Parallel Agent Execution - Fan-out and Fan-in - Interactive Visualization

Parallel agent execution dramatically reduces wall-clock time by running independent subtasks simultaneously. A fan-out step assigns each subtask to a separate agent; a fan-in step collects results and synthesizes them once all (or enough) agents complete. The speedup equals the sequential total divided by the longest single task. Dependency analysis determines which subtasks can safely run in parallel versus which must execute sequentially due to data dependencies.

• Fan-out assigns independent subtasks to separate agents running concurrently
• Wall-clock time equals the slowest agent, not the sum - yielding large speedups for N agents
• Fan-in must handle partial results gracefully when one or more agents fail
• Dependency graphs determine which subtasks are safe to parallelize and which must remain sequential

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.