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AI quality engineering

AI Agent Testing Framework

A practical framework for AI agent testing that separates deterministic validation, semantic evaluation, trajectory integrity, security and release evidence.

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A useful AI testing framework needs to define what must be true, how the system will be observed, which judgments are deterministic or semantic, how execution integrity is proven and how failures affect release decisions.

01

Define test intent

Start from a business goal, persona, behavioural playbook, invariants and evidence requirements instead of a brittle transcript.

  • Goals and personas
  • Behavioural playbooks
  • Business invariants
02

Separate evaluation layers

Keep exact facts, semantic judgments, trajectory integrity and security controls explicit so a strong score cannot hide a critical failure.

  • Deterministic
  • Semantic
  • Trajectory and security
03

Build an evidence graph

Link the test specification, agent build, environment, conversation, trace, assertions, evaluations and security findings to the final verdict.

  • Traceability
  • Reproducibility
  • Audit-ready evidence
04

Operationalize release gates

Turn the framework into repeatable engineering workflows with CI/CD integration, exceptions and governed release policy.

  • Release candidate scope
  • Gate semantics
  • GO / NO-GO / REVIEW

Related Shyena capabilities

Evaluation modelAI agent testingRelease evidenceDocumentation

Frequently asked questions

What should an AI agent testing framework contain?

At minimum: test intent, deterministic assertions, semantic evaluation where appropriate, execution-integrity checks, security controls, evidence traceability and release-gate semantics.

Why separate deterministic and semantic testing?

Exact business and system facts should be verified deterministically. Semantic evaluation is appropriate where interpretation is required. Keeping them separate makes failures more auditable.

Can the framework work with existing QA tools?

Yes. The framework can sit alongside existing test automation, CI/CD, APIs, observability and execution engines.

See the assurance model against a real AI system.

Bring one business journey and see how Shyena maps, tests, evaluates, secures and connects the evidence to release decisions.

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