Agentic AI

We architect agents the way you would architect any critical distributed system — with explicit contracts and failure plans. Tools are typed, versioned interfaces with input validation and scoped credentials, never raw system access. A planner model decomposes the goal; executor steps run against those tools; a checkpointed state store (we typically use LangGraph over Postgres, or Temporal for long-running workflows) means any failure resumes from the last good step instead of restarting — or silently corrupting — the task.

Autonomy is graduated, not binary. Every action class gets a permission tier: read-only queries execute freely, reversible writes execute with logging, consequential actions (payments, customer communications, record deletion) require human approval in a review queue designed for speed. Tool-call and token budgets bound every run. The result is a system your risk committee can reason about: it can do exactly these things, under exactly these limits, with every step traced.

Reliability is proven, not assumed. We build task-level evaluation suites — hundreds of scenario replays covering edge cases and adversarial inputs — and gate every release on pass rates. In production, full traces of every plan, tool call, and model decision flow into observability dashboards, so when an agent does something unexpected, your team replays the exact trajectory and fixes the root cause. That loop is what turns a promising pilot into a system that runs unattended at 2 a.m.