This post was co-authored with Rafael Guedes.
The rise of generative AI is the new platform shift of the digital era. It solves problems ranging from automation in large enterprises to various types of R&D and creativity. The global market is projected to surpass $65 billion in 2024, and 86% of IT leaders anticipate large organizational changes [1]. So far, the biggest returns are from chatbots (the more generic and abundant use case), code copilots, and enterprise search.
Investment continues to flow into AI, with $13.8 billion invested in 2024 (a sixfold increase from 2023) [1]. Besides, businesses are embedding AI into their core strategies and systems. Technologies like retrieval-augmented generation (RAG), fine-tuning, and specialized models for vertical applications (e.g., healthcare, legal) are becoming mainstream.
Large Language Models (LLMs) have brought attention to AI (in several ways) and opened the door to new ways of solving old problems. This new way is through Agentic AI — a framework where autonomous agents work collaboratively to execute complex, multi-step workflows.
Our demo shows how you can work and develop a multi-agent system. It integrates three specialized agents:
These agents operate within a structured workflow. They leverage foundational LLMs and existing tools in everyday enterprise stacks. We show how organizations can streamline tasks, reduce human effort, and enhance output quality — all while maintaining adaptability to complex scenarios.
As always, the code is available on our GitHub.
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