Autonomous agents scale a retail platform to 100K monthly transactions.
A multi‑step agent system that plans, calls tools, and verifies its own work — carrying a retail platform to 100K transactions a month.
- Transactions
- 100K/mo
- Task success
- 96%
- Stack
- Claude · LangGraph
- Status
- Live
Order handling, inventory reconciliation, and customer follow‑up were bottlenecked on manual operations. We designed an autonomous agent layer that decomposes each request, calls the platform’s internal tools to carry it out, and verifies the result against acceptance criteria before it completes.
Reliability came from structure, not luck: bounded retries, deterministic guardrails, and a human‑in‑the‑loop escalation path for anything the agent flagged as uncertain. Task success settled at 96%, and the platform scaled to 100K monthly transactions without a proportional headcount increase.
Every agent run is fully traced, so the operations team can see exactly which tool was called, with what arguments, and why — turning a black box into something they can actually own.