Long-horizon agents and durable execution
Story 4, from Martin Fowler
Long-horizon agents automate complex, multi-step tasks that span days or weeks, using durable execution to recover from failures. This model ensures progress is preserved even if the process crashes or external systems fail.
Key points
- Long-horizon agents handle tasks that require multiple interactions with people and systems over extended periods.
- Durable execution records each step's result, allowing the program to resume from where it left off after failures.
- The agentic loop includes a goal, tools, state, and a model that selects the next action.
- Tools like Temporal and Cloudflare Workflows implement durable execution by logging steps and replaying them on restart.
- Entity workflows represent business entities like SKUs and manage long-running processes that trigger actions as needed.
Why it matters
Software engineers working with AI tools can benefit from durable execution patterns to build more reliable, long-running agents. This approach reduces the need for custom error handling and makes it easier to manage complex, real-world workflows.
Counterpoint
Durable execution requires careful design of the state and tools, and the overhead of logging and replaying steps may not be worth it for simple or short-lived tasks.
Sources
- Long-horizon agents and durable execution (Martin Fowler)
This summary was written by a language model and may contain errors. Read the original for the full story.