoring, missed must-have requirements, and frequent human rework.
• Goal: reduce time-to-shortlist while keeping quality and compliance high.

Timeline and implementation

• Days 1–3: Defined scorecard (task success, requirement coverage, hallucination rate, PII handling, explanation quality). Built v1 test set of 80 cases from past roles (mix of seniority, skills, tricky resumes).
• Days 4–7: Calibrated hybrid scoring: LLM-judge for all cases + human audit of 20%. Added golden examples for “meets requirements” vs “appears to meet.”
• Days 8–14: Iterated prompts + tool schema for structured output (skills match table, must-have flags, confidence). Added regression gates: 0 PII leaks, >95% must-have detection on high-risk cases.
• Days 15–21: Rolled out to 30% of roles with monitoring; converted production misses into +18 new eval cases.

Results (measured)

• Time-to-shortlist: reduced from 2.4 days to 0.9 days (median).
• Human rework rate: dropped from 38% of shortlists to 14%.
• Must-have requirement misses: decreased from 17% to 4% on the eval suite.
• Compliance: maintained 0 PII leakage incidents in audited outputs.

What made the difference: the team treated evaluation as a release gate, not a one-time benchmark. Every production failure became a tagged test case, which steadily increased coverage.

Checklist 7: Map the checklist to your vertical (templates you can copy)

Agent evaluation isn’t one-size-fits-all. Below are concrete “what to test” lists for common operating models. Use these to seed your test set and scorecard tags.

Marketing agencies: TikTok ecom meetings playbook

• Lead qualification accuracy (budget, niche, spend, offer maturity)
• Calendar tool correctness (time zones, double-book prevention)
• Policy: no fabricated case studies, no false performance claims
• Conversation control: handles objections, asks for missing info
• Outcome: booked call with correct notes pushed to CRM

SaaS: activation + trial-to-paid automation

• Activation guidance correctness (product steps, permissions)
• Event-based triggers: sends the right nudge after inactivity
• Tool calls: billing changes require confirmation
• Outcome: trial users reach activation milestone; paid conversion intent captured

E-commerce: UGC + cart recovery

• Discount policy compliance and guardrails
• Cart context usage (items, sizes, shipping constraints)
• UGC requests: consent language, brand voice, correct incentives
• Outcome: recovered carts without margin-killing offers

Agencies: pipeline fill and booked calls

• Routing quality (ICP match, territory, urgency)
• Data hygiene: correct enrichment vs hallucinated firmographics
• Outcome: qualified meetings with complete notes and next steps

Professional services: DSO/admin reduction via automation

• Invoice/AR workflow correctness (no unauthorized changes)
• Tool errors: retries, escalation rules, audit trails
• Outcome: fewer touches per invoice; reduced days sales outstanding

Real estate/local services: speed-to-lead routing

• Response time and routing correctness (zip code, service area)
• Scheduling tool correctness; lead source attribution
• Outcome: higher contact rate and booked appointments

Creators/education: nurture → webinar → close

• Personalization grounded in user history (no invented details)
• Objection handling and CTA timing
• Outcome: webinar attendance and qualified sales calls

Checklist 8: Create an improvement loop (weekly operating cadence)

Definition: The improvement loop is the process that turns evaluation results into prioritized changes and verified gains.

Why it matters: Evaluation without cadence becomes a dashboard nobody checks. Cadence turns it into compounding quality.

• Weekly: review top failing cases by impact and frequency.
• Weekly: add 5–20 new cases from production (tagged and deduped).
• Per change: run eval suite baseline vs candidate; generate diff report.
• Monthly: refresh golden examples and re-calibrate judge prompts.
• Quarterly: prune outdated cases; add new journeys after product changes.

FAQ: agent evaluation framework checklist

How many test cases do I need to start?

Start with 50–150 well-tagged cases that cover your top journeys and known failures. Expand continuously by converting production issues into new cases.

Should I use an LLM as a judge?

Often yes, but use a hybrid: LLM-judge for scale, plus human audits for calibration and high-risk categories (safety, compliance, payments, account access).

What should block a deploy?

Block on safety/policy regressions, tool correctness drops on critical journeys, and failures on high-risk cases. Everything else can be a soft gate with explicit sign-off.

How do I prevent “teaching to the test”?

Keep a small holdout set (not used for iteration), rotate in new production-derived cases weekly, and track performance by tags (journey, tool, risk) so improvements generalize.

What’s the difference between evaluating an agent vs a prompt?

Agents require multi-step evaluation: tool calls, state, memory, routing, and recovery from errors. Your framework must score both the final answer and the operational trace.

CTA: turn this checklist into a repeatable system

If you want this checklist implemented as an automated workflow—versioned eval sets, scorecards, regression gates, and diff reports—Evalvista helps teams build, test, benchmark, and optimize AI agents using a repeatable agent evaluation framework.

Next step: map your top 3 user journeys and we’ll help you turn them into a v1 evaluation suite you can run on every change.

Talk to Evalvista to set up your first agent evaluation run.