Finite State Machines: The Unshakeable Map

Before we had LLMs, we had Finite State Machines (FSMs). An FSM is a mathematical model of computation. It is "Finite" because it has a specific, limited number of states. It can only be in one state at a time.

1. The Components of an FSM

• States: The possible situations (e.g., OPEN, CLOSED, PENDING).
• Transitions: The paths between states.
• Triggers: The events that cause a transition.
• Actions: What happens during or after a transition.

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2. Why FSMs are a Superpower for Agents

In a standard agent loop, the LLM can "forget" where it is. It might try to "Update an Order" before it has "Verified the User." In an FSM-governed agent, this is physically impossible. The code simply won't allow the transition to the UPDATE state unless the current state is VERIFIED.

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3. Visualizing a State Machine

stateDiagram-v2
    [*] --> GUEST
    GUEST --> LOGGED_IN : provide_credentials
    LOGGED_IN --> CHECKOUT : add_to_cart
    CHECKOUT --> PAID : process_payment
    PAID --> [*]
    
    LOGGED_IN --> GUEST : logout
    CHECKOUT --> LOGGED_IN : cancel

Notice:

There is no arrow from GUEST to PAID. The model cannot "Hallucinate" its way into skipping the login or checkout steps.

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4. Code Concept (Python transitions library)

You don't have to build FSMs from scratch. Libraries like transitions handle the logic for you.

from transitions import Machine

class AgentOrderFlow:
    states = ['new', 'authenticated', 'paid']
    
    def __init__(self):
        self.machine = Machine(model=self, states=AgentOrderFlow.states, initial='new')
        self.machine.add_transition('login', 'new', 'authenticated')
        self.machine.add_transition('pay', 'authenticated', 'paid')

flow = AgentOrderFlow()
# flow.pay() -> This will raise an error because we are in 'new' state!

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5. FSM vs. LangGraph

You might notice that FSMs look like LangGraph. They are cousins!

• LangGraph: Focused on "Graph" flow where the LLM decides the path.
• FSM: Focused on "Constraint" flow where the Code decides the path.

In a hybrid system, we use an FSM to restrict the LangGraph.

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Key Takeaways

• FSMs provide deterministic logic that LLMs cannot break.
• States represent Stages of a process.
• Transitions represent Legal moves between stages.
• Combining FSMs with agents creates Fail-Safe AI systems.