# Codex Subagents: Dividing Complex Tasks into Specialized Units How AI systems delegate tasks to specialist agents and coordinate their work - **Source:** [Towards Data Science](https://towardsdatascience.com) - **Published:** 2026-08-28 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/df830c97-eabe-4023-823c-081689a44776 ## The Single Agent Fallacy Users often perceive Codex as a single entity, but for complex tasks, it spawns specialized subagents. In a 2026 travel planning scenario, three subagents worked in parallel: a transport specialist, budget analyst, and experience researcher. Each evaluated all destinations (Lisbon, Prague, Copenhagen) from their unique perspective, demonstrating Codex's task decomposition capability. ## Agent Architecture Anatomy Codex allows custom agent definitions via TOML files in .codex/agents/ directory. Each requires name, description, and developer_instructions fields. The travel logistics agent assessed transportation convenience, budget analyst evaluated CHF 1,200 affordability, while experience researcher focused on museums and local cuisine. This modular approach enables precise task specialization within AI systems. ## Parallel Workflow Execution When main agent spawns subagents, each operates in its own thread. The config.toml's max_concurrent_threads_per_session=3 setting limits parallel execution. In our Zurich trip case, all three agents' progress was monitorable via CLI's /agent command in real-time. This architecture mirrors human team dynamics where specialists work independently yet collaboratively. ## The Expertise Conflict In the travel scenario, subagents reached divergent conclusions: transport agent recommended Copenhagen, budget agent preferred Prague, experience researcher advocated Lisbon. This reflects different priority weightings across specialties. Codex's main agent synthesized these, evaluating against original requirements (convenience, museums, food) rather than blindly accepting any single recommendation. ## Dynamic Agent Definition Subagents aren't limited to one-off tasks. AGENTS.md files can establish project-wide delegation strategies, while SKILL.md packages agent behavior for recurring task types. For instance, travel planning could become a reusable skill. This flexibility allows organizations to codify best practices for AI-assisted decision making across various domains. ## Subagent Customization Depth Each subagent can specify its own model type, reasoning effort, sandbox config, and tools. Unspecified settings inherit from main Codex session. Notably, enabling web search for main session automatically grants this capability to subagents. Such inheritance mechanisms reduce configuration overhead while maintaining customization potential for complex projects. ## Synchronization Mechanism Main agent waits for all subagents before synthesizing results. In our case, it weighted transport convenience (30%), budget (40%), and experience quality (30%) to recommend Lisbon. Codex's ability to resolve conflicting expert opinions through configurable weighting demonstrates advanced multi-criteria decision making capabilities surpassing simple report concatenation. ## Real-World Applications The subagent pattern could revolutionize complex workflows requiring multiple expertise domains. Financial modeling, medical diagnosis, or engineering simulations could each utilize specialized subagents. By 2026, Codex's subagent approach may improve decision-making efficiency by up to 60% in AI-assisted processes, particularly where tradeoffs between competing priorities exist. ## Sources covering this story - https://towardsdatascience.com/from-one-agent-to-a-team-understanding-codex-subagents/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._