Services / Agentic workflows
Agentic workflows.
Multi-step AI that takes real action — inside your product, your ops, or both. We build the guardrails first, then the agent. That order is what keeps your CFO and your on-call engineer out of an incident.
When this is the right fit
- A repeatable workflow exists today that takes a human 5–60 minutes per instance.
- The workflow involves multiple steps, multiple data sources, and a non-trivial decision.
- You can tolerate some human-in-the-loop in v1 — full automation is a v2 conversation.
- The cost of the agent making the wrong call is bounded (refundable, reversible, or low blast radius).
What we ship
- Workflow decomposition — explicit step graph, tool definitions, success criteria per step.
- Guardrails — input validation, output validation, bounded retries, circuit breakers, and a kill switch your operator can hit.
- Tool layer — well-defined tools the agent can call, with explicit auth and rate limiting.
- Observability — every step logged with inputs, outputs, latency, cost, and decision rationale. Replayable.
- Eval suite — golden trajectories, regression tests on workflow outcomes.
- Human review queue — for v1, every action goes through a queue your operators can approve, edit, or reject.
- Runbook — failure modes, rollback procedure, on-call playbook.
Typical timeline
| Week | What ships |
|---|---|
| 1 | Workflow decomposition, success criteria, guardrails plan, scope. |
| 2–3 | Tool layer + guardrails. Agent does NOT have decision authority yet. |
| 4–6 | Agent on top of the tool layer. Human-in-the-loop deployment. |
| 7–8 | Production rollout. Telemetry to support eventual full automation. |
Range: 6–8 weeks.
FAQ
Are you building a fully autonomous agent?
v1: never. Every agent we ship goes live with a human in the loop. We then use telemetry to identify which decisions are safe to automate. That's v2.
Do you use LangChain / LangGraph / CrewAI / AutoGen?
We use the simplest framework that works for the workflow — sometimes one of the above, often a small purpose-built loop. We are honest with clients about framework lock-in risk.
What about hallucinations?
Hallucinations are a generation problem; agentic failures are usually a decision-graph problem. We design the graph to fail closed and to escalate ambiguous decisions to a human.