Agentic Testing
Confidence to tackle complexity
Many organisations are caught between the need to change, and the fear of what change might break.
Systems have accumulated years of complexity. Meanwhile, test coverage is incomplete, automation is fragmented, environments and data are inconsistent, and critical knowledge sits with a small number of people. So, teams hesitate to touch fragile code, regression cycles lengthen, and modernisation is deferred because its consequences are unclear. However, the cost of doing nothing rises each day.
Agentic AI changes both sides of that equation
Autonomous testing agents can now explore applications continuously, surfacing functional defects and edge cases at a scale that manual testing could never reach. By generating and maintaining deterministic tests across unit, integration and UI layers, the agents build the regression safety net that legacy systems have typically lacked.
How we help
By combining experienced engineers with AI-enabled and agentic testing, we create an intelligent safety net around the customer journeys, system behaviours and risks that matter most. This improves coverage, makes regression repeatable and accelerates defect diagnosis, giving leaders credible evidence that change can proceed safely.
Our engagement model
Every engagement starts with a two-day assessment: we review how you build, test and release today, identify the customer journeys where confidence matters most, and agree how improvement will be measured. You get clear findings and a practical plan, whether or not you take it further with us.
From there, a twelve-week delivery focuses on one significant journey, application or project:
Assess (2 days)
Reviewing how you currently build, test and release.
Foundation (Weeks 1–3)
Building the first safety net.
Accelerate (Weeks 4–7)
Expanding coverage until regression runs on every change.
Scale & Sustain (Weeks 8–12)
Embeding the workflows and skills into your teams so the capability remains when we leave.
What confidence looks like
- Changes that previously felt too risky become routine, because problems surface in tests rather than in production.
- Autonomous AI agents navigate more system complexity than manual review alone could reach, with experienced engineers providing the guardrails that keep it safe.
- More valuable product changes reach customers sooner, without asking customers to absorb the risk of change.
- A defensible answer to "are we ready?": know what you tested, know what you missed.
Our experience
Yuki
At Visma Yuki, a bookkeeping platform provider, the team produced 37% fewer bugs per story than comparable teams working on the same product, and the bugs that did appear were fixed 60% faster. The team also shipped 58% faster, so quality improved while speed went up. This included autonomous AI agents exploring the application for defects, alongside the deterministic regression suite.
Scopevisio
At Scopevisio, a German ERP provider, engineers told us at the outset that without tests, refactoring “feels like open-heart surgery”. The same system now has automated regression tests running on every change. The safety net was built first, before any major change was attempted, and it now catches problems that used to be found by hand, or by customers.
"Scott Logic brought the professionalism, leadership and technical calibre we needed to move a highly ambitious programme forward. The goals we set were challenging, and at the start there was real uncertainty about how we would achieve them, but the team delivered on every single one."
Dr. Lukas Pustina, CTO, Scopevisio
