AI evals for LLM agents explained — tool-call accuracy, trajectory tests, human gates, and fixed-scope harnesses that catch agent failures before production.
If you searched AI evals for LLM agents explained, you’re probably past the chatbot demo stage. You have an agent that calls tools — creates tickets, queries PostgreSQL, posts to Slack, writes draft orders into NetSuite through a REST bridge — and you need to know whether it’s safe to put in front of staff or customers. Evals give you numbers for that. Not vibes.
At Wolverine Solution (Montréal; US and EU delivery), we build AI & LLM Systems for early-stage SaaS founders, wholesale distributors, and multi-location operators: RAG pipelines, agentic workflows with human approval gates, fine-tuning when retrieval isn’t enough, and evaluation harnesses that hold up against real SKUs, SOPs, and exception codes. This post covers what agent evals actually measure, which metrics matter at SMB scale, and why they’re not the same as “run 50 prompts and eyeball the output.”
AI evals for LLM agents are structured tests that score multi-step runs — prompts, tool calls, retrieved context, final outputs — against expected trajectories and outcomes. Single-turn chat evals aren’t enough here. Agent evals check whether the model picked the right tool, passed correct arguments, recovered from errors, and stopped when policy required human approval.
An LLM agent isn’t one API call. It’s a loop: the model reads state, decides an action, invokes a function (search catalog, fetch invoice, open a GitHub issue), reads the result, repeats until it finishes or hits a guardrail. Evals turn that loop into repeatable evidence — pass rates, failure taxonomies, latency and cost per successful task — so you can ship, regress-test after model swaps, and defend go/no-go decisions to ops and compliance stakeholders.
Common building blocks in production stacks:
| Layer | Examples | What agent evals test here |
|---|---|---|
| Orchestration | LangGraph, LangChain agents, Semantic Kernel, custom Python / Node.js runners | Correct routing, max-step limits, stop conditions |
| Models | GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, fine-tuned Llama on Together.ai | Tool-selection accuracy after prompt or model change |
| Tools | REST APIs, SQL, Stripe, Zendesk, Salesforce, ERP connectors | Schema-valid arguments, idempotent writes, read-only boundaries |
| Retrieval (when agents RAG) | pgvector, Pinecone, OpenSearch, Bedrock Knowledge Bases | Whether the agent cited the right doc before acting |
| Scoring | Promptfoo, DeepEval, Ragas, LangSmith evaluators, pytest + LLM-as-judge | Trajectory match, outcome correctness, safety refusals |
| CI / ops | GitHub Actions, Terraform-managed staging, Helicone or Langfuse traces | Regression gates before prod deploy |
[Internal link: agentic workflow development for B2B order exceptions]
Agent failure modes are sequential and transactional. A wrong tool call or bad argument can write bad data even when the final natural-language answer sounds fine. Chat evals score one reply. Agent evals have to score the full action path, side effects, and escalation behavior.
Three failure classes rarely show up in single-turn tests:
create_shipment instead of create_credit_memo.For a wholesale distributor, that might mean an agent that “successfully” routes a chargeback — except it attached the wrong deduction code. For a seed-stage SaaS team, it means a support agent that closes tickets with confident hallucinations because retrieval never ran. Evals catch those paths before a human operator trusts the automation.
When evaluating an LLM agent, measure tool-selection accuracy, argument validity, task completion on golden scenarios, safe refusal rate, cost/latency per successful run, and human-escalation correctness. Weight metrics by blast radius: a read-only FAQ agent tolerates lower tool precision than an agent that posts journal entries.
| Metric | What it captures | Starter threshold (indicative) |
|---|---|---|
| Tool selection accuracy | Did the agent pick the intended function for this scenario? | ≥90% on golden set before limited prod |
| Argument schema pass rate | Valid JSON, required fields, enum values | ≥95% |
| Trajectory match | Ordered steps match expected path (allow benign variants) | ≥85% on critical flows |
| Task success rate | End state matches expected DB row, ticket, or API response | ≥80% v1; raise per domain |
| Harmful action rate | Writes outside scope, exfiltration, policy violations | 0% on adversarial set |
Trajectory evals are the agent equivalent of integration tests. Define expected sequences explicitly:
User: "Hold order SO-4412 — credit limit exceeded"
Expected: get_order → check_credit → create_hold → notify_ar (no shipment create)Promptfoo handles multi-turn scenarios. DeepEval fits pytest if your team already runs CI on Python services. LangSmith helps when you want trace-linked datasets from staging replays.
If the agent has to read policy or catalog data before acting, add RAG metrics from Ragas or DeepEval:
Founders ignore them until finance notices. Ops directors ask on day one:
Build a minimum agent eval harness with 30–50 golden scenarios exported from real tickets or ops emails, expected tool trajectories, automated scoring (LLM-as-judge plus schema validators), and a local runner you can execute before every deploy. Skip building a custom eval platform. Start with files in git and one CI job.
Week 1 — Capture reality
expected_tools and forbidden_tools — not just expected natural language.Week 2 — Automate scoring
notify_slack before or after create_hold is OK).Week 3 — Gate deploys
main or if harmful-action tests fail once.That’s enough for most seed-stage SaaS and regional distributor pilots. You don’t need MLflow on day one. You need a golden set that reflects how AR, inside sales, or support actually phrase requests — messy ones included.
Keep a human in the loop when agent actions are financial, legally binding, or hard to reverse — order holds, credit memos, price overrides, contract language, PII exports. Evals prove the loop works by testing that the agent escalates on ambiguous cases, never skips approval on high-risk categories, and presents diffs clearly in the review UI.
For B2B operators, we implement approval gates as first-class states in the workflow — not a generic “are you sure?” modal. Eval scenarios should assert:
REJECTED human decision in the same session[Internal link: human-in-the-loop approval gates for agentic B2B workflows]
Run these adversarial cases on every model upgrade. Claude and GPT families differ in eagerness to act vs ask.
Fixed-scope agent eval work delivers a repo-native harness, golden dataset, CI gate, and handoff doc — typically over 2–4 weeks when an agent already runs in staging. Standalone eval engagements often sit in a $5k–$12k band; bundled with a full agentic v1, evals are usually a line item inside a broader $35k–$65k fixed build depending on tool count and ERP adjacency.
Deliverables we scope at Wolverine Solution:
If you’re still choosing between RAG and an agent, evals come after you can demo the loop in staging. Evaluating a slide deck is waste.
Public benchmarks score base models on academic or repo tasks in isolation. Agent evals score your orchestration: tools, prompts, retrieval, approval rules. A model that tops a leaderboard can still fail your NetSuite hold workflow if tool descriptions are vague. Run benchmarks for model shortlisting. Run agent evals for ship decisions.
Start with 30–50 golden scenarios covering your highest-blast-radius flows, plus 10–15 adversarial prompts. Enough for a cautious internal pilot. Expand toward 100+ as you add tools or regions. Prioritize cases tied to money, compliance, or customer commitments — not exhaustive paraphrases of the same happy path.
Yes. Promptfoo, DeepEval, and file-based datasets in git work for v1. Add Langfuse (open-source) or Helicone when you need trace replay across many staging runs. The non-negotiable piece is golden scenarios with expected tool paths, not a SaaS dashboard.
No. Evals automate regression detection when prompts, models, or tools change. Humans still validate tone, edge-case judgment, and UX of approval screens. The win: QA stops re-testing the same 40 flows manually after every deploy.
On every PR that touches agent code, prompts, tool schemas, or model routing. Nightly against staging if production traffic is high. Re-run the full suite when you swap providers (OpenAI → Anthropic or vice versa) — tool-calling behavior isn’t portable by default.
If you’re building an agentic workflow — support triage, order exceptions, internal ops copilots — and want a fixed-scope v1 with trajectory tests, CI gates, and human approval where finance requires it, Wolverine Solution scopes AI & LLM Systems work for SMB and seed-stage teams in the US and EU. We ship the harness in your repo, tuned to your tools and escalation rules, alongside the same fixed-scope discipline we bring to React / Next.js portals, React Native field apps, and Terraform on AWS / GCP.
Book a scoped AI agent + eval discovery call →