Stop Letting AI Agents Break Your Database: Transactional Multi-Agent Workflows with Temporal and Spring AI
In 2026, AI agents are no longer just glorified chatbots summarizing PDFs; they are executing real-world financial transactions, booking flights, and mutating production databases. But when an LLM tool call succeeds and the subsequent step fails due to a rate limit or a hallucinated parameter, you cannot just throw a 500 Internal Server Error and leave your database in an inconsistent state.
Why Most Developers Get This Wrong
Relying on @Transactional: Standard database transactions completely fail when dealing with asynchronous, non-blocking, and external LLM API calls.
Trusting LLMs to "Self-Correct": Believing that a Claude 3.5 or GPT-4o agent can reliably invoke its own "undo" tools when a downstream system fails is a recipe for data corruption.
