AI Agent Evaluation Evidence Diagnostic
Find the gaps between your release decision and the evidence behind it. Start with one agent workflow and a bounded set of questions.
Evaluate
Make release confidence inspectable.
Connect requirements to representative cases, traces and findings. Understand where the system works, where it fails and what remains unknown.
Book a 30 minute readiness callA release claim needs an inspectable evidence trail
Illustrative working format
- Requirement
- State expected behaviour and the conditions under which it matters.
- Case and trace
- Connect representative scenarios to observable tool and system behaviour.
- Finding and decision
- Record uncertainty, proposed restrictions and the accountable release owner.
Ways to approach the work
AI Agent Evaluation Foundation
Turn scattered tests into a repeatable release discipline. Connect the behaviours that matter to datasets, evaluation criteria and evidence your team can maintain.
AI Agent Component Evaluation
Find where agent behaviour breaks down. Follow memory, retrieval and delegation through your workflow, then isolate the components that need attention.
AI Agent Protocol Interoperability Lab
Find where connected agents disagree. Exercise message contracts, identity propagation and failure recovery across your selected integrations.
AI Retrieval Evaluation
Find why the right evidence is missing from an agent’s answer. Evaluate retrieval, grounding and context assembly against representative tasks.
AI Long-Horizon Agent Evaluation
Examine what changes when an agent keeps working. Evaluate accumulated state, recovery and goal adherence across extended tasks.
AI Orchestration Reliability Review
Follow the work across agents and tools. Examine routing, delegation, retries and recovery where coordination can fail.
Agent Release Readiness Review
The Agent Release Readiness Review is a fixed-scope, three-week independent test of one AI agent workflow that shows a release board what the agent does, what it is allowed to do and what happens when it is wrong, and ends in a signed recommendation.
AI Agent Memory Assurance
Examine what your agent remembers, when it retrieves it and whose information it can expose—in the deployment where it actually works.
AI Continuous Agent Assurance
Keep evidence aligned with change. Define recurring checks for the models, prompts, tools and workflows your agents depend on.
AI Production Agent Assurance
Review the evidence needed to operate a specific agent workflow, from release criteria to monitoring, escalation and rollback.
Supporting capabilities
Self-evolving and long-lived agent assurance
Examine drift, accumulated state and recovery across long-running agent workflows.
Agent evaluation and assurance
Connect intended agent behaviour to test cases, traces and release criteria.
Component-level agent evaluation
Isolate failures in memory, retrieval, context and orchestration within a deployment.
Distinctive research-led offerings
Develop research questions, reproducible methods and useful assurance frameworks.
A little more clarity
Questions worth asking.
Start with the questions that shape a better decision.
What evidence supports the decision?
Connect requirements to representative cases, traces and findings. Understand where the system works, where it fails and what remains unknown.
How is the engagement scope agreed?
Start with one workflow, its accountable owner and the decision that needs evidence. Agree inputs, boundaries, deliverables and acceptance criteria before work begins.
Can a project involve more than one service?
Yes. A release question may involve evaluation, security and governance together. The scope should connect these disciplines to one decision and avoid duplicating work.
What evidence supports the decision?
Bring the workflow, the constraints and the evidence you already have.