Scope
Week 1
We choose one task with you, write down what a correct result looks like and collect real examples.
- One task, one owner
- Success criteria in writing
- 30 to 100 real examples
AI agents that finish the task, and show what they did.
We design and build AI agents that plan, call your APIs and complete multi-step work. Each agent has narrow permissions, stops for a person where a person should decide, and leaves a log you can audit.
What we build
Agents, the tools they use, and the controls around them.
An agent is only as good as the tools it can call and the limits it works inside. We build all three parts: the agent loop, the tool layer and the controls.
Task agents
Agents that take a goal, make a plan, call your systems step by step and report the result. Built on Claude, OpenAI or open-weight models.
MCP servers
Model Context Protocol servers that expose your APIs, databases and internal tools to Claude, Cursor, Codex and your own agents, with typed inputs and scoped access.
Workflow automation
Back-office work such as triage, data entry, report drafts and ticket routing, moved from people to an agent that works the queue.
Human approval steps
Where money, customer data or an irreversible action is involved, the agent stops, shows what it plans to do and waits for a yes.
Evaluations
A test set made from your real cases. It runs on every change, so you know if a new prompt or a new model made the agent better or worse.
Audit logs and tracing
Every model call, tool call and decision is recorded. You can replay a run and see why the agent did what it did.
How a project runs
One task first. Then the next one.
Agent projects fail when they start too wide. We start with one task that has a clear result, prove it with numbers, and then add more.
Build
Weeks 2 to 6
We build the agent, its tools and the evaluation set together. You get a working demo every week.
- Tools with narrow permissions
- Evaluation on every change
- Weekly demo and written update
Run
After launch
The agent goes live behind approval steps and spend limits, with monitoring. We hand over the code and the runbook.
- Approval steps and limits
- Cost and quality dashboards
- Code and accounts are yours
FAQ
Questions about AI agents.
What is an AI agent?
An AI agent is software in which a language model decides which steps to take to reach a goal. It calls tools such as your APIs or database, reads the results and continues until the task is done or it needs a person.
What is an MCP server, and do we need one?
The Model Context Protocol (MCP) is an open standard that lets AI applications use external tools and data. An MCP server makes your systems available to any MCP client, such as Claude, Cursor or your own agent, without a custom integration for each one. You need one when more than one AI tool must use the same system.
Which models and frameworks do you use?
Anthropic Claude, OpenAI GPT models and open-weight models. For orchestration we use LangGraph or a custom agent loop, whichever is simpler for the task. We choose the model by running your examples through an evaluation.
How do you keep an agent from doing damage?
Each tool has the smallest permission that does the job. Risky actions need human approval. There are limits on spend and on the number of steps, and every action is logged. Before launch we test the agent for prompt injection and unsafe tool use.
Can the agent and our data stay in the EU?
Yes. We can host the application and its data in EU regions and choose model providers and settings that meet your data-processing requirements. We design for GDPR from the start.
Related
See it in practice.
Oh My Android
Our Mac app includes an MCP server that lets Claude Code, Codex and Cursor see and drive the Android Emulator.
RAG systems
When the agent must answer from your own documents, retrieval is the part that decides the quality.
Consulting
Not sure an agent is the right tool? An AI readiness review gives you an answer before you spend on a build.