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Agentic workflows wired into real operations through Model Context Protocol — governed, auditable, and measured on the workflow's own KPIs.
The problem
AI pilots impress in demos and stall at the boundary with real systems: no safe path to tools and data, no audit trail, no owner for what the agent may do. The gap is not model quality — it is integration and governance.
How we work it
We start from a workflow your team already runs — alarm triage, report drafting, config checks — not a hypothetical assistant.
MCP servers expose your systems through typed, least-privilege tool contracts — every capability explicit, every call logged.
Guardrails, evals, and audit trails make agent behavior reviewable; usefulness is judged on the workflow's own metrics, not demo impressions.
What you get
Stack
Outcome
Agents doing bounded, reviewable work inside your systems — not a chatbot bolted onto the org chart.
Start here
Send the shape of the problem. An engineer — not a sales rep — replies within one business day.
What happens next