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Sub-agent Tactics ​

Sub-agent Tactics (OpenAI Codex) ​

Developers often ask in the OpenAI developer forum and GitHub discussions:

How do I use multiple Codex agents in parallel to handle tasks efficiently?

Understanding Task Types ​

Before spinning up multiple Codex runs or "sub-agents," you need to distinguish between:

  • Non-destructive tasks → analysis, research, code review, documentation. Safe to parallelize.
  • Potentially destructive tasks → direct code edits, refactors, file system writes. Require coordination and serialization to avoid conflicts.

This mirrors general AI orchestration patterns: non-destructive = safe for parallelization, destructive = better handled sequentially or with a lockstep plan.

Perfect Parallelizable Tasks ​

A good Codex use case:

  • Research & documentation — Ask multiple Codex runs to explore different frameworks (e.g., compare LangChain vs. LlamaIndex vs. DSPy) and each generate pros/cons. Then consolidate the outputs into a final report.

  • Code reviews — Spawn 3–4 Codex instances to check the same diff from different perspectives: security, performance, readability, and maintainability. Each review is independent and safe to run in parallel.

Because OpenAI's API supports concurrent calls (docs), you can launch these in parallel from your orchestrator (e.g., Python asyncio, LangChain agents, or custom task runners).

Developing an Itch for Parallelism ​

Once you've used Codex this way, you'll start spotting parallel opportunities everywhere:

  • Running unit test generation per file in parallel.
  • Having separate Codex calls draft API docs, error handling improvements, and linting fixes simultaneously.
  • Spinning up a reasoning model (like GPT-4o or o3-mini) for critique, while Codex focuses on implementation.

This follows research-backed approaches like ReAct (reasoning ↔ acting separation) and Reflexion (self-critique) where multiple specialized runs improve reliability.

The Consolidation Strategy ​

Parallel outputs aren't valuable until you merge them. Typical strategy:

  1. Collect outputs from sub-agents (e.g., JSON responses or structured markdown).
  2. Feed them into a single reasoning model (e.g., GPT-4o) to consolidate.
  3. Approve a final action plan before allowing Codex to execute changes.

This ensures consistency, avoids duplicated edits, and lets you preserve a clean context window.

How to Use Sub-agents ​

Codex itself doesn't auto-spawn sub-agents like Claude, but you can simulate sub-agents via orchestration:

  • Explicitly say: "Create 3 parallel solutions to this coding problem."
  • Programmatically run N API calls in parallel, each with a different instruction (security check, style audit, test generator).
  • Label their outputs clearly, then merge.

Tools like LangChain or OpenAI Assistants API make it easier to manage multi-agent setups with memory, tools, and file handling.

Parallel Processing Mindset ​

Think like a CPU scheduler:

  • Queue non-destructive tasks for parallel Codex runs.
  • Consolidate findings into a single "master thread."
  • Execute potentially destructive changes only after review.

This way, you maximize Codex's throughput, balance safety, and avoid paying Opus-level (Claude-style) costs unnecessarily.

✅ TL;DR: In OpenAI Codex, sub-agent tactics = parallel API calls for non-destructive work + consolidation step + cautious execution for destructive tasks. This orchestration pattern is widely used in multi-agent frameworks and matches OpenAI's best practices for tool calling and structured planning.