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New projects
A paragraph about the problem is enough to start. We reply within two working days, usually with questions rather than a quote.
AI · Service AI agent development
Production AI agents built with scoped tool access, state that survives a retry, an evaluation suite from your real inputs, and a cost ceiling per run.
In short
An AI agent is a language model that takes actions — it calls tools, reads the results, and decides what to do next until a task is finished. We design and build agents for production: the tools they may call, the state they keep between steps, the points where a person approves, and the tests that show the agent still behaves after every change.
- How to engage
- Product build, Dedicated engineering pod
- Practice
- AI
Signs this is the right service
- A prototype works in a demo and nobody can say how often it is wrong.
- A workflow has too many branches for a fixed script, but each step is well understood.
- An agent needs to act inside real systems — tickets, billing, a CRM — not only answer questions.
- More than one agent has to hand work to another without losing or repeating it.
What you get
Task and tool design
The smallest set of tools that completes the job, each with a narrow scope, a typed input, and an error shape the model can recover from.
State and retries
Run state stored outside the model, idempotent tool calls, and resumable steps, so a timeout does not send the same email twice.
Human approval points
Anything that moves money, changes access, or contacts a customer stops for a person. Where that line sits is decided with you and written down.
Evaluation suite
Test cases built from your real historical inputs, scored on every pull request, so a prompt change that breaks an old case is caught before release.
Traces and cost limits
A trace per run showing each model call and tool call, with a spend ceiling per run and per day that stops the agent rather than the invoice.
The process
- 01
Scope the task
We write down what the agent must do, what it must never do, and how success is measured, using examples from your own data.
- 02
Build the evaluation first
The test set exists before the agent does, so every later decision is scored rather than argued.
- 03
Build against real systems
Tools are connected to a staging copy of your systems from the first week, not mocked until the end.
- 04
Stage the rollout
Shadow mode, then a small share of traffic with a person reviewing, then wider — each step gated on the evaluation score.
ClaudeMCPTypeScriptPythonTemporalPostgresOpenTelemetry
AI agent development: common questions
Which model do you build on?
- We choose per task and keep the provider behind an interface, so the model can be changed without rewriting the agent. The evaluation suite is what tells us whether a different model is better for your workload.
How do you stop an agent doing something harmful?
- By limiting what it can do, not by asking it nicely. Each tool has the narrowest scope that works, write actions are separated from read actions, and high-impact actions require a person to approve.
Can the agent work inside our existing product?
- Yes. We build inside your authentication, your data boundaries and your release process, so the agent is a feature of your product rather than a separate system your team has to learn to operate.
What does it cost to run?
- It depends on the model, the length of each run and how many runs you have. We measure cost per run during the build and set a ceiling, so the running cost is a known number before launch.
06 / Let’s build
Bring us the difficult problem.
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Careers
Engineers, designers and researchers who like systems that are hard to get right. Send something you built and what you would change about it now.
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Studio
Based in India and working remotely, with overlap hours agreed up front so your team always has a time to reach us.
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Read
Guides on AI agents, RAG, MCP, tokenization and chain abstraction, written by the engineers who build them.