AI Company Templates

Catalogs reusable structural blueprints for AI agent companies, including org charts, role definitions, and inter-agent reporting lines. Each template encodes a tested pattern: solo operator, two-agent loop, hub-and-spoke, or full hierarchy with specialist nodes.

Intended as a starting point rather than a prescription. Operators copy a template, rename roles, and edit responsibilities to fit a specific workflow.

The gallery is organized by team size. Single-agent setups handle one closed loop: read input, act, log output. Two-agent configurations split planning from execution, a pattern popularized by ReAct-style and AutoGPT-style early systems. Hub-and-spoke designs assign a coordinator that delegates to specialists; this works well when subtasks are heterogeneous (research, writing, code, finance) and parallelism reduces total runtime. The deeper hierarchical templates introduce middle-manager agents that aggregate specialist output before reporting upward, mirroring conventional corporate structures.

A worked example: a content-marketing pod template includes one editor agent, three writer agents, one fact-checker, and one publisher. The editor receives a brief, drafts an outline, dispatches sections to writers in parallel, hands the assembled draft to the fact-checker for citation verification, and routes the approved piece to the publisher for CMS upload. Each role description states the inputs the agent expects, the outputs it must return, escalation rules, and tool permissions. Operators tweak the multipliers (more writers, no fact-checker, etc.) without rebuilding from scratch.

Limitations are real. The templates do not specify which model powers each role, do not provide prompts, and do not guarantee that a given orchestration runtime can wire the structure together. Some patterns assume the runtime supports parallel tool calls or persistent memory; if it does not, the pattern degrades to sequential. Templates also tend to overspecify roles. Most early-stage workflows succeed with one or two agents; layering on a five-role hierarchy before there is volume to justify it adds coordination tax with no payoff. The honest recommendation: start with the smallest template that solves the problem and only graduate when handoff bottlenecks appear.

Related reading worth pursuing includes the Stanford Generative Agents paper, the multi-agent debate literature, and the older organizational-design canon (Mintzberg's structural configurations). Each frames the same trade-off from a different angle: communication overhead vs. specialization gains.