Autonomous agents are evolving into persistent, goal-oriented systems that reason, plan, coordinate, and operate under real-world constraints.

But agents fail for architectural reasons- not model limitations.

This session examines the design patterns behind modern autonomous agent systems, from single-agent loops to coordinated multi-agent architectures.

We will explore:
- ReAct-style reasoning–acting loops and their limitations
- Planning–execution–reflection architectures
- Manager–worker and hierarchical agent patterns
- State machine workflows (LangGraph-style architectures)
- Memory systems: working, episodic, and semantic integration
- Tool orchestration and learned tool selection
- Constraint-aware optimization across latency, cost, and token budgets

Rather than focusing on a single framework, this talk synthesizes architectural principles emerging across research and production systems including neuro-symbolic multi-agent approaches, reflection-based improvement loops, and resource-constrained coordination strategies.

We will examine architectural trade-offs between specialization and coordination overhead, autonomy and constraint enforcement, and simplicity versus hierarchical decomposition.

This session is for engineers building complex agent workflows, multi-agent systems, or long-running autonomous processes.

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Supporting Link:
1. https://medium.com/@himanshusangshetty/state-of-the-art-autonomous-agent-architecture-design-patterns-and-best-practices-f456addd9f07