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Debby McKinney
Debby McKinney

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Agent Evaluation vs Model Evaluation: What Devs Get Wrong

#ai #aiops #programming #discuss

You can benchmark a model to death and still ship an unreliable agent. Why? Because models and agents are not the same thing. Models predict tokens. Agents make choices. If you judge an agent like a model, you will miss the failure that hits production at 3 a.m.

Let’s fix that. Here is a clean, verifiable breakdown of agent evaluation vs. model evaluation, what most teams miss, and how to stand up a workflow that catches real problems before users do.

Quick References

The Core Difference in One Minute

  • Model evaluation checks the raw model. You score outputs against a labeled set. Think accuracy, BLEU, toxicity flags, latency, and cost. Useful, but narrow.
  • Agent evaluation checks the whole system. Multi-turn decisions, tool calls, retrieval, retries, guardrails, and fallbacks. You care about task success, groundedness with your data, escalation behavior, safety, and whether the agent stayed inside policy.

If you only test the model, you get pretty scores and ugly incidents. If you test the agent, you get predictable behavior under real conditions.

What Devs Get Wrong

  • They treat a 95% offline score like a production guarantee. It's not.
  • They test single prompts and ignore tools, APIs, and retrieval. That's not how users behave.
  • They track session success and skip node-level checks. Then they can't find the real failure.
  • They log everything and alert on nothing. No one reads 10,000 traces after an outage.
  • They run LLM as a judge without guardrails, so their score drifts with model updates.

Fixing this isn't hard. It just needs a better plan.

The Two-Layer Model: Session and Node

You need both layers. One to answer "did we get the job done?" and one to answer "where did it go wrong?"

  • Session-level metrics
    • Task success. Did the agent solve the user’s problem?
    • Escalation correctness. Did it escalate when it should?
    • Satisfaction proxy. Rating, thumbs, or intent to recontact.
    • Cost and latency. End-to-end numbers users feel.
  • Node-level metrics
    • Tool success. Did the API call produce the required state?
    • Retrieval groundedness. Did the answer cite the right facts?
    • Policy compliance. No PII leaks, no unsafe steps, no forbidden calls.
    • Reason step checks. If the plan said "search, then summarize," did it actually search?

Session tells you the outcome. Node tells you the fix.

Deep dives and examples:

A Simple Evaluation Workflow that Works

  1. Start from real traces
    • Log production runs. Sample a slice into a dataset. Label outcomes and key attributes.
    • Turn those traces into your first eval set. Keep it small and honest.
  2. Define 6 to 10 metrics that matter
    • Session: task success, escalation correctness, user rating.
    • Node: tool success, groundedness, safety violations, step latency.
    • Add cost per session and per node.
  3. Mix evaluators
    • Auto checks for deterministics like tool return codes, policy regex, cost budgets.
    • LLM as a judge for relevance and faithfulness. Use fixed prompts, fixed models, and anchors to reduce drift.
    • Human review for high-risk flows and a small weekly sample.
  4. Simulate the real world before rollout
    • Multi-turn flows with tools, flaky APIs, rate limits, long contexts.
    • Validate against your metrics. Find the breakage in staging, not in prod.
  5. Wire it to CI and alerts
    • Evals run on PRs that change prompts, tools, or retrieval.
    • Alerts fire only on the three signals that correlate with user pain.
    • Keep a weekly quality note. Wins, regressions, and next bets.

Templates and patterns:

Concrete Metric Examples You Can Copy

Pick the ones that match your app.

Support Agent

  • Session: resolved without escalation, correct escalation, CSAT proxy.
  • Node: retrieval groundedness, tool success for ticket updates, policy compliance.
  • Ops: p95 latency, cost per ticket.

RAG Search and Answer

  • Session: answer correctness with citations, no hallucination.
  • Node: recall@k on retrieval, citation faithfulness, chunk coverage.
  • Ops: context length, token budget alerts.

Sales Copilot

  • Session: did it draft a compliant email, was the CTA correct.
  • Node: CRM tool success, PII redaction, tone compliance.
  • Ops: p95 run time, compute cost per email.

Metric references and scoring patterns:

The Decision Tree: Agent vs. Model Evaluation

Use this when you are scoping work.

  • Does your user flow involve tools, retrieval, or multiple turns?
    • Yes. You need agent evaluation.
    • No. Model evaluation may be enough for this piece.
  • Is failure about behavior, not just content?
    • Yes. Agent eval with node checks will isolate it.
    • No. Model eval could cover it.
  • Do you owe reliability to a customer or an auditor?
    • Yes. Add simulation, alerts, audit trails, and human loops.
    • No. Keep it lighter, but still measure what matters.

Agent vs. model deep dive:

LLM as Judge, Used Safely

LLM as a judge is powerful and dangerous if you wing it. Make it boring.

  • Fix the judge model and prompt. No silent upgrades.
  • Calibrate with a labeled seed set. Look for judge bias.
  • Use tie breakers. If scores are borderline, send to human review.
  • Watch drift. If the judge’s model changes, rebaseline your scores.

If it is high stakes, keep a human in the loop. If it is routine, automate and spot check weekly.

Observability that Earns Its Keep

Do not hoard logs. Instrument for decisions.

  • Tracing that shows inputs, outputs, tool calls, and intermediate steps.
  • Labels for scenario, user cohort, and experiment flags.
  • Dashboards for task success, groundedness, cost, and latency.
  • Alerts for the three things that predict user pain. Not twenty. Three.

Start here if you are wiring from scratch:

A 30-Day Plan You Can Run Next Sprint

Week 1

  • Capture traces on your top two flows.
  • Build a 50 to 200 example dataset.
  • Define 8 metrics: 3 session, 5 node.

Week 2

  • Stand up auto checks and LLM as judge scorers.
  • Add a 10% human sample.
  • Baseline your scores. Share a one-pager.

Week 3

  • Simulate full flows with tools and retrieval.
  • Fix the top two failure modes.
  • Re-run evals. Track deltas.

Week 4

  • Wire CI gates on prompt and tool changes.
  • Add two alerts: p95 latency and groundedness failure rate.
  • Publish your first weekly quality report. Keep it to one screen.

Workflow help:

Where Maxim Fits

If you want one place to run simulations, evals, prompts, tracing, alerts, and governance, use Maxim. It is built for multi-turn agents, human-in-the-loop when needed, and production observability that doesn't drown you in noise. If you already have parts of the pipeline, you can still use Maxim for the missing pieces.

If You Want to Go Deeper

Bottom Line

  • Evaluating the model tells you if it writes nice sentences.
  • Evaluating the agent tells you if it does the job.

Ship with the second one. Keep the first, but do not mistake it for production truth.

Want the fast lane? Copy the 30-day plan, plug in your flows, and run. If you want it all in one place, bring Maxim in and make quality your default.

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