Agents that do real work in production
We design and ship autonomous agents and automation pipelines that survive contact with real data — with guardrails, evals, and human-in-the-loop where it matters.
What you get
Workflow audit
We map the process you want to automate, identify where AI actually helps, and flag the steps that should stay human.
Agent architecture
Task decomposition, tool design, memory strategy, and failure handling — documented before a line of code is written.
Production agent system
Deployed agents with structured logging, cost budgets, retry policies, and human-review queues for low-confidence actions.
Evaluation & monitoring
An eval harness that scores agent runs continuously so quality regressions are caught before your users see them.
How it works
Audit
Map the workflow, define success metrics, and pick the right automation boundary.
Design
Agent architecture with tools, guardrails, and cost budgets — reviewed with your team.
Ship
Iterative deployment: shadow mode first, then supervised, then autonomous.
Tune
Continuous evals and cost optimization once real traffic flows.
Tools we reach for
Questions founders actually ask
How do you keep agents from going off the rails?
Hard budgets (cost, retries, time), structured verification after every action, and human-review queues for anything below a confidence threshold. Autonomy is earned in stages — shadow mode, supervised mode, then autonomous.
Can agents work with our internal tools?
Yes. We build tool integrations against your APIs, databases, and even browser-only legacy systems using Playwright-based automation.
What's a realistic first agent project?
One workflow with clear success criteria — ticket triage, report generation, data reconciliation, code-fix loops. We deliberately start narrow, prove reliability, then expand.
Ready to ship your AI product? Let's scope it together.
Book a free scoping call. You'll leave with a concrete plan, a realistic budget, and a working-prototype offer — whether you build with us or not.