Agents that do real work
Multi-step systems that take an actual task off someone's desk — with the tools, permissions and audit trail that makes that safe. Most of what gets called an agent should be a script; we say so.
Three reasons teams bring us agent work
When an agent is right
An agent earns its complexity when the task has many steps, branches on judgement, and touches more than one system. If the path is fixed, we build a pipeline instead and charge you less.
→Many steps, real branching, more than one system→Judgement calls a rule set cannot enumerate→Volume high enough that people are the bottleneck→A human who owns the outcome and reviews the edge cases→A rollback story if the agent gets it wrongTools, permissions, limits
An agent is only as safe as the tools you give it. We define each tool narrowly, scope its permissions, and put hard limits on what can happen without a human.
→Typed, validated tool interfaces — no open shell→Least-privilege credentials per tool, per environment→Spend, rate and action caps enforced outside the model→Approval gates on anything irreversible→Full trace of every call, input and decisionRunning them in production
We operate the unglamorous parts: retries, partial failure, drift, cost, and the dashboard your ops team actually reads.
→Deterministic retries and idempotent tool calls→Failure modes designed, not discovered→Evals per task type, run on every change→Cost and latency per task, tracked and alerted→Handover so your team can add tools safelyBook a call→An agent you cannot audit is a liability
Every agent we ship writes a complete trace: which tools it called, with what, and why it stopped. If a run cannot be explained after the fact, it is not finished.
What we build them on
A short, deliberate list — each in production on a system we maintain. See the full partner stack.
Clients we work with
Agent case studies
What we will not do
Most agent briefs we receive should be smaller. These are the jobs we hand back.
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