Quality and assurance
AI agent testing and auditing
Agentic systems fail in ways unit tests do not catch. We test tool use, planning, memory and guardrails end to end, and audit deployed agents against your policies. Built for enterprise deployments where an agent acts on live systems of record and a wrong action has financial or regulatory consequence.
Discuss your AI initiativeScope of work
Process
How an engagement runs
01
Scope
We agree what to test, against which benchmarks and thresholds.
02
Harness
A reproducible test or evaluation harness is built in your repositories.
03
Execute
Suites run on every release; failures are triaged with your team.
04
Report
Findings, evidence and a remediation backlog, written for review.
Questions
Common questions
What is AI agent testing?
Structured testing of an agentic system's tool use, planning, memory and guardrails against defined tasks and policies — before deployment and continuously after it.
How is agent testing different from model evaluation?
Model evaluation measures a model in isolation. Agent testing measures the whole loop — prompts, tools, orchestration and error recovery — where most production failures occur.
What do we receive at the end?
A reproducible harness in your repositories, task-level completion metrics, and a ranked list of failures with reproduction steps.