Temporal vs. Diagrid Catalyst: Which Durable Execution Approach Fits a Long-Running AI Research Agent?

Getting a research agent to call three tools in a demo is easy. The hard part starts when the seventh tool call times out and the first six have already spent money and changed state somewhere. So this is a question about recovery, not about AI frameworks. Temporal and Diagrid Catalyst both do durable execution, and both position themselves for AI workloads. What separates them is what each one asks you to build and operate around your agent. Start with the failure contract Say your agent searches internal documents, calls an external research API, asks an LLM to synthesize the evidence, and then waits for a human to approve the result. Before that design goes to production, you need answers to four questions: Which completed steps will not run again after a crash? How do you stop non-idempotent tool calls from firing twice? Can the agent wait for hours without holding a process open? Can an operator reconstruct what the agent did, and why? Temporal treats failure-prone work as activities, coordinated by durable workflows. It persists workflow state and rebuilds it by replaying history. You can self-host Temporal or use Temporal Cloud, and Temporal's current materials cover agentic applications and framework integrations directly. Catalyst builds on Dapr Workflows. Your agent runs as a durable workflow, and Catalyst ships runners for established agent frameworks. Diagrid's documentation describes a shared runtime layer that handles durability, workload identity, policy enforcement, and operational visibility across agents, workflows, MCP servers, and applications. Where the approaches diverge Temporal fits when you want your application logic written in its workflow-and-activity model. The conceptual model is mature, language support is broad, and there is a large body of distributed-systems guidance to learn from. The cost is fluency. Someone on your team has to understand Temporal's execution semantics well enough to reason about replay, and you still decide separ