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ToolReference
AI-Agents
by GURPREETKAURJETHRA
Reference patterns for multi-agent coordination and delegation
Jupyter Notebook
Updated May 7, 2024
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Overview
Documents design patterns for building multi-agent systems across frameworks like Autogen, LangGraph, Taskweaver, and Crewai. Presents Market-Based Coordination Pattern, Chain of Thought Pattern, and example flows (in a Jupyter notebook) to help architects map capabilities between agent frameworks. Highlights coordination, delegation, and interaction styles so practitioners can adapt patterns to their stack.
Key Benefits
As multi-agent deployments grow, reproducible patterns let teams reason about trust, AI Governance, delegation, and failure modes rather than reinventing coordination logic. Until now, most guidance lived in fragmented demos — this reference centralizes patterns that matter for agent-to-agent evaluation and operational reliability. That makes it easier to design systems where agent track records and interaction choices are visible and testable.
Ideal For
Engineers and architects comparing multi-agent frameworks or prototyping agent orchestration and delegation strategies, including Sub-Agent Delegation Pattern.
Applications
- Compare coordination and delegation patterns when porting workflows between autogen, langgraph, and crewai
- Design agent pipelines with explicit handoff and failure-mode handling for improved agent reliability
- Document interaction styles and trust signals to inform A2A evaluation or an agent reputation model
Works With
autogenlanggraphtaskweavercrewailangchain
Topics
aiagentautogencrewaigenerative-ailanggraphmultiagenttaskweaver
Similar Tools
autogencrewai
Keywords
multi-agentmulti-agent orchestrationagent delegationagent-to-agent evaluation