Multi-agent orchestration platform

A handful of operational workflows ate analyst time every day. Each one had the same shape: pull data from 3-5 internal systems, summarize, decide, log. The decision was the only part that needed a human, and most of the time even that was reflex.

LangGraph as the orchestrator. Each node is an agent (Claude or GPT) with a narrow tool surface and persistent memory in Redis. Agents talk through typed message passing, not free-form chat. Anything that writes to a system of record pauses for a human checkpoint, with the proposed diff inline.

• Typed messages between agents.Free-form chat between agents drifts in days. A schema doesn't.
• Per-agent memory, not shared.Easier to evict, easier to debug. Cross-agent context flows through explicit handoff messages.
• Human checkpoint on irreversible writes, never on reads.The bar for interrupting a human is high. Reads happen freely, writes pause.
• Replay-from-step.Every agent invocation is logged with inputs and outputs. Failed runs replay from the failing step, not from scratch.

The first version had agents calling each other recursively until token budget ran out. Now there's a depth limit and a per-run token cap enforced by the orchestrator, not the agents.