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Agentic AI

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Multi-Agent Platforms & Frameworks
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Basic Information

This repository collects the materials for a talk titled "Agentes inteligentes" (Agentic AI). It serves as a curated set of references and supporting material prepared by Marcelo Mendoza for a presentation on intelligent agents, multi-agent interactions, and agent-based modeling applied to social simulations. The README lists selected academic papers and references covering collaboration mechanisms among LLM agents, consensus and debate approaches, opinion dynamics simulated with networks of language-model agents, integrations of agent-based modeling with generative AI, and related methodology papers. The repository is intended to provide attendees and interested readers with a concise reading list and starting point for further study in agentic and multi-agent research.

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App URL
https://github.com/marcelomendoza/Agentic-AI
Github Repository
https://github.com/marcelomendoza/Agentic-AI/blob/main/README.md

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App Details

Features
The README contains a focused bibliography of important recent papers and preprints relevant to agentic AI and multi-agent systems. Citations include titles, author lists, publication venues or preprint identifiers, and DOIs or arXiv identifiers where present. The selection highlights topics such as collaboration mechanisms for LLM agents, round-table consensus methods, opinion dynamics in agent networks, agent-based modeling with generative AI, and systems for compiling language-model calls into pipelines. The file explicitly notes the material was prepared by Marcelo Mendoza and includes a contact email for the author. The list is described as a non-exhaustive selection of key references.
Use Cases
This repository is useful as a concise, curated entry point for researchers, students, and practitioners interested in intelligent agents and multi-agent simulations. It gathers seminal and recent works that illustrate theoretical and practical approaches to agent collaboration, consensus building among LLMs, simulation of social dynamics with language-model agents, and engineering patterns for model orchestration. Users can rely on the collection to identify influential papers to read next, to design lecture reading lists, or to situate their own work within current directions in agentic AI research. The author attribution and contact information also facilitate academic follow-up.

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Multi-Agent Platforms & Frameworks (1230)
Health & Life (957)
AI Resources (920)
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