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Agent Systems

Multi-Agent Orchestration

LangGraph topologies with self-assessment and verification gates between every handoff.

Outcome

Production agent systems where five or more roles collaborate to research, reason, draft, and ship — with verification gates that block low-confidence output before it reaches the operator.

Graph + verification
Pattern
From any checkpoint
Replayable
Arbiter, not editor
Operator role
Technologies
Python 3.12LangGraphFastAPIWebSocketPostgres state storeTyped inter-node contractsThreshold-based gatingCritique-and-retry loops
Problem

Generic AI tools generate output faster. They don't generate better. The hard problem is reasoning about goals, evidence, and quality control — and exposing that reasoning to humans in time to intervene. That requires real orchestration, not a chat wrapper around a model.

How it's built
  • Model the system as a directed graph in LangGraph with typed input/output contracts between nodes
  • Require every agent to emit a structured self-assessment alongside its output: confidence, citations, assumptions, flags
  • Place a verification node between any two agents; gate the handoff against thresholds
  • Persist state at every node boundary so any run can be replayed from any checkpoint
  • Stream orchestration state to the UI over WebSocket so operators can pause, edit, and resume mid-flight

The default agent UX is generate-then-edit. The human becomes a slow ranker for a fast generator. The job becomes drudgery, and AI gets credit for work the human is doing. Verification gates flip this: agents self-assess, gates block low-confidence output, and what reaches the human is the small set of cases that genuinely need adjudication.

Self-assessment is a contract. Every agent emits not just output but a structured rationale: confidence score, citations, assumptions, and flagged weaknesses. The verification node between agents scores those against thresholds. Below threshold, the work goes back with a critique. Above threshold, it forwards.

The orchestrator is the architecture. The graph is the contract; adding a role doesn't change other roles' code. State persistence makes runs replayable from any checkpoint. Real-time streaming makes the system inspectable — and inspectable is the only kind of agent system anyone should ship to production.

What I'd tell someone about to build this
  • Verification gates over editing bad output. Make agents self-assess; let the gate catch the weak runs.
  • Pick LangGraph and commit. Don't roll your own state machine.
  • Persist every prompt, response, and tool call. Replay is the most underrated feature in agent systems.

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