Every engagement ships these as concrete artifacts you own — not slides, not hand-waving.
LangGraph (or your framework of choice) with explicit nodes, edges, and rollback points — not a black-box ReAct loop.
Typed, schema-validated tools with rate limits, idempotency keys, and dry-run modes for safe testing.
Every agent run is replayable: see every prompt, tool call, intermediate state. Critical for debugging and audits.
UI for ops staff to approve / reject / edit at chosen boundaries. Slack approvals, queue dashboards, escalation routing.
Break the workflow into a graph of nodes and decisions. Identify what's deterministic, what needs an LLM, what needs a human.
Implement nodes, tools, and the state machine. Integrate with your CRM / DB / SaaS. Daily eval runs on golden trajectories.
Load test, chaos-test (failed tool calls, malformed inputs), tune retry / fallback policies, finalise audit logging.
Roll out with a controlled cohort, watch dashboards, tune. Hand off runbooks to your team.
Best-in-class where it matters; boring and battle-tested everywhere else.
Agentic systems iterate a lot — fixed bids tend to underprice or over-spec. We engage as a pod (2 senior eng, 1 ML, 1 PM) on monthly retainer. Roll on/off any month after the first.
LangGraph for production / enterprise — explicit graphs, audit-friendly, durable state. CrewAI when role-based team-of-agents is the natural model. OpenAI Agents SDK when you're already deep in the OpenAI stack. We pick per-engagement, not by religion.
Per-step tool-call budgets, max-iteration limits, hard cost ceilings, and live dashboards. We've yet to ship an agent that blew its budget — but the guardrails matter.
Yes — we configure checkpoint policy per node. High-stakes decisions get human-in-loop; low-stakes batch through. You decide the boundary.
Explicit timeout, fallback to a human queue, and a notification. Stuck-without-notice is the #1 production failure mode and we design against it.