Discover articles on multi-agent systems and agentic workflows.
The advent of large language models (LLMs) propelled chatbots into a new era, allowing for more natural, nuanced, and coherent conversations. However, even the most sophisticated LLM-powered chatbots fundamentally remain reactive interfaces, primarily generating text. They excel at answering questions and providing information but typically struggle with tasks that require multiple steps, interaction with external systems (APIs, databases), browsing the web, or maintaining complex state over time. They are, in essence, "talkers, not doers."
Read MoreSingle, monolithic AI agents are incredibly powerful for contained tasks. An agent designed to "summarize this email" or "answer a question about this document" can achieve remarkable performance. However, this simplicity breaks down when faced with complex, multi-step business goals. A request like, “Analyze our Q3 sales report from BigQuery, identify the top three underperforming product categories, and draft a presentation for the regional sales leads,” pushes a single agent beyond its limits.
Read MoreIn the domain of Artificial Intelligence, Multi-Agent Systems (MAS) enable the decomposition of complex problems into manageable sub-problems, each handled by a specialized agent. While effective communication protocols, as discussed in our previous article, lay the groundwork, true collective intelligence emerges when agents can actively collaborate to achieve shared goals and intelligently negotiate when their individual objectives or resource demands diverge. This article delves into the fascinating world of multi-agent collaboration and negotiation, exploring the mechanisms that allow AI entities to work together harmoniously and resolve conflicts efficiently.
Read MoreIn the evolving landscape of artificial intelligence, Multi-Agent Systems (MAS) are emerging as a powerful paradigm for tackling complex problems. By distributing tasks among specialized AI agents, these systems can achieve levels of robustness and intelligence far beyond what a single, monolithic agent can offer. However, the true power of MAS lies not just in the individual capabilities of its agents, but in their ability to communicate effectively. Just as human teams rely on clear communication to coordinate efforts, AI agents need well-defined protocols to exchange information, coordinate actions, and resolve conflicts. This article explores the fundamental aspects of multi-agent communication protocols, their types, and the challenges involved in designing them.
Read MoreIndividual AI agents, equipped with planning capabilities and tool-use (as discussed in previous articles), are remarkably powerful. They can browse the web, execute code, and query databases to achieve complex goals. However, just like a lone human genius, a single, monolithic agent eventually hits limitations when faced with truly open-ended, multi-faceted problems like developing an entire new software feature or conducting a multi-stage research project. A single agent can suffer from: Scope Overload, Lack of Perspective, Context Window Limits. The core engineering problem is: How can we enable AI to tackle challenges that require diverse expertise, iterative refinement, and validation, mirroring the collaborative power of human teams?
Read MoreLarge Language Models (LLMs) have captivated the world with their ability to generate fluent, coherent, and often creative text. They can summarize articles, write code, and engage in sophisticated conversations. However, despite their linguistic prowess, early LLMs often struggled with complex, multi-step reasoning tasks. When faced with mathematical word problems, logical puzzles, or multi-hop questions requiring a sequence of deductions, they frequently jumped directly to an incorrect answer, lacking the ability to break down the problem into intermediate steps.
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