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.
AI Evaluation
Test the behaviour behind the release decision.
Build repeatable evidence about agent behaviour, components and workflow reliability.
Book a 30 minute readiness callTest the behaviour behind the release decision.
Illustrative working format
- Representative scenarios
- Build repeatable evidence about agent behaviour, components and workflow reliability.
- Repeatable evaluation
- Define the evidence, interfaces and accountable owners for this part of the work.
- Failure analysis
- Review findings against agreed decision criteria and record unresolved limitations.
Explore the engagement scope
Build repeatable evidence about agent behaviour, components and workflow reliability. Begin with an agreed workflow, accountable owner and representative evidence. The engagement defines its coverage, dependencies and decision criteria before work starts.
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.
Supporting capabilities
Agentic system architecture and engineering
Design the context, tools, state and control paths behind agent workflows.
Agent-to-agent interoperability
Test the contracts, identities and failure handling between connected agents.
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.
Privacy engineering for AI systems
Examine data flows, retention, access boundaries and privacy requirements in context.
A little more clarity
Questions worth asking.
Start with the questions that shape a better decision.
What does AI Evaluation cover?
Build repeatable evidence about agent behaviour, components and workflow reliability. Indicative focus areas include representative scenarios, repeatable evaluation, failure analysis.
What is needed to scope AI Evaluation?
Build repeatable evidence about agent behaviour, components and workflow reliability. Begin with an agreed workflow, accountable owner and representative evidence. The engagement defines its coverage, dependencies and decision criteria before work starts.
What decision does AI Evaluation support?
Test the behaviour behind the release decision. Findings and indicative outputs apply to the agreed scope. Your accountable owner retains deployment, investment and risk decisions.
How does the agent behave under test?
Bring the workflow, the constraints and the evidence you already have.