As AI systems evolve beyond basic conversational bots, engineering teams face the challenge of architecting autonomous agents that can reliably handle complex, multi-step workflows. In this session, we will explore a practical, architecture-first approach to designing production-grade autonomous agents. We will examine how agents reason, plan, retain memory, and collaborate to execute sophisticated tasks. We will walk through the trade-offs between single-agent, multi-agent, and hybrid deterministic workflows, showing how to connect models with enterprise APIs, persistent state, and distributed data systems. Drawing from real-world implementations, we will cover critical considerations for scalability, observability, fault tolerance, and security across distributed environments.

What You Will Learn:

· Architectural Trade-offs: When to deploy single-agent, multi-agent, or deterministic orchestration patterns for complex tasks.

· State & Memory Management: How to design robust agent tools, persistent memory, and dynamic context retrieval.

· Enterprise Integration: Patterns for securely connecting agents to backend APIs, structured databases, and external services.

· Production Engineering: Concrete strategies for observability, latency control, cost optimization, and error handling in mission-critical environments.

· Avoiding Pitfalls: Real-world lessons on mitigating infinite execution loops, tool misuse, and non-deterministic behavior.

Key Takeaway: You will leave with an end-to-end framework for turning complex engineering workflows into reliable, well-architected autonomous systems that deliver measurable productivity gains.