What is the Spring AI Agentic pattern and how does it differ from a single-turn chat call?

An agent in the context of Spring AI is an autonomous loop where an LLM iteratively reasons, selects tools, executes them, incorporates results, and reasons again until it can produce a final answer — all without a human in the loop for each step. This contrasts with a single-turn chat call, which is a one-shot request-response with no iteration.

The simplest agentic pattern is the ReAct loop (Reason + Act): the model receives a task, reasons about which tool to use, Spring AI executes that tool, the result is fed back, and the model reasons again with the new information. This repeats until the model decides it has enough to answer.

Spring AI supports this naturally through its function-calling mechanism. When a model response contains a tool call, Spring AI executes the registered function and re-calls the model with the result. If the model needs to call multiple tools in sequence, this loop repeats automatically.

// The model will autonomously chain tool calls if needed
String answer = chatClient.prompt()
    .user("Find the current price of Spring Boot on Maven Central and compare it to last week.")
    .tools("mavenSearch", "priceHistory")  // model picks which tools and when
    .call().content();

For more complex agents with explicit planning, parallel tool calls, or multi-agent coordination, Spring AI integrates with Spring AI Agentic Frameworks (e.g. LangGraph4j bindings) or custom orchestration using the low-level ChatModel in a manual loop. Key production concerns for agents include: limiting maximum tool-call iterations to prevent infinite loops, timeout handling per tool, and logging every decision step for auditability.

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