AI Agent Teams: Roles Like Doers, Planners, Critics
Build AI agents for complex tasks by assigning specialized subagent roles—doers for execution, planners for breakdown, critics for feedback—like human teams, then optimize via prompting, model selection, tuning, and context.
Core Roles Mirror Human Teams for Complex Tasks
AI agents tackle problems beyond single LLMs by dividing labor into subagents with distinct roles, just as human teams do for projects like mobile app development. Start with a doer for granular actions like coding individual steps. Add a planner to decompose user input into requirements and architecture plans, identifying needed skills. Include a tool operator for API calls, Python snippets, or web services with structured inputs/outputs. A learner pulls external data via RAG or rules-based retrieval, like competitor app features from blogs/social media, to inform planning. Deploy a critic for blunt feedback: hallucination checks, QA tests, or scoring rival outputs for the best one. Use a supervisor to monitor progress at task/project levels, unsticking stalled steps. End with a presenter to synthesize outputs, summarizing requirements, code, and results for users.
These roles scale from simple to robust: tool operators and learners often chain LLM calls with tools/retrieval, forming standalone agents themselves.
ReAct Pattern as Starter Team, Expand for Reliability
Combine roles into proven patterns like ReAct: reason (planner breaks down tasks), act (tool operator executes), observe (critic feedbacks), yielding a final answer (presenter). This handles basic loops but falters on diverse/complex tasks. Scale by adding roles for deeper planning, precise execution, and internal feedback, boosting output quality—like growing a startup team to fix bugs and polish products.
Optimize Roles with Prompting, Models, Tuning, Context
Excel roles via four levers: (1) Prompting gives clear instructions, e.g., 'retry if stuck,' mirroring human guidance. (2) Model selection matches role needs—specialization, size, reasoning ability, persona (e.g., analytical critic). (3) Model tuning feeds good/bad examples to fine-tune weights, but demands datasets and compute. (4) Context provides targeted access (files, DBs, APIs) without overload, like onboarding humans. Begin lean with 2-3 roles for quick prototypes, then expand to cover weaknesses.