AutoGenAgents: A Practical Framework for Autonomous Generative Content Creation from Multi-Source Documents
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806012Keywords:
Generative AI, Autonomous Agents, Multi-Agent Systems, Content Automation, Document Intelligence, Natural Language Generation, Large Language Models, Knowledge Extraction, RAG, AutoGenAgents, IEEE StandardsAbstract
The exponential growth of heterogeneous documents demands automated systems that convert dispersed information into coherent content. We present AutoGenAgents, a modular, multi-agent framework combining Generative AI and autonomous agent orchestration for end-to-end content creation from multi-source documents. The framework integrates document ingestion, knowledge preprocessing, agent orchestration, and LLM-based synthesis. Specialized agents (Planner, Retriever, Summarizer, Synthesizer, Reviewer) collaborate to extract, reconcile, and generate high-quality content with minimal human oversight. We provide implementation blueprints, prompt/agent patterns, and evaluation metrics. Case studies in healthcare and finance illustrate practical gains: higher automation levels and improved throughput. The paper supplies reproducible guidance so practitioners can implement AutoGenAgents in enterprise settings.
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