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Agentic workflow development

Agents are easy to demo.
We build the ones you can ship.

A prototype that works on the happy path is a week of effort. An agent that survives real users, bad inputs, and a tool that times out is a different job entirely — and that is the job we take.

The work

What we actually do.

Design the tools before the agent

Most agent failures are tool failures. We define the actions, their inputs, and their error shapes first, so the model has something narrow and unambiguous to reason about instead of an open-ended API surface.

Orchestrate the steps

Planning, retries, fallbacks, and the point where the agent stops and asks a person. We write the control flow deterministically and let the model make only the decisions that genuinely need judgment.

Build the evals alongside

Every workflow ships with a suite that scores it on the cases you care about — tool selection, output schema, grounding, refusals. When you change a prompt, you find out what it cost you before your users do.

Instrument the run

Traces you can read, cost per task, and latency at p95. When something goes wrong in production you get the whole trajectory, not a stack trace at the end of it.

What you get

  • tool + action schema
  • orchestration layer
  • eval suite & scorecards
  • trace + cost dashboards
  • runbook & handover

What we work in

  • Claude
  • OpenAI
  • LangGraph
  • MCP
  • TypeScript
  • Python

Also from us

The rest of what we do.

Start with one workflow.

The fastest way to find out whether this fits is to pick something narrow and build it. Tell us what you have in mind.