AI agent development that ships to production
Most AI agents look great in a demo and fall apart with real users. We build agentic systems that hold up: scoped to a clear job, wired to your tools, tested against real cases, and observable once they go live.
The problem
Prototyping an agent is easy. Making one reliable is the hard part. Teams run into non-deterministic output, runaway tool calls, made-up actions, and no real way to tell whether a change helped or hurt. That gap, between a demo that impresses and a system you would trust with customers, is where most projects quietly die.
Our approach
We start with the job you need done, not the model. We define the agent's tools and guardrails, build an evaluation set before we write the loop, and instrument every run so we can see regressions. You get a small, focused agent that fits your existing workflow, not a giant bot that tries to do everything.
What we deliver
- Agent scoping, tool design, and guardrail definition
- Tool-use / function-calling and orchestration loops
- Retrieval (RAG) grounding for accurate, cited answers
- Evaluation harnesses and regression tracking
- Human-in-the-loop approval and escalation flows
- Observability, tracing, and cost/latency monitoring
How a production agent runs
The agent plans, calls your tools, and grounds answers in your data. Anything high-risk routes to a human first, and every run is evaluated and traced so we catch regressions.
- 01
Request comes in
A user asks for something concrete.
- 02
Agent plans and acts
It calls your tools and grounds answers in your real data.
- APIs and tools
- Your data / RAG
- 03
Guardrail check
High-risk actions pause for a person. Everything else proceeds.
Yes Human approvalNo Proceed - 04
Action or answer
The agent completes the task or replies.
Use cases
Mifu: “Alex”, an AI marketing co-worker (London)
We built Alex, a conversational AI agent for the influencer-marketing platform Mifu. Alex plans campaigns, finds creators, runs outreach, tracks performance, and handles payments from start to finish. It works like a co-worker, not another dashboard.
Turned a three-week workflow into under three hours, and earned the trust of teams at StudioCanal, Universal, and e.l.f. Cosmetics.
Questions, answered
What is AI agent development?
AI agent development means building software that uses a language model to plan and take real actions through tools, like calling APIs, querying data, and finishing multi-step tasks, instead of just generating text. The production side adds guardrails, evaluation, and observability so the agent behaves predictably.
How long does it take to build a production AI agent?
A focused, single-job agent usually reaches a usable pilot in a few weeks. Most of that time goes into tool integration, evaluation, and guardrails, not the model itself.
Which models and frameworks do you use?
We pick the model per task rather than committing to one. Often that is Claude or OpenAI models, orchestrated with tools like LangGraph and the Model Context Protocol (MCP) for tool access.
