Java & AI: What Developers Need to Know

Stop the ReAct Chaos: Building Deterministic Multi-Agent Cycles with Spring AI Graph

If you are still letting LLMs freely decide their next execution step in an unconstrained ReAct loop, you are burning cloud budget on infinite loops and non-deterministic failures. In 2026, enterprise-grade AI requires the strict guardrails of stateful, cyclic graphs where transitions are governed by code, not LLM vibes.

Why Most Developers Get This Wrong

• Naive ReAct Loops: Relying entirely on prompt-based tool calling to determine flow, which inevitably derails after 3-4 turns.


• Stateless Agents: Passing massive, unmanaged chat histories back and forth instead of maintaining a single, thread-safe state object.


• Lack of Edge Controls: Failing to hardcode conditional transitions, letting the LLM hallucinate its way into non-existent API endpoints.

The Right Way

The solution is to model your multi-agent system as a deterministic, cyclic graph where the LLM only executes node-level tasks, while Java code controls the state transitions.

• Define an Immutable State: Use Java record types to represent the thread-safe state passed between nodes.


• Explicit Nodes and Edges: Map agents (e.g., Writer, Critic) to discrete nodes and use conditional routers to decide the next transition.


• Spring AI Graph API: Leverage Spring AI 1.2.0's StatefulGraph to manage state persistence and concurrent transitions out-of-the-box.


• Model Specialization: Use fast, cheap models (like Llama 3.3) for routing decisions, and reasoning models (like Claude 3.5 Sonnet) only for complex node tasks.

Show Me The Code (or Example)

// Define stateful graph with immutable State record

var workflow = new StatefulGraph<AgentState>()

    .addNode("writer", state -> writerAgent.call(state))

    .addNode("critic", state -> criticAgent.call(state))

    .addEdge(START, "writer")

    .addEdge("writer", "critic")

    .addConditionalEdge("critic", state -> {

        return state.isApproved() ? END : "writer"; // Deterministic cycle

    });



AgentState finalState = workflow.compile().execute(new AgentState("Draft context"));

Key Takeaways

• Control the Flow, Not the Agent: Keep LLMs inside the nodes; your Java code must own the edges.


• Thread-Safety is Native: Spring AI Graph manages state transitions safely across concurrent JVM threads, eliminating manual synchronization.


• Fewer Tokens, Better Accuracy: Deterministic routing cuts out the prompt overhead of ReAct, saving up to 40% in token costs.

Heads up: if you want to see these patterns applied to real interview problems, javalld.com has full machine coding solutions with traces.

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