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AI agents that work inside your tools.
Service — AI Agents & MCP
AI agents read requests, look up context and take actions in your CRM, ticketing or ERP. We connect them through APIs and the Model Context Protocol (MCP), with clear permissions, human approval for binding steps and a full audit trail.
Agents with tools, limits and oversight.
Agents with tools, limits and oversight.
What we build
Workflow agents
Agents that handle a defined process end to end: classify a request, gather context, prepare an action and hand over to a person where needed.
MCP servers for your systems
Model Context Protocol servers that expose your internal tools and data to AI assistants in a controlled, reusable way.
Tool & API integration
Secure connections to CRM, ticketing, ERP, email and document systems, with scoped credentials and least-privilege access.
Human-in-the-loop approval
Approval steps before commitments, payments or customer-facing messages, configured to your risk tolerance.
Stack
Model-agnostic, standards-based.
Agents act. People stay accountable.
Autonomy with boundaries
An agent is only as trustworthy as the limits around it. We design permissions, approval points and logging first, then add capabilities. That way, every action is traceable and your team decides how much autonomy each step gets.
From one task to a reliable agent.
How we work
[Build]
[01]
Pick one processWe choose a recurring, well-defined task and document today’s steps, systems and decision points.
[02]
Define tools & limitsWhich systems may the agent read or change? Where does a person approve? What counts as success?
[Deliver]
[03]
Build & testThe agent is built with scoped tools and tested against realistic scenarios, including incomplete and adversarial inputs.
[04]
Pilot & expandA limited pilot with monitoring and review, then gradual expansion to more cases or tools.
Further reading
Go deeper on this topic.
The Model Context Protocol in 2026: A Field Guide
Article: how MCP connects AI assistants to tools and data.
AI Agents in Production: What Actually Works in 2026
Article: patterns and limits of AI agents in production.
Guide: automating customer enquiries
From inbox to a reviewed workflow with human approval.
AI security & EU AI Act
Prompt-injection testing, guardrails and governance.
FAQ
AI agents, without the hype.
An AI agent is software that uses a language model to decide which steps to take toward a goal and then executes them through tools, such as searching a database, creating a ticket or drafting an email.
MCP is an open standard for connecting AI applications to tools and data sources. Instead of building a custom integration for every assistant, a system is exposed once through an MCP server and can then be used by compatible clients.
A chatbot answers questions. An agent can also take actions in other systems. That is why agents need permissions, approval steps and logging that a simple chatbot does not.
With scoped tool permissions, human approval before binding actions, input validation, idempotent operations and monitoring. Agents are tested against realistic and adversarial scenarios before going live.
Any system with an API or database access, for example CRM, ticketing, ERP, email, calendars and document storage. Where an MCP server already exists, integration is faster.
We use API tiers and hosting options where your data is not used for training, and can run agents with self-hosted models when required.
Which task should an agent take off your plate?
Describe one recurring process. We assess whether an agent is the right fit and which approvals it would need.
Get in Touch
Book an intro call.
Tell us about your project — we'll get back within 24 hours with a tailored proposal. No fluff, no boilerplate.
Pick a slot that suits you — or use the form. We reply within 24 hours on working days.
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