Agents that decide, act, and hand back when they should
Most agent projects die between the demo and production, because a prompt loop is not a system. We build the runtime underneath: durable execution, typed tools, real escalation paths, and a trace of every decision.
- 412 ms
- P95 agent response in production
- 100%
- Of runs traced and replayable
- 4–6 wks
- Concept to production
Where the time goes
What the day costs you now
- 01
The demo that never ships
It worked in the notebook. It fails on the fifth real input, and nobody can tell you why.
- 02
No escalation path
When the agent is unsure it guesses, because handing back to a person was never designed in.
- 03
No observability
Something went wrong last Tuesday and the only record is a log line saying the call completed.
- 04
Brittle prompts
A vendor updates a model and behaviour shifts overnight, with no regression suite to catch it.
What we build
Four systems, in production in four to six weeks.
Durable execution
State that survives a restart, with replay and resume, so a long-running task is not lost to a deploy.
Typed tool calling
Real integrations against your own systems with typed contracts, so a malformed call fails loudly instead of silently doing the wrong thing.
Human-in-the-loop
Escalation as a designed path rather than an error case, with confidence thresholds you set and can change.
Evaluation and guardrails
Task-specific eval sets built from your data, run on every model and prompt change, with policy enforced as code.
Controls
What it ships under in ai agents
- Full run traces, replayable
- Model-agnostic, no lock-in
- Your VPC, our cloud, or on-premise
- Cost and latency monitored per run
Tell us what the work looks like today.
Thirty minutes with the people who would build it. You leave with a scored readiness readout, the two workflows with the fastest payback in ai agents, and a fixed quote.