AI Fractional Agentic AI Office
Create a regular place for agentic AI decisions. Connect initiative priorities, architecture questions, risk ownership and evidence reviews.
Scale
Keep control as the system grows.
Plan for integration, sustained engineering and recurring assurance as agents, tools and operating conditions evolve.
Book a 30 minute readiness callTreat change as a reason to revisit evidence
- Observe changes
Identify new tools, data sources, users and operating conditions.
- Assess consequence
Trace which assumptions, controls and evaluation cases are affected.
- Revisit the decision
Re-test the relevant boundaries and record the revised operating scope.
Ways to approach the work
AI Fractional Agentic Transformation Office
Coordinate the operating changes behind agent adoption. Align workflow redesign, ownership, dependencies and adoption measures.
AI Harness Engineering Pod
Engineer a bounded AI execution harness with tool adapters, context handling, orchestration, recovery and observable traces.
AI Harness Integration Pod
Integrate an AI harness into existing systems with bounded tool access, context interfaces, failure handling and ownership handover.
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.
AI Impact Measurement Framework
Define what better should mean before scaling an agent. Connect quality, cost, adoption and business outcomes to a baseline your team can revisit.
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.
Agent-to-agent interoperability
Test the contracts, identities and failure handling between connected agents.
Partnerships with agentic IaaS, PaaS, and SaaS companies
Explore collaboration with platform providers around integration and customer readiness.
Self-evolving and long-lived agent assurance
Examine drift, accumulated state and recovery across long-running agent workflows.
Measuring AI impact
Define baselines and measures that connect workflow behaviour to business value.
A little more clarity
Questions worth asking.
Start with the questions that shape a better decision.
What changes after launch?
Plan for integration, sustained engineering and recurring assurance as agents, tools and operating conditions evolve.
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 changes after launch?
Bring the workflow, the constraints and the evidence you already have.