AI Agent Org Chart Patterns: Single, Multi, Supervisor, Hierarchical (2026)
Where AI agents fit in your team. Visualised, with a cited source for every diagram.
Single agent, multi-agent peer, supervisor, hierarchical, human-in-the-loop, evaluator-optimiser, orchestrator-workers. Codified in Anthropic 2024, LangGraph, CrewAI, AutoGen documentation. Examples drawn from named industry deployments.
An AI agent org chart is a visualisation of where one or more AI agents sit in a team or system structure, with reporting lines to humans and to other agents. Like any organisational diagram, the structure shapes what the system does and who is accountable for it. The seven canonical patterns on this site are drawn from public engineering literature and named industry case studies. Every diagram cites its source.
This is the structural view. For the underlying definition of an AI agent, see whatisanaiagent.com. For the process flow view (how work moves across agents and teams over time), see agenticswimlanes.com. For the engineering reference behind these patterns, see buildingeffectiveagents.com.
Each pattern documented with a cited public source.
One agent, model, and tool surface. The foundational unit. Documented in Anthropic's Building Effective Agents (December 2024) as the augmented LLM.
Two or more agents collaborate as peers via a shared message bus or a turn-taking conversation. CrewAI's role-based crew and AutoGen's group-chat are common implementations.
One supervisor agent decomposes a goal and dispatches sub-tasks to specialised worker agents. Defined by Anthropic (December 2024) and shipped as a primitive in LangGraph.
A manager routes work to team-leads who route work to workers. Documented in LangGraph as the hierarchical-teams primitive.
An agent's actions are reviewed or approved by a human before they take effect. Three named variants: assistant, reviewer, arbiter. LangGraph's interrupt() primitive is the canonical implementation.
Two agents in a loop: a generator produces a candidate output, an evaluator scores it, the generator revises. Defined by Anthropic (December 2024).
An orchestrator decomposes a goal into sub-tasks at runtime and spawns worker agents to execute them. Defined by Anthropic (December 2024) alongside the supervisor pattern.
When to use which pattern.
Most production deployments could be single-agent with better tools. The honest framing in Anthropic’s December 2024 paper is that complexity should be added only when a simpler shape has been ruled out by evidence. A single well-tooled agent with a sensible prompt and a curated tool surface clears the bar for most internal-tooling and structured-task workflows.
Peer multi-agent and supervisor patterns are genuinely valuable for parallel work, role specialisation, and tasks that exceed a single context window. The supervisor pattern (Anthropic’s orchestrator-workers, LangGraph’s supervisor) is the most-cited shape for an enterprise team because it preserves a single accountable orchestrator for cost, latency, and audit while still exploiting parallelism in the workers.
Hierarchy adds value when sub-teams have clearly separable scopes. Human-in-the-loop is mandatory wherever the agent has write access to systems of record, where the action is irreversible, or where the cost of an error exceeds the cost of human review. The evaluator-optimiser pattern is the right shape when quality criteria are well-defined and iterative improvement converges within a bounded number of rounds.
Cited case studies, not invented examples.
Each industry sub-page carries one named case study with a publication date. See the index.
A neutral comparison of the diagram tools.
A reader who lands on this site usually wants the same artefact for their own deck. The honest answer: the tooling is a stylistic choice (mermaid, react-flow, D3, hand-built SVG, excalidraw); the substance is the topology. The patterns above are the substance.
The tools to build yours page lays out the bundle-size, accessibility, build-time-versus-runtime, SEO, and licensing tradeoffs for each. No rankings. No “best”. And the examples gallery shows fifteen-plus published deployments with the source URL and access date for each.
Every diagram on this site references a real, public artefact.
If a topology pattern cannot be sourced to a published paper, framework doc, or named case study with a publication date, it does not appear here. The hand-built SVGs cite their source on first appearance. The per-example diagrams carry the citation in the figcaption with the access date. The methodology page lays out the full source bibliography, the update cadence, and the editorial discipline. Corrections are welcomed via the contact note in the methodology page.