Inspect the evidence
Follow a memory or conclusion to its source. See what supports it, correct it, and keep uncertainty visible.
AI workflow assurance
Testing the AI your business already uses. Guth assesses an existing AI workflow: its sources, permissions, and behavior in agreed test scenarios. You receive documented findings, supporting evidence, and prioritized next steps. Begin with one workflow, with scope and access agreed before work starts.
Safety failures cost time, trust, and sometimes real money. Your customers may also need to understand how you manage AI. A scoped assessment gives teams a practical view of risk and a plan they can verify.
Follow a memory or conclusion to its source. See what supports it, correct it, and keep uncertainty visible.
Define tools, approvals, model choice, and where the agent must stop.
Record inputs, permissions, actions, and outcomes so failures can be investigated.
We begin with the workflow, data, tools, people, and effects in scope. Pricing is quoted by scope after a short discovery conversation.
Map the use case, users, data, model, tools, approvals, expected behavior, and failure modes.
Run agreed tests for unsupported answers, unsafe tool use, permission gaps, leakage, cost surprises, and recovery.
Deliver findings, evidence references, prioritized fixes, and checks you can run again after the fixes.
Regulation in context
The EU AI Act uses a risk-based approach; the requirements that apply depend on your system, your role, and where it is used. NIST’s AI Risk Management Framework is voluntary guidance. We help you review your controls and records, while qualified legal advisers handle legal interpretation and formal compliance advice.
Names for the work
You may be looking for an AI workflow assessment, an AI agent audit, AI risk assessment services, or help with agent governance. These requests overlap, but the question matters: what should the workflow do, who controls its actions, and what evidence would show that it meets your criteria?
AI evaluation tests behavior. AI governance defines owners, rules, and approvals. Observability supplies logs and traces. Assurance brings relevant evidence together to support a defined claim. A technical assessment does not automatically provide legal compliance, certification, comprehensive security testing, or ongoing monitoring.
Explore the AI assurance and discovery glossary for the terms people use, their differences, and the questions behind them.
Before you begin
Agree on representative examples, identify the approved sources and versions, and compare observed answers with the evidence and acceptance criteria. Record unsupported claims and uncertainty as findings.
It can retrieve or repeat an obsolete source. Source provenance, retrieval quality, citation verification, and checks for stale knowledge help reveal that failure. The test scope determines which documents and cases are examined.
Define permitted and prohibited actions, run agreed scenarios, record the actual results, and retest after changes. An agent handoff checklist and customer acceptance tests can make the review easier to repeat.
Enforce approval at the tool or action boundary, then test attempts to act without it. A written instruction alone does not demonstrate that the permission control works.
The agreed assessment can include a workflow and source inventory, test criteria, observed results, evidence references, limitations, prioritized fixes, and a retest plan. Agree on the deliverables before access or work begins.
No blanket certification or legal compliance conclusion is included. Ongoing monitoring, broader governance programmes, security engagements, and additional workflows require their own scope. Start by describing the workflow and the question you need answered.