Agents that reach production
Most AI projects die between the demo and production. The gap is rarely the model — it is orchestration, evaluation, guardrails and the operational surface nobody built.
We design and ship the autonomous system end to end: hierarchical multi-agent orchestration, eval harnesses treated as infrastructure, structural safety at the protocol boundary, and observability your on-call engineer can actually read. It runs in your environment, operated by your team.
Why teams bring this to us
Follow-the-sun operations
How we build: agents are staffed like an engineering org — explicit roles, tiered model routing matched to task complexity, verification that never gets downgraded, and an escalation ladder that parks work for human review after two failed escalations.
Dangerous capability is removed at the protocol layer rather than discouraged in a prompt. Irreversible actions require a signed approval before they execute. Stale data fails closed.
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The demo works. Production is a different engineering problem.
Industry estimates for agent pilots that never reach production run from 78% to 89%. The number varies by who is counting; the pattern does not. The model was rarely the blocker.
What is missing is orchestration, evaluation, guardrails, and an operational surface someone can actually run at 3am.
- Failure handling. A demo runs the happy path once. Production runs thousands of times against inputs nobody anticipated.
- Verification. A human eyeballed the demo output. At volume, something else has to decide whether the work is correct.
- Cost behaviour. Ten runs cost nothing. Ten thousand runs a day is a budget line with an owner.
- Blast radius. The demo agent had your credentials because it was quicker. That is now a security posture.
- Observability. When it misbehaves in month three, what do you actually look at?
- Explicit roles. Agents are staffed like an engineering org, not spawned ad hoc. Each has a defined remit.
- Tiered routing. Model class matched to measured task complexity, enforced at the runtime layer.
- Adversarial verification. Independent reviewers try to refute the work. Surviving refutation is the bar.
- Escalation ladder. Retry same tier, escalate one tier, then park for human review. Verification is never the step that gets downgraded.
- Structural safety. Dangerous capability removed at the protocol boundary rather than discouraged in a prompt.
The components that turn a prototype into a system.
Delivered into your environment, on your infrastructure, operated by your team. Provider-agnostic by design — nothing assumes a single model vendor remains the right choice next year.
| Component | What it does |
|---|---|
| Orchestration | Hierarchical delegation with sub-agents, isolated execution contexts, and a supervisor that owns retry, dead-letter and escalation rather than leaving it to chance |
| Evaluation harness | Golden-set parity tests, regression suites, drift and decay detection with false-positive guards. Evals as infrastructure, not a notebook someone runs manually |
| Guardrails | Tool allowlists, approval gates on irreversible actions, capability stripped at the protocol layer, stale inputs fail closed |
| Cost governor | Per-task and per-tenant budgets that throw rather than degrade; tiered router; per-call ledger recording model, tokens, cost and prompt hash |
| Observability | Behaviour, drift, spend and gate verdicts as first-class SLOs, with a kill switch that works |
| Memory | Durable lessons written after failures and retrieved before decisions, so the system compounds instead of repeating |
Fixed scope. Declared gates. One client at a time.
We do not sell open-ended time and materials, and we do not run parallel engagements. That constraint is why delivery dates hold and why senior engineers are on your problem rather than supervising it.