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Category: Multi Agent Systems

  • Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents”

    Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” Hard-won lessons on how to scale agentic systems without scaling the chaos, including a taxonomy of core agent types. The post Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” appeared first…

  • How IntelliNode Automates Complex Workflows with Vibe Agents

    How IntelliNode Automates Complex Workflows with Vibe Agents Many AI systems focus on isolated tasks or simple prompt engineering. This approach allowed us to build interesting applications from a single prompt, but we are starting to hit a limit. Simple prompting falls short when we tackle complex AI tasks that require multiple stages or enterprise…

  • Decentralized Computation: The Hidden Principle Behind Deep Learning

    Decentralized Computation: The Hidden Principle Behind Deep Learning Most breakthroughs in deep learning — from simple neural networks to large language models — are built upon a principle that is much older than AI itself: decentralization. Instead of relying on a powerful “central planner” coordinating and commanding the behaviors of other components, modern deep-learning-based AI…

  • How Agent Handoffs Work in Multi-Agent Systems

    How Agent Handoffs Work in Multi-Agent Systems Understanding how LLM agents transfer control to each other in a multi-agent system with LangGraph The post How Agent Handoffs Work in Multi-Agent Systems appeared first on Towards Data Science. Kenneth Leung Go to original source

  • Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It

    Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It A real-world analysis of why CrewAI’s hierarchical orchestration misfires—and a practical fix you can implement today. The post Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It appeared first on Towards Data Science. Partha Sarkar Go to original source

  • Build Multi-Agent Apps with OpenAI’s Agent SDK

    Build Multi-Agent Apps with OpenAI’s Agent SDK Creating multi-agent apps is simple with this open-source SDK, and it can be used with any OpenAI-compatible LLM The post Build Multi-Agent Apps with OpenAI’s Agent SDK appeared first on Towards Data Science. Alan Jones Go to original source

  • A Multi-Agent SQL Assistant You Can Trust with Human-in-Loop Checkpoint & LLM Cost Control

    A Multi-Agent SQL Assistant You Can Trust with Human-in-Loop Checkpoint & LLM Cost Control Your very own SQL assistant built with Streamlit, SQLite, & CrewAI The post A Multi-Agent SQL Assistant You Can Trust with Human-in-Loop Checkpoint & LLM Cost Control appeared first on Towards Data Science. Alle Sravani Go to original source

  • Agentic AI 103: Building Multi-Agent Teams

    Agentic AI 103: Building Multi-Agent Teams Build multi-agent teams that can automate tasks and enhance productivity. The post Agentic AI 103: Building Multi-Agent Teams appeared first on Towards Data Science. Gustavo Santos Go to original source

  • Multi-Agent Communication with the A2A Python SDK

    Multi-Agent Communication with the A2A Python SDK The Agent Card helps discover agents, but how does communication between agents actually work in practice? The post Multi-Agent Communication with the A2A Python SDK appeared first on Towards Data Science. Deborah Mesquita Go to original source

  • AI Agents from Zero to Hero — Part 3

    AI Agents from Zero to Hero — Part 3 Intro In Part 1 of this tutorial series, we introduced AI Agents, autonomous programs that perform tasks, make decisions, and communicate with others.  In Part 2 of this tutorial series, we understood how to make the Agent try and retry until the task is completed through…

  • Supercharge Your RAG with Multi-Agent Self-RAG

    Supercharge Your RAG with Multi-Agent Self-RAG Introduction Many of us might have tried to build a RAG application and noticed it falls significantly short of addressing real-life needs. Why is that? It’s because many real-world problems require multiple steps of information retrieval and reasoning. We need our agent to perform those as humans normally do,…