Every organization has the same problem: critical information is scattered across PDFs, slide decks, and meeting notes. Turning that into structured, decision-ready documents is manual, repetitive, and doesn't scale.
I built an AI agent that automates that process with 15 specialized sub-agents, each responsible for a specific part of the output. They read source documents, extract what matters, write structured sections, and cite their sources. A verification layer ensures every agent actually delivered — not just claimed it did. Post-processing removes redundancy across sections.
What you will learn from this session:
* Beyond the Single Prompt: How to design a multi-agent system where each agent has a specific role (extraction, writing, verification).
* Reliability & Verification: How to implement a "verification layer" to ensure agents actually deliver facts, not hallucinations.
* Production-Grade Architecture: Master the "Ops" side of GenAI — cost predictability, prompt caching, and token-level tracking.
* Traceability: Techniques to link every AI claim back to its original source page for total transparency.
* Business Impact: How to pitch a solution that reduces hours of manual labor into minutes of automated, high-quality output.
