AI Agent Development
Agents for real jobs
Output
Agents in production
Proof
Pilot, public repo
Most agent projects stop at the demo. The model answers nicely in a notebook, then meets real tickets, real customers and real data, and nobody trusts it enough to leave it alone. We build agents past that point.
What you're aiming for
- An agent that owns a job end to end: a ticket, a support thread, a nightly data run.
- Humans reviewing outcomes, not babysitting every step.
- A log of what it did and why, so you can audit it.
- Cost you can see per task, before the bill arrives.
The agents we build
Any job with a clear input and a checkable output is a candidate.
- Coding agents: ticket in, reviewed pull request out.
- Support and operations agents that read a queue, act, and escalate what they can't handle.
- Data agents that answer questions from your systems without inventing numbers.
- Review agents that gate what other agents and people are allowed to ship.
What we've shipped this way
Pilot, our coding agent, runs the studio. It picks up tickets from GitHub Issues, GitLab, Linear, Jira, Asana, Plane or Discord, writes the change with Claude Code, runs the gates and opens a pull request. Its counts are in the public repo. Pointer is a data agent: read-only on your database and documents, it picks the metric and never computes the number itself. Pilot Cloud is the hosted version, one isolated AWS setup per client.
How to start
We start with one job, in your stack, on your real tickets or queue. Scope and price are fixed before we build.
From you: one job with a clear definition of done, access to the tracker and the repo, and a person who reviews.
From us: the agent, its harness, the gates, the docs, and a handover so it is yours.
We recommend
Read next
Reliability around the model
Harness engineering
Focus
Evals & guardrails
Result
Output you can gate on
Skills, guardrails, evals and cost control around your coding agents, so the whole team ships like your best engineer. Built for Pilot first.
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