AI agents are no longer just thinking systems — they are becoming context-rich, tool-aware, and decision-oriented collaborators.
The real shift is not better models, but how context, retrieval, tools, and governance are structured around agents.

In this talk, we explore a practical agentic framework where:
Design and scale agentic AI frameworks where multiple LLM agents, governed by councils, domain-specific context, and agentic tools, work together to augment human intelligence—while maintaining quality, security, and enterprise-grade guardrails.

LLM agents collaborate through an LLM Council (multi-agent review, validation, and arbitration)

Each agent operates with domain-specific context powered by 16 types of RAG

Traditional MCP tools evolve into Agentic Tools with intent, memory, and policy awareness

Quality, security, and jailbreak resistance are enforced at the framework level, not bolted on later

We’ll show how agentic systems move from reactive question-answering to proactive problem-solving, and how this elevates—not replaces—human intelligence in real engineering workflows.