Graphs and Finite State Machines

We use graph structures and finite state machines to model agent behavior, decision flow, and recovery logic in complex multi-agent systems. This approach enables deterministic, auditable, and flexible execution flows.

Why Graphs and FSMs?

While LLMs can generate contextually rich responses, they lack built-in state awareness or planning memory. Graphs and FSMs provide:

Deterministic execution flows
Recovery checkpoints
Branching logic with conditional guards
Transparent and auditable decision paths

They allow agents to operate not just intelligently - but predictably and safely.

Key Concepts

Finite State Machines (FSMs)

FSMs are ideal for stepwise logic. They define:

States (e.g. idle, plan, act, verify)
Transitions between states
Guards (conditions that control transitions)
Entry/exit hooks (for validation, logging, etc.)

Use cases:

Prompt validation
Multi-phase reasoning (plan → verify)
Task lifecycle control

Directed Graphs

Directed Acyclic Graphs (DAGs) represent agent reasoning flows and delegation logic. They allow:

Parallel execution branches
Conditional backtracking and retries
Modular subtask routing

Example Flow:

Start → Plan → Research → Validate → Summarize → End
                    ↘︎ ↘︎
                  Critic  Fallback

This structure supports both forward progress and fail-safe detours.

Example: Research Pipeline Graph

    A[Start] --> B[Planner]
    B --> C[Researcher]
    C --> D[Critic]
    D --> E[Summarizer]
    D --> F[Replan?]
    F --> B
    E --> G[Finish]

This visual pipeline governs agent execution and error recovery. For example:

If the Critic flags a flaw, the agent may trigger a Replan
The flow restarts from Planner, preserving earlier context

Tools and Frameworks

We implement FSMs and DAGs with:

LangGraph – Graph-based flow control over LangChain
CrewAI – Agent team coordination with state modules
Graph compiler – Converts declarative YAML into live runtime logic
Eval hooks – Trace transitions and capture metrics for each node

Research Goals

We're actively exploring:

Self-modifying graphs: Agents restructure FSMs in real time
Heuristic triggers: Use uncertainty scores to condition transitions
State compression: Convert long history into reusable state tokens
Hybrid FSM × reactive agents: Combine strict logic with real-time adaptability

Why It Matters

In complex AI systems, how decisions are made is just as important as what decisions are made. Graphs and FSMs give us:

Simulation and testing for agent behavior
Visual maps of reasoning paths
Debugging tools for misaligned outputs -Guardrails for safe and repeatable execution