Session brief
Building Multi agent Systems with Finite State Machines
Overview
This talk explores how finite state machines (FSMs) can provide a structured and reliable foundation for building complex AI systems, particularly agentic AI. While AI intelligence is advancing, the critical need for predictability, observability, and control in orchestrating autonomous agents is paramount. FSMs, and their extension state charts, offer a robust framework for governing agent decision-making and behavior, complementing the dynamic intelligence of large language models (LLMs).
Who should watch
- AI Engineers building autonomous agents
- Product Managers seeking to add predictability to AI features
- Developers looking for structured approaches to complex system design
- Anyone interested in AI governance and control mechanisms
- Builders facing challenges with LLM reliability and traceability
Key takeaways
- Finite State Machines (FSMs) and State Charts offer predictability, traceability, auditability, and reliability, addressing key weaknesses of LLMs.
- Actors, through message passing and internal state management, provide a framework for concurrent, distributed, and encapsulated execution, complementing FSMs.
- Combining LLMs with FSMs and actors allows systems to leverage the strengths of each component: LLMs for dynamic intelligence, FSMs for structure, and actors for communication.
- Patterns like tool use, human-in-the-loop approvals, feedback iteration between agents, and collaborative multi-agent workflows can be effectively implemented using FSMs.
- Agentic orchestration allows an LLM to dynamically direct the sequence of states, while agentic chartering enables an LLM to generate entirely new processes for achieving goals.
- The development of agentic systems can be made more manageable and scalable by composing specialized units, akin to building with Legos, with FSMs playing a foundational role.
- *The advantages brought by state machines are everything that LLMs are not.*
- *By combining LLMs and state machines together we can build a system that leverages the strengths of one to mitigate the weaknesses of the other.*
Notable quotes
The speaker suggests that FSMs may have a foundational role in building platforms that effectively govern agent decision-making and behavior.
*The advantages brought by state machines are everything that LLMs are not.*
*By combining LLMs and state machines together we can build a system that leverages the strengths of one to mitigate the weaknesses of the other.*
Unofficial community note. Prefer the recording for nuance.