Everyone is building AI agents, but how do you design Agentic AI Systems that are truly reliable in the real-world?

Agentic AI systems can plan tasks, use tools, reflect on results, and even collaborate with other agents. But building them at scale brings challenges:

• Choosing the right agent architecture
• Handling memory & context efficiently
• Reducing latency
• Monitoring and evaluating agents effectively

This session draws from my personal experience building and deploying Agentic AI systems over the past year. We’ll focus on three pillars: Architecting, Optimizing, and Observability for Agentic AI Systems.

Agenda

1. Introduction

• What are AI Agents?
• Common challenges in building Agentic AI Systems
• AI Agents vs. AI Workflows

2. Architecting Effective Agentic AI Systems

• Popular Tools & Frameworks - key players and my recommendations
• Why LangGraph Matters - benefits for building AI agents
• Agent Design Patterns - tool use, planning, reflection, multi-agent (with practical recommendations)
• Single-Agent vs. Multi-Agent Systems - real-world hands-on example & recommendations

3. Optimizing Agentic AI Systems

• Context Engineering - what it is and popular approaches
• Agentic RAG - integrating RAG with agents
• Router Agentic RAG - real-world hands-on example
• MCP & A2A - separating hype from value
• Proven Multi-Server MCP Architecture - real-world hands-on example
• Memory Management - long-term vs. short-term
• Tools & Frameworks for Memory - key players
• Memory Context Engineering - hands-on examples for Agentic AI

4. Observability (Monitoring & Evaluation) for Agentic AI Systems

• Agent Observability - what it is and why it matters
• Observability Tools & Frameworks - key players
• Monitoring Metrics - token usage, latency, cost, tool calls, errors, etc.
• Evaluation Metrics - goal accuracy, reasoning quality, trajectory accuracy, etc.
• Hands-On Monitoring - tracing and dashboarding agent behavior
• Hands-On Evaluation - building datasets and running evaluations with metrics

Throughout the Session

• Best practices and caveats for real-world readiness
• Hands-on code demos using LangGraph, FastMCP, LangMem, and LangSmith