Agentic AI Patterns: From RAG to Multi-Agent Systems

Designing agentic cycles, tool use, orchestration patterns, and security guardrails to transform workflows—grounded in APIs, microservices, and governance.

Agentic AI is evolving beyond single LLM prompts into tool-using agents and multi-agent collaboration. This talk breaks down the agentic cycle (perceive, reason, act, learn), architectural patterns (single, hierarchical, collaborative), orchestration strategies, and security risks to watch.

What you’ll learn:

- Evolution: LLM → RAG → tools → agents → multi-agent systems

- Agentic cycle: perceive, reason, act, learn (PRAL)

- Architectures: single agent, hierarchical, collaborative committees

- Orchestration: sequential, concurrent, and handoff patterns

- Security risks: prompt injection/remote-exec analogs, memory poisoning/extraction

- Governance and least privilege for tokens, APIs, and data

- Why this is workflow transformation, not just task automation