AI · Agents

One Peak for AI agent development.

AI agents go beyond chat: they plan, call tools, and complete multi-step work — booking, researching, operating software. We build agents on OpenAI and Claude with the unglamorous parts done properly: guardrails, evaluation, and costs that scale.

What we build

Scoped agents that complete real workflows, not open-ended chatbots.

  • Tool-using agents that operate your product's own API
  • Retrieval-augmented agents over your documents and data
  • Workflow agents: intake, triage, drafting, scheduling
  • Multi-step orchestration with LangGraph-style state machines

Why most agent projects fail

Unbounded scope. An agent that 'handles customer support' fails; an agent that drafts responses for the five most common ticket types, with confidence thresholds and human handoff, ships. We scope agents like products: one job, measurable success.

The reliability work

Demos are easy; production agents need evaluation sets, tracing, guardrails on tools with side effects, and graceful failure. That's the actual engineering — and it's included, not optional.

Our process

From first conversation to launch.

01

Discovery & Scope

We clarify the buyer, the core workflow, and platform requirements before any design or code begins.

Deliverables
Scope doc, feature priority list, architecture direction
Process
1 workshop session, async Q&A, written summary
02

Design

High-fidelity screens and flows for the core journey, optimised for the way people actually use the product.

Deliverables
Figma prototype, component system, handoff-ready specs
Process
2 feedback rounds, async or live review
03

Build

Implementation with clean architecture, tested as we go and wired to a real backend and staging environment.

Deliverables
Working product, documented codebase, staging environment
Process
Weekly milestone demos, async updates
04

Launch & Iterate

We deploy, set up analytics, review the first real usage, and plan the next cycle with you.

Deliverables
Live product, analytics dashboard, iteration roadmap
Process
30-day post-launch support window

Technical expertise

What we bring to the build.

Orchestration

LangGraph and custom state machines for multi-step, recoverable workflows.

Tool design

Schema-first tools with permission boundaries — agents that can't take actions you didn't grant.

RAG done right

Chunking, embeddings, and retrieval tuned and evaluated, not just installed.

Evaluation & tracing

Test sets and production traces so quality is a number, not an anecdote.

Cost engineering

Model routing, caching, and budgets — agent economics designed before launch.

Human handoff

Confidence thresholds and escalation paths for the cases the agent shouldn't own.

Practical answers

Questions founders ask before moving forward.

What's the difference between an AI agent and a chatbot?

A chatbot answers; an agent acts — it plans steps, calls tools, and completes a task. Agents need guardrails and evaluation a chatbot doesn't.

Which model should our agent use?

Often several: a capable model for planning, cheaper ones for routine steps. We route by task and measure quality per route.

How do you stop an agent from going off the rails?

Bounded tools, permission checks, confidence thresholds, human handoff, and traces on every run — designed in from the start.

What does an agent MVP cost?

From €7,000 when the agent is the core feature — one workflow, evaluated and production-ready, plus the product around it.

Can you add an agent to our existing product?

Yes — most agent work is an API layer plus UI surface on what you already have, not a rebuild.

Related pages

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Blog

Related reading.

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Next step

Have a workflow an agent should own?

Describe the job — we'll scope an agent that completes it reliably, with the guardrails to prove it.