AI agents are rapidly moving beyond chatbots and copilots—but scaling them reliably requires more than better models. The real challenge lies in how context, tools, and governance are engineered around agents.

This session presents a practical agentic framework where multiple LLM agents collaborate through LLM Councils, operate with domain-specific context powered by multiple RAG strategies, and use agentic tools instead of static APIs. We’ll explore how agents transition from reactive responders to proactive problem-solvers, and how these systems elevate human intelligence rather than replace it.

The talk focuses on real design patterns: context as a versioned artifact, agent-to-agent validation, bounded autonomy, and framework-level guardrails that significantly reduce hallucinations, security risks, and jailbreak attempts. Attendees will leave with a clear mental model for building AI-native systems that scale across teams while preserving quality, trust, and engineering discipline.