Agentic AI Implementation. Production agents, embedded.
Production agents embedded in your workflows, on the platform that runs eight in ours. Not chat demos — governed workers with confidence scores, gates and write-backs.
AGENTS · GOVERNED WORKERSWhat this engagement is
The gap between an agent demo and an agent in production is governance: what it may touch, when it must stop, who owns its exceptions, and how its work is inspected. We build agents on the same platform that runs our own delivery — eight agents in production across our internal operations — so those questions are answered by architecture, not by hope.
Agents run against your Company Brain: grounded in the graph, bounded by your policies, gated at the Human Check Point, and visible like colleagues — every run records its inputs, model versions, cost and self-assessment.
Typical first agents: claims pre-inspection, document verification, ticket triage, quote assembly, report generation. We ship one function to production, prove the metric, then scale sideways.
The numbers behind it
What ships
Agent design & skills
Your experts' judgment captured as reusable, testable skills.
Tool integrations
Typed, scoped actions into your systems — every call logged and revocable.
Gate placement
Escalation paths and approval thresholds defined before launch.
Run observability
"How this was made" panels for every run: inputs, confidence, cost.
Evaluation harness
Golden sets and regression suites so agent changes ship like code.
Production operations
SLOs, cost budgets and drift monitoring from day one.
How the engagement runs
Five phases from one selected workflow to agents that run and write back.
Proof from production
From hours of BIM drudgery to minutes
“Agents grounded in the project ontology now run the BIM automation engineers used to do by hand — hours of modelling work compressed into minutes, with humans gating every release.”
Questions teams ask
Which models do the agents run on?
Whichever fits the job — the skills, policies and index sit above model choice, so models can be swapped without rewriting the agent. You own the model decision, not the vendor.
How do you handle hallucinations?
Grounding plus gates. Agents retrieve from your governed index and carry a traceable rationale for every run; anything consequential passes a human checkpoint before it acts. Trust is a process, not a model setting.
Do we own the agents at the end?
Yes. Skills, policies, evaluation suites and platform configuration are handed over as versioned artifacts — no licence dependency on us to keep them running.
How do agents respect our permissions?
The index carries permissions with the data. An agent answering on someone's behalf can only see what that person could see — enforcement happens below the agent, not inside its prompt.
Pick your function. Own the intelligence behind it.
Discover one opportunity, engineer one capability, deliver one measurable outcome — then scale.