AI Agents & MCP

An agent is software that pursues a goal across multiple steps: read the request, look up the record, draft the response, update the system, know when to stop. The pitch is capacity. The risk is initiative without judgment. The difference between the two is not the model; it is the engineering around it.

What an agent actually needs

Strip the mythology and an agent needs four things a model alone does not have: access to your systems, limits on that access, a definition of done, and somewhere to send the cases it should not decide. Every agent failure story you have read is one of those four missing. Every dependable agent we build is those four made explicit.

MCP, in one paragraph

MCP (the Model Context Protocol, an open standard introduced by Anthropic in 2024 and since adopted across the industry) is how an agent connects to business systems without being handed the keys to everything. A business exposes specific, named tools: look up this client’s appointments, create a task, never touch payroll. The agent sees exactly those tools and nothing else. For us this is familiar territory with a new wire format: it is an integration surface, and designing integration surfaces is what we do. Its browser-side sibling, WebMCP (the same idea applied to your public website) has its own page.

What we build

Scoped tool surfaces

MCP servers and APIs that expose precisely the operations an agent may perform against your booking platform, CRM, or data warehouse: allow-lists, not hopes.

Approval steps

Workflow design that routes irreversible actions (money moved, messages sent to clients, records deleted) through a person, with everything staged so approval takes a glance, not an investigation.

Audit and monitoring

Every tool call logged: what the agent did, with what input, on whose behalf. When something looks wrong, you can answer “what happened?” in minutes, from records, not memory.

Escalation design

The most important tool an agent has is the one that hands the case to a human. We design the escalation path first and the happy path second.

The refusals

We do not deploy agents that act unsupervised on money, client communication, or records of legal consequence. We do not deploy agents against systems whose data the business does not trust: an agent on bad data is bad decisions at scale. And we do not deploy agents nobody owns: every one we build has a named human accountable for what it does, the same as any employee.

Where to start

Agent readiness is systems readiness: integrated platforms, reliable data, explicit processes. A systems assessment establishes where you stand, and the broader position on the instrument lives on Artificial Intelligence.