Software Testing & Quality Assurance

Prove the agent works before it goes live

AI is not deterministic, so the same input will not always give the same output. That makes testing harder and more important. We build the evaluation harnesses, test suites and quality gates that measure how often the agent is right, catch the failures, and keep it honest as the model and your data change.

Let’s talk See how it works

Evaluate the AI

We test the agent against real examples and measure how often it gets things right, so quality is a number you can track, not a vibe.

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Test the whole system

The code, the integrations and the interface all get proper test coverage, so the parts around the model are as solid as the model itself.

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Catch the failures

We hunt the tricky inputs, the edge cases and the ways an agent goes wrong, so they surface in testing rather than in front of a customer.

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Guard against drift

Ongoing checks that flag when a model update or a data change quietly degrades quality, before your users are the ones to notice.

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What it does

Capabilities

Agent evaluation harnesses
Accuracy and quality metrics
Automated and regression tests
Edge case and failure testing
Integration and end to end coverage
Adversarial and safety checks
Drift and regression monitoring
Release quality gates

Why it holds up

Built on four things we don’t bend on.

Honesty

We tell you what AI can and cannot do, then we ship the part that pays for itself.

Craft

Production systems, not slideware. Built around how you actually work.

Speed

Find the one workflow costing the most, ship it to production, prove the return.

Care

Success is hours given back to people and dollars saved. Never the size of the invoice.

Questions

FAQ

What does testing and quality assurance for an agent actually involve?

Evaluation harnesses that test the agent against real examples and measure how often it gets things right, plus full test coverage across the code, integrations and interface around it.

Why is testing harder for an AI agent than normal software?

AI is not deterministic, so the same input will not always give the same output. That makes accuracy something we measure and track as a number rather than assume from a single test run.

Does testing stop once the agent goes live?

No. Ongoing drift and regression monitoring flags when a model update or a data change quietly degrades quality, before your users are the ones to notice.

How are tricky or unusual inputs handled?

We hunt the tricky inputs, edge cases and adversarial cases specifically, so they surface in testing rather than in front of a customer.

Get in touch

Talk to us about software testing & quality assurance

Tell us what you’re trying to do and we’ll reply with how we’d build it, no obligation.

You go live with evidence the agent works, and a safety net that catches problems as the model and your data shift, instead of finding out from an unhappy customer.

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