AI Harness Engineering Pod
Engineer a bounded AI execution harness with tool adapters, context handling, orchestration, recovery and observable traces.
AI Harness Engineering
Build the system around the agent.
Engineer the execution harness that connects AI models to tools, context, orchestration and recovery.
Book a 30 minute readiness callBuild the system around the agent.
- Execution control
- Engineer the execution harness that connects AI models to tools, context, orchestration and recovery.
- Tool integration
- Define the evidence, interfaces and accountable owners for this part of the work.
- Observability and recovery
- Review findings against agreed decision criteria and record unresolved limitations.
Explore the engagement scope
Engineer the execution harness that connects AI models to tools, context, orchestration and recovery. Begin with an agreed workflow, accountable owner and representative evidence. The engagement defines its coverage, dependencies and decision criteria before work starts.
AI Harness Integration Pod
Integrate an AI harness into existing systems with bounded tool access, context interfaces, failure handling and ownership handover.
Supporting capabilities
Agentic transformation partnerships
Explore sustained technical and organisational support for transformation programmes.
Agentic system architecture and engineering
Design the context, tools, state and control paths behind agent workflows.
Self-evolving and long-lived agent assurance
Examine drift, accumulated state and recovery across long-running agent workflows.
A little more clarity
Questions worth asking.
Start with the questions that shape a better decision.
What does AI Harness Engineering cover?
Engineer the execution harness that connects AI models to tools, context, orchestration and recovery. Indicative focus areas include execution control, tool integration, observability and recovery.
What is needed to scope AI Harness Engineering?
Engineer the execution harness that connects AI models to tools, context, orchestration and recovery. 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 Harness Engineering support?
Build the system around the agent. Findings and indicative outputs apply to the agreed scope. Your accountable owner retains deployment, investment and risk decisions.
What makes the agent executable and recoverable?
Bring the workflow, the constraints and the evidence you already have.