MCP server development
We build the Model Context Protocol server that lets an agent use your systems — tools named like a contract, permissions scoped tight, errors returned not thrown.
AI · Agents & MCP
AI agents & MCP servers that do real work in production
We build AI agents that use your tools to get a job done — and the MCP servers and tool-calling that connect them to your systems safely. The engineering is not the loop; it is recovery when a tool returns garbage.
MCP
servers & tool calling
Scoped
permissions per tool
Logged
every decision, auditable
Most "AI agents" are a for-loop with good marketing.
A demo agent works once. A production agent survives a tool timing out, an API returning nonsense, and an edge case nobody scoped. We build the guardrails, retries and evaluation that make an agent dependable — and expose your systems through clean MCP tools.
What we build
The parts that decide whether it works.
Tool calling & orchestration
Function calling wired into your CRM, database and APIs, with the orchestration logic that decides what to call and when — and knows when to stop.
Guardrails & evals
Human-in-the-loop on irreversible actions, evaluation on real tasks before launch, and confidence checks so the agent fails safe, not loud.
Multi-step workflows
Agents that chain research, decisions and actions across steps to complete work end to end, not just answer a question.
How we work
Scoped, shipped weekly, handed over clean.
01
Map the job
We define exactly what the agent should do, which tools it needs, and where a human must stay in the loop — fixed estimate up front.
02
Build tools & agent
We build the MCP server and tools first, then the agent on top, testing against real tasks each week in a live environment.
03
Harden for production
Permission scoping, retries, logging and evals — plus readable code and a clean handover, with support after launch.
Where it fits
What teams hire us to build.
- Support agent that resolves tickets end to end
- Ops agent that automates a multi-step internal process
- Research agent over your data and the web
- MCP server exposing your product to AI tools
- Sales agent that qualifies and drafts follow-ups
- Data agent that pulls, cleans and reports
Questions
What clients ask before we start.
What is an AI agent, in practical terms?
An agent is an LLM given tools and a goal — it decides which tool to call, reads the result, and acts again until the job is done. The hard part is not the loop; it is recovery when a tool returns garbage. That reliability engineering is what we do.
Do you build MCP servers and tool calling?
Yes — MCP (Model Context Protocol) is how we expose your systems to an agent cleanly. We build the MCP server, define tools like a contract, scope permissions tightly, and handle the errors and retries that keep an agent from going off the rails in production.
Can an agent connect to our existing tools and APIs?
That is the point. We wire agents into your CRM, database, internal APIs and third-party services through well-defined tools, with guardrails on what each is allowed to do.
How do you keep an autonomous agent safe?
Tight permission scoping per tool, human-in-the-loop on irreversible actions, evaluation on real tasks before launch, and logging so you can see every decision it made. Autonomy without those is a liability, not a feature.
MCP is new — why build on it now?
Because the buyers and the tooling are moving to it fast, and the SERP and the market are still wide open. Building your agent on a clean MCP foundation now means it stays maintainable as the ecosystem standardises around it.
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