AI & Agent Evaluation
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what is AI eval / first principles

What is AI eval?

A practical map of how teams test AI and agent systems: what to measure, tasks, execution traces, grading, metrics, and feedback loops.

AI eval, or AI evaluation, is the practice of testing LLMs, AI agents, and GenAI applications for correctness, reliability, safety, tool use, and production behavior. This site collects practical notes on agent benchmarks, graders, traces, production monitoring, and reliability testing for AI systems.

$ evals.map --intro
purpose: orient visitors
focus: agent systems
format: visual concepts
evaluation map / system view

AI eval as a five-component system

A compact map of how AI systems are evaluated from design-time goals to production feedback.

map. evaluation system
1. What to measure2. Tasks and scenarios3. Execution environment and traces4. Offline judgment and grading5. Online feedback loop
component 01 / model · system · product

What to measure

Define the properties the eval should estimate across the model, runtime system, and product experience. A metric matters only if it maps to a concrete product or deployment decision.

key dimensions
Modelcapability · reliability · safety
Systemlatency · cost · tool/retrieval quality
Productusefulness · trust · business impact
component 02 / realistic usage distribution

Tasks and scenarios

Design tasks that reflect real usage, workflow complexity, and likely failure modes. Good scenarios cover both normal demand and the edge cases that break systems.

key dimensions
Structuresingle-step · multi-turn · long-horizon
Sourcesbenchmarks · held-out data · production replay
Riskadversarial · drift · stress cases
component 03 / runtime and observability

Execution environment and traces

Run the model inside the production-like stack and capture enough execution evidence to debug outcomes, attribute failures, and compare behavior across versions.

key dimensions
Interfacestools · APIs · browser/terminal
Contextretrieval · memory · compaction
Tracelogs · timing · state transitions
component 04 / success · metrics · interpretation

Offline judgment and grading

Judge attempts offline and turn runs into interpretable signals. Combine objective checks with calibrated evaluators and metrics that expose quality, uncertainty, regressions, and cost.

key dimensions
Checksunit tests · state validation · schemas
Judgesrubrics · LLM judge · human review
Signalspass@k · severity · CI · win rate
component 05 / intent · telemetry · value

Online feedback loop

Learn from real usage: whether the system understood intent, created quality outcomes, produced durable value, and improved through experiments and telemetry. Online evidence and offline grading calibrate each other.

key dimensions
Intentrequests · corrections · abandonment
Valuequality · satisfaction · long-term lift
Telemetryclient/server events · A/B tests
evaluation paradigms / what changed

Traditional ML eval vs modern GenAI application eval

Traditional ML eval usually measures a single prediction against known ground truth. Modern GenAI and agent evals still use that discipline, but expand the unit of evaluation from one output to a whole system trajectory.

Traditional ML evaluation versus modern GenAI and agent evaluation Traditional ML flows from input to prediction to ground-truth comparison. Modern GenAI and agents flow from goal through reasoning, tools, environment interaction, final artifact, and rubric or ground-truth evaluation. traditional ML single prediction eval modern GenAI / agents system + trajectory eval input prediction ground truthcomparison goal iterativereasoning tools environmentinteraction final artifactor completed task rubric+ tests overlap remainslabels · tests · metrics

Traditional ML eval

Often looks like: input → single prediction → compare with ground truth. The core object is a function, f(x) → ŷ.

  • Best for well-defined tasks and stable labels.
  • Traditional metrics include accuracy, precision/recall, F1, AUC, RMSE, log loss, NDCG, and others.
  • The lifecycle is often framed as training and testing.

Modern GenAI and agent eval

More often looks like: goal → reasoning → tools → environment → artifact → evaluation. The object is the behavior of a system over a trajectory.

  • Coding agents can be evaluated with execution success, tests, and task completion.
  • Image/video and other generative systems often need rubrics, preference data, and virtual judges.
  • Open-ended systems must also be useful, safe, robust, and reliable in interactive environments.
Example: Alignment shows the shift. Traditional ML mostly assumes a defined target. Modern AI systems need to behave consistently with human intentions, values, or product objectives. Today this is often introduced during post-training through RLHF, preference optimization, or system prompts, but robust safety alignment may need to move earlier into pretraining data and system design.