Long-horizon agents and durable execution
AI Summary
Long-horizon agents change enterprise economics by automating multi-step, unpredictable tasks that take days or weeks. Using an agentic loop, these systems interact with external tools, wait for human approvals, and handle long delays. Because these agents operate like distributed systems subject to crashes, network failures, and state drift, they require durable execution runtimes like Temporal or Cloudflare Workflows to record steps, handle retries, and resume safely after interruptions.
Key Takeaways
- Long-horizon agents execute multi-turn loops over days or weeks, managing tasks like stock replenishment and customer onboarding.
- Durable execution runtimes record steps interacting with the outside world, enabling programs to rebuild state and skip completed steps after a crash.
- Business rules should live in deterministic code rather than prompts, such as restricting tool availability based on the current state.
Why it matters
Understanding how to combine AI models with durable execution is critical for engineers building reliable enterprise automation. Without durable runtimes and proper harness architecture, long-horizon agents fail silently during extended wait times or unpredictable real-world interactions.
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