D-01 · Domain-specific engineering · AI-first, Human-in-the-Loop
Capability is commodity. Engineering is the work.
Every organisation has tried AI. Most have a licence; some have a prototype. Almost all have hit the same wall: outputs drift, agents wander, regulated workflows can't be audited. That isn't a model problem — it's a harness problem. A harness is the operating system for your agentic workforce: the engineering between the model and the business.
Most organisations do it backwards: take a human process and sprinkle AI into the gaps. We design the other way around — the harness owns the workflow and humans are inserted at the decision points, with every approval captured and signed. Done well, one reviewer supervises the throughput of a team.
- 01
Context assembly
What the AI sees — which documents, records, policies and prior decisions, and in what order.
- 02
Tool surface
The exact set of actions the AI is allowed to take, and the guards around each one.
- 03
Workflow orchestration
How agents hand work to each other, and where a human must sign off.
- 04
Evaluation & observability
How you know it worked, what it cost, and how to prove it afterwards.
- 05
Guardrails & policy
Domain rules, regulatory constraints and refusal logic — baked into the runtime, not the prompt.
- 06
Memory & state
What the system remembers between turns, sessions and users.
Questions
What is an agentic harness?
The engineering between a general-purpose AI model and your business: context assembly, the tool surface, workflow orchestration, evaluation and observability, guardrails and policy, and memory. It's what turns a model into a governed, auditable member of your team.
Why do AI pilots stall before production?
Outputs drift, agents wander and regulated workflows can't be audited. That isn't a model problem — it's a harness problem, and it's solved with engineering rather than a better prompt.
Where do humans fit in?
We design AI-first, Human-in-the-Loop: the harness owns the workflow and people are inserted at the decision points, with every approval captured and signed. Done well, one reviewer supervises the throughput of a team.
Are we locked into one AI vendor?
No. Harnesses are model-agnostic through our GAISe abstraction, so you can change model or provider without rebuilding the workflow.