How to test and evaluate AI agent systems: a practical framework

Defining what “good” looks like for an AI agent is much more than “did the unit test pass?”. It means agreeing on success criteria, measuring both reasoning and action quality, and wiring those measurements into a continuous feedback loop that guides development and deployment.
ibm
+1

1. Define success criteria that actually matter

Start by writing down what “good” means in terms that map to business and user outcomes, not just model scores.
jobsbyculture
+1

Think in layers:

Task-level outcomes

Task completion rate: did the agent actually accomplish the requested task?

Objective accuracy: did it produce the correct answer, action, or change in state?

Constraint adherence: did it respect policies, safety rules, and guardrails?
arize
+1

Experience and efficiency

Latency: how long from user input to final answer or completed workflow?

Cost per task: tokens + tool calls + infrastructure.
jobsbyculture

Interaction quality: clarity, helpfulness, and tone (usually judged by humans or an LLM-as-judge).
cloud.google
+1

Safety and robustness

Hallucination rate: fraction of outputs that are wrong or not grounded in allowed context.
atlan
+1

Policy/compliance violations: security, privacy, regulatory breaches.
ibm

Reliability: rate of timeouts, tool errors, and unrecoverable states.
arize
+1

Write explicit thresholds per dimension (for example: “completion ≥ 90%, hallucination ≤ 2%, latency p95 ≤ 5s, cost ≤ £0.02 per query”) so evals can clearly say “acceptable” vs. “regression”.
jobsbyculture

1. Build evaluation datasets (beyond trivial unit tests)

Unit tests still matter, but they only check narrow, deterministic behaviors. Agents need richer “golden”

-

Rizwan Saleem | https://rizwansaleem.co