Frontier · Deploy
The agents work. Now your team has to trust them.
Training, prompt libraries, evaluation harnesses and internal champions.
Agent enablement is the organisational work of moving a team from AI curiosity to AI operation: role-specific training, maintained prompt libraries, evaluation harnesses that let non-engineers verify agent output, and identified internal champions.
Nearly two-thirds of organisations cite security and risk concerns as the top barrier to scaling agentic AI - ahead of regulatory uncertainty or technical limits (McKinsey, 2026). That is a trust and literacy gap, not a model gap.
Not advice. Working parts.
- 01Role-specific training across product, sourcing and commercial
- 02Maintained prompt libraries per function
- 03Evaluation harnesses non-engineers can run
- 04Internal champion identification and support
- 05Escalation and override paths
- 06Adoption measurement by function
Artifacts, not a deck.
Role-specific training programme
Prompt library per function, maintained
Evaluation harness your team runs without us
Adoption measurement by function
Consulting installs it. The platform runs it.
This capability has a counterpart on McLeuker AI, the agentic platform the same team built and operates. The engagement is how it arrives inside your operation, loaded with your brand context; the platform is where it runs afterwards.
Deploy. Thirty days onto McLeuker AI, then retained.
Your operation loaded onto agents that already know the fashion domain.
Diagnose
Where agents change the economics, and where they do not.
Design
Which agents, which tools, which data, which guardrails.
Deploy
Shipped into the operation with your context loaded.
Operate
Retained partnership, monthly sessions, continuous updates.
What buyers ask first.
Why is this a separate engagement rather than training bundled in?
Because it is the one most often skipped, and the deployments that fail usually failed here. A team that cannot verify an agent output will not rely on it, and an agent nobody relies on is shelfware with a running cost.
What is an evaluation harness in this context?
A way for a merchandiser or a technical designer to check whether an agent got it right, without reading a trace or asking an engineer. It is the difference between trusting the system and hoping.
Who should the internal champions be?
People who own an outcome, not people who like the technology. The strongest champions are usually the ones most sceptical at the diagnostic stage and then convinced by their own evaluation.
How is adoption measured?
By function, against the workflows the diagnostic identified. Aggregate seat counts flatter the programme and tell you nothing about whether the work changed.
Rarely bought alone.
Foundation
Agent Opportunity Diagnostic
Four weeks. A ranked map of the workflows worth automating, and the ones that aren't.
Foundation
Agent Orchestration
Single think-act-observe-ship loop, multi-model routing, parallel dispatch, self-verification.
Frontier
AI Governance & Compliance
EU AI Act, content provenance and DPP-ready product data - as an architecture problem, not a policy document.
Start with the diagnostic.
Four weeks, fixed fee, and a ranked map of what is worth automating in your operation - including what is not.