Overview
How might we design agent orchestration so people can trust and act on AI output?
When to use
- Ideal for enterprise automation platforms, complex problem-solving systems, and workflows requiring multiple specialized AI agents to collaborate on tasks.
When to skip
- Single-agent chat products where a graph UI invents fake complexity.
- Audiences who only need an answer, not an operations console.
- Runs so short that orchestration chrome finishes after the work is done.
Rules
Pretty graphs with no live status, errors, or replay.
Agents that rewrite shared state without showing conflict or ownership.
Orchestration UIs that expose infra jargon (nodes, edges) without user jobs.
No human gate between agents that can spend, message, or delete.
Evidence
| Product | Implementation |
|---|---|
| LangGraph Studio-style UIs | Graph views of agent state machines with step inspection. |
| CrewAI / multi-agent dashboards | Role agents and task boards showing handoffs and outputs. |
| AutoGPT-style runners | Goal → task lists with sequential agent loops and logs. |
| Enterprise agent platforms | Swimlanes for planner, worker, and reviewer with approval nodes. |