---
type: concept
title: "Parallel Agent Execution"
description: "Running multiple agents (or agent steps) concurrently to cut wall-clock time on a task."
tags: [agents, parallelism, orchestration, concurrency]
videos: [should-i-build-my-ai-agents-with-n8n-or-python]
created: 2026-07-21
updated: 2026-07-21
---

# Parallel Agent Execution

Parallel agent execution means dispatching several agents, or several branches of one workflow, at the same time so their work overlaps instead of queuing one after another. The classic example is fan-out research: "it kicks off all of these specialized research agents at exactly the same time" [0:12:24]. Because each agent works independently and only its summary is needed back, they can all run concurrently and the orchestrator waits once for the slowest rather than summing every step's latency.

## How it works

The distinction Cole draws is between *true* concurrency and the appearance of it. In a code-based framework you can genuinely launch N agents on N threads or async tasks and collect their results; the wall-clock cost collapses to roughly the single slowest branch. He contrasts this sharply with visual no-code tools: "in n8n there isn't actually the idea of parallel execution... these will end up running simultaneously" is the promise, but he is clear it does not hold. "You can't do this in n8n. It's just not possible" [0:12:24]. The practical upshot is that when a task decomposes into independent sub-tasks (multiple research queries, multiple tool calls, multiple document reads) parallelism is close to free speed, and it is one of the strongest arguments for building agents in real code when latency matters.

## Contrasts with

- [No-Code vs. Code](./no-code-vs-code.md) - genuine parallel execution is a capability code has and visual no-code builders like n8n largely fake with simultaneous-looking sequential runs.

## Related

- [Synthesizer Agent](./synthesizer-agent.md) - the final agent in a parallel architecture that takes every sub-agent's output and summarizes, formats, and optionally validates it into one coherent answer, replacing a dumb non-LLM aggregator.
- [Subagents Pattern](./subagents-pattern.md) - subagents are the usual unit that gets run in parallel, each with its own isolated context.
- [Parallel Agentic Coding](./parallel-agentic-coding.md) - the same concurrency idea applied to running multiple coding agents on separate worktrees.
- [Agent Teams](./agent-teams.md) - coordinated groups of agents where parallelism is one of the coordination modes.
- [Disposable Agent Sandboxes](./disposable-agent-sandboxes.md) - Running each agent in an ephemeral, preloaded cloud dev box ('cattle, not pets') instead of a local worktree, so many unattended agents can run in parallel with scoped permissions.

## Tools

- [n8n](../entities/tools/n8n.md) - the no-code workflow tool Cole names as lacking true parallel execution.
- [Higgsfield](../entities/tools/higgsfield.md) - A video-generation platform with a CLI used to create marketing videos and UGC-style ads from prompts and branded reference images, driven from inside the coding agent's terminal.
- [iTerm2](../entities/tools/iterm2.md) - macOS terminal application with split-pane support; one of only two terminals Claude Code can drive to display agent teams live.
- [tmux](../entities/tools/tmux.md) - Terminal multiplexer (Cole's recommended option) that Claude Code drives to create the split-pane windows showing each agent-team teammate working in real time.

## Sources

- [Should I Build My AI Agents with n8n or Python?](../sources/should-i-build-my-ai-agents-with-n8n-or-python.md) - "[0:12:24] it kicks off all of these specialized research agents at exactly the same time... And you can't do this in n8n. It's just not possible."
