One AI with many roles is limited. Multiple specialized agents — researcher, analyst, writer, reviewer — collaborating like a human team? That's a system.
A single AI agent trying to research, analyze, write, and review its own work is like one person doing every job in a company. It works for small tasks, but for complex projects, specialization and collaboration produce far better results.
Multi-Agent Compositions assign different Level 2 patterns to different agents. A researcher agent uses ReAct with RAG to gather information. An analyst uses Tree-of-Thoughts to explore implications. A writer uses Skeleton-of-Thought for structured output. A reviewer uses Chain-of-Verification for quality control. Then these agents are coordinated through defined communication patterns — just like a human team.
Each agent is powered by Level 2 compositions:
ReAct RAG Patterns Skeleton-of-Thought Meta-Prompting Reflexion Self-ConsistencyThe magic is not in any single pattern but in how specialized agents coordinate — each bringing the right capability at the right time.
Each agent completes its work and passes the result to the next. Like an assembly line — clear handoff points, predictable flow.
A supervisor agent decomposes the task and delegates to workers. It decides who to call, what to ask, and how to combine results.
Two advocates argue different positions across multiple rounds. A judge evaluates the debate and renders a verdict. Diverse reasoning reduces blind spots.
Multiple agents solve the same task independently using different patterns. Their answers are combined by voting or synthesis. Independence improves reliability.
Gathers info from tools and knowledge bases
Explores implications through branching reasoning
Produces structured, organized output
Checks facts, catches errors, ensures quality
Orchestrates workers, decides delegation
Provides constructive feedback for improvement
A research report pipeline using the sequential topology.
Specialization works for the same reason it works in human teams: each agent can be optimized for its specific task. A researcher agent's prompt is tuned for thoroughness and source finding. A reviewer's prompt is tuned for skepticism and error detection. These are fundamentally different cognitive modes.
The communication topology adds a second layer of value. In a pipeline, each stage refines the previous. In a debate, adversarial pressure catches blind spots neither side would find alone. In an ensemble, independent approaches provide robustness against any single method's weaknesses.
Give each agent a specialized role and the right technique for that role. Connect them through pipelines, hierarchies, debates, or ensembles. The team is greater than the sum of its parts.
Each agent in a multi-agent system can internally run a Cognitive Loop. For tool-heavy tasks, agents can delegate to JARVIS. For hard reasoning within a single agent, LATS can plug in. The Adaptive Pattern Router can choose which topology to use based on the task.
At Level 4, Federated Agent Networks extend this concept to distributed systems where agents collaborate without central control, and Collective Intelligence patterns emerge from large-scale agent interaction.