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Evaluating AI Agents: Evals for Reliable Production Systems

AISoftware QualityEngineering LeadershipSoftware Architecture

Many teams still evaluate AI agents through individual demos and manual spot checks. That is insufficient once an agent reads customer data, calls tools, or changes business processes: a new model, revised prompt, or different API can shift behaviour, cost, and risk without being noticed.

What Evals for AI Agents Need to Measure

A conventional software test usually checks a deterministic input and output. With AI agents, it also matters how an outcome was reached and whether it remains reliable across repeated runs.

A robust eval set therefore covers several levels:

  • Task outcome: Was the business goal achieved, rather than merely producing a plausible answer?
  • Tool trajectory: Did the agent use the correct APIs with permitted parameters and a sensible sequence?
  • Safety boundaries: Did it respect permissions, data access, approvals, and stopping rules?
  • Consistency: How often does the same task succeed across repeated runs, models, and realistic input variations?
  • Economics: Do latency, token usage, tool cost, and manual rework remain within the product's limits?

Evaluating only the final response can hide risky intermediate actions. Conversely, teams should not force every valid workflow into one reference sequence. The relevant distinction is which steps are required by the business process and which are explicitly prohibited.

Where Teams Should Start With Agent Evaluation

The most common mistake is building a large synthetic benchmark with little connection to the product. An initial set of 20 to 50 real tasks from support cases, manual acceptance tests, and known failures is often enough to begin.

Every task needs explicit success criteria and a stable test environment:

task: "Review contract data and flag discrepancies"
success: "all required fields assessed, no invented values"
required_tools: ["contract_api"]
forbidden_actions: ["write_customer_record"]
limits: { max_seconds: 30, max_cost_eur: 0.20 }
human_review: "required_for_legal_recommendation"

Teams should then combine three types of grader:

  • Code-based checks for schemas, tool calls, permissions, limits, and objectively verifiable outcomes.
  • Model-based graders for open-ended quality criteria, using precise rubrics and regular calibration by domain experts.
  • Human review for samples, edge cases, and decisions with material financial or regulatory risk.

The eval set belongs in the release process. Changes to models, prompts, tools, retrieval, or routing should only ship when defined quality, safety, and cost thresholds remain satisfied. New production failures then become new test cases.

Why This Matters

Without evals, the quality of an AI agent remains an opinion. Teams debate impressions from demos while regressions only become visible through support cases, incorrect transactions, or unexpected cloud cost.

Repeatable evals turn agent development into a controllable product process. Product leaders can define quality boundaries, engineering can compare changes, and compliance teams can see which controls were actually tested before release.

For growing software companies, this discipline determines whether an agent merely looks impressive or delivers value reliably. An Architecture & AI Review can assess whether evaluation data, system boundaries, and release gates are sufficient for production use.