Picture a Java developer shipping an AI agent into production. The agent gets a prompt and starts working through a task, 50 steps, each one a call to a model like OpenAI or Anthropic, a database write, maybe a few dollars of compute. At step 49 of 50, something fails: a bug, a network blip, an AWS outage, a Kubernetes scale-down mid-run. What happens next, according to Yaron Schneider, CTO and co-founder of Diagrid, is the part nobody budgets for. "The agent is going to need to pick back up all the way from the beginning, and you're going to be paying for all of those LLM calls right from the very beginning," he said. "Which is a great way to go bankrupt."
That failure mode is what Diagrid's new integration, diagrid-spring-ai, is built to close. Announced this week, it brings durable execution to Spring AI, the framework Java developers already use to build agents, without asking them to rethink how they write one.
The same 40-year-old problem, with worse consequences
Schneider doesn't frame this as a new category of failure. It's the same reliability problem distributed systems have always had. "The exact same problems that made software unreliable for the past 40 years are still true for AI agents," he said. "Only with AI agents, the consequences are much bigger." A crashed script used to mean lost time. A crashed agent means lost time and a re-billed stack of model calls, at a cost that scales with however many steps the agent had already completed.
Schneider said the companies Diagrid works with see failures on roughly 30% of production agent runs, on average, not rare edge cases, but a routine cost of running agents at scale.
Zero code changes, by design
The integration's core pitch is unusually low-friction for infrastructure-level tooling. "With Spring AI, literally nothing," Schneider said, when asked what a developer has to change to adopt it. "All you need to do is pull in the package. There is zero code changes. You do not need to recompile your code." Underneath, the integration runs on Dapr Workflow, the open-source project Schneider originally built at Microsoft and later spun into Diagrid, now used by more than 30,000 companies, including over half the Fortune 500 and, notably, NASA.
How it actually avoids re-running side effects
The obvious follow-up question for anyone who's built distributed systems: how do you resume an interrupted agent without re-executing a step that already sent an email or wrote to a database? Diagrid's answer is an immutable ledger. "We keep an immutable record of everything that the agent did. We're essentially journaling," Schneider said. When an agent fails mid-run, the platform reads the journal, recovers the state, exactly what was completed, its inputs, outputs, and memory, and resumes from that point rather than replaying anything already finished.
Schneider was direct about the confidence customers should have in that recovery: "If they're using our technology, they know for 100%, for a fact, that we will always resume where they left off mid-run." He added that customers can and do test this themselves, running failure simulations in their own environments to verify the behavior before trusting it in production.
Why this isn't just a Spring AI feature
A natural question is why durable execution lives as a separate integration rather than something Spring AI ships with directly. Schneider's answer comes down to what a workflow engine actually requires. "Workflow engines are incredibly hard to get right. They require their own separate infrastructure, and Spring is just not that," he said. Bundling a database and deployment model into a lightweight developer framework would hurt adoption, not help it. Building that infrastructure in-house isn't a reasonable alternative either: Schneider pointed to Microsoft's own investment, a team of 15 engineers spending more than $10 million a year maintaining its workflow engine, as a sense of the actual cost most teams would take on trying to solve this themselves.
The claim Schneider thinks is overrated
Asked what's overstated in the broader AI agent conversation right now, Schneider didn't hesitate: model accuracy. "Models are going to continue getting better. They're going to continue being cheaper," he said. "I see a lot of emphasis being put on that instead of, you know, how do you orchestrate multiple agents needing to solve a task and all of them failing at the same time." In his view, the infrastructure problem, keeping agents reliable when things break, is a bigger and less-discussed risk than whatever incremental accuracy gain the next model release brings.
Beyond Spring
diagrid-spring-ai is one piece of a broader set of integrations Diagrid maintains across roughly ten agent frameworks spanning Python, C#, and Java, including LangChain, LangGraph, and CrewAI. Developers can try the platform directly at diagrid.io, where Schneider said quick-start guides let teams test the failure-recovery behavior themselves before committing to it in production.
