Build AI Agents as Teams of Specialized Roles
Complex tasks need agent teams with roles like doers, planners, critics, and supervisors—mirroring human teams—to outperform single LLMs. Optimize via prompting, model selection, tuning, and context.
Core Roles That Divide Complex Tasks
AI agents tackle tasks beyond a single LLM's pretrained knowledge by assembling subagents with distinct roles, akin to human teams. Start with a doer for execution: these handle granular steps like writing code or generating app components but require oversight for full projects. Pair it with a planner that decomposes user input into steps—e.g., for mobile app development, first outline user requirements, then architect the app before coding. Add a tool operator to manage APIs, Python scripts, or web services by structuring inputs and parsing outputs.
Incorporate a learner for external knowledge: it retrieves competitor app data, user trends from blogs/social media, or implements RAG for relevance filtering to inform planning/execution. Use a critic for quality: review outputs for hallucinations, run QA tests on code, or score competing doer outputs to select the best. A supervisor monitors at task/project levels, detecting stalls and rerouting. Finally, a presenter synthesizes results—e.g., summarizing requirements, code functionality, and deployment for the user.
Popular combos like ReAct combine action (tool operator), reasoning (planner), observation (critic), and answer (presenter) for simple loops, but scale by adding roles for consistency across varied tasks via deeper planning and internal feedback.
4 Ways to Sharpen Each Subagent's Skills
Make roles excel like hiring/training humans. Prompting sets clear instructions: e.g., 'If stuck, retry' for beginners, tailoring behaviors without retraining. Model selection matches strengths—use reasoning models for planners, specialized/smaller ones for doers, considering size, persona, and capabilities.
Model tuning provides few-shot examples of success/failure, building datasets for fine-tuning weights—resource-heavy due to human labeling and compute needs. Context management grants targeted access (files, DBs, APIs) without overload, like onboarding: excess distracts, precision boosts focus.
Scale Agent Teams from MVP to Robust Systems
Launch minimally like a startup: few roles (e.g., planner + doer + critic) solve simpler problems quickly. Expand to address gaps—more critics for reliability, learners for trends, supervisors for flow—yielding higher-quality outputs through specialization, competition, and loops. This mirrors team growth: fix bugs, polish, and handle complexity without single-point failures.