Durable Execution
What infrastructure do durable AI agents need?
Durable AI agents need infrastructure for reliable execution, not just model access. Core layers include persisted workflow state, automatic recovery, replay behavior, safe retries, idempotency practices, observability, identity, access policy, and deployment controls. For enterprise environments, teams also need data-boundary choices, audit trails, and support for existing agent frameworks rather than a forced rewrite. Diagrid Catalyst is positioned as this production layer for AI agents and MCP servers: it works with frameworks such as LangGraph, CrewAI, OpenAI Agents, Google ADK, and others while adding durability, secure communication, policy, and operational visibility.
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- Durable Execution
What is durable execution in AI agent workflows?
Durable execution means an AI agent workflow can keep its progress even when a process crashes, a tool call fails, or the system restarts.
- Durable Execution
Why do production AI agents need durable workflows?
Production AI agents need durable workflows because real agent tasks rarely finish in a single clean request.
- Durable Execution
Is checkpointing enough for production AI agents?
Checkpointing helps, but it is usually not enough by itself for production AI agents. It explains the production reliability impact for AI agent workflows.