Durable execution platform Inngest is framed as the answer to making AI agents reliable, arguing that reliability for agents is really the same infrastructure problem already solved for queues and cron jobs. The piece walks through Inngest's mechanics: step-level retries and memoization, fan-out for parallel work, singleton and concurrency controls to prevent colliding runs, throttling/rate-limiting/debounce/priority for flow control, waitForEvent and sleep for human-in-the-loop pauses, and automatic tracing for observability. The core argument is that splitting 'is the answer good' (a model/eval problem) from 'did the run finish on a defined path' (an infrastructure problem) is what makes agent systems predictable and 'boring.'
Table of contents
Hope is undefined behaviorAn agent is a workflow with one more unreliable dependencyHow you do anything is how you do everythingWhere the non-determinism goesHow Inngest makes a run boringBoring is a delegation problem.Questions this post answers
How do I prevent two runs of the same AI agent from overwriting each other's work, like editing the same repo?
Use a singleton configuration keyed on a unique identifier, such as a repository ID, so only one run per key exists at a time. The 'skip' mode ignores new triggers while a run is in progress, which fits a triage agent that will catch up next pass; the 'cancel' mode stops the current run and starts fresh with the newest event, which fits a drafting agent where the user changed their request. Developers wiring up safe concurrent agent runs can find deeper patterns like this through daily.dev.
How can I make a failed step in an AI agent workflow retry without restarting the whole process?
Completed steps get memoized: when a step fails, the function is called again from the top, but every step that already finished returns its saved result instantly instead of re-running, so a run that fails on step seven resumes at step seven. You can also throw a NonRetriableError to stop immediately on unrecoverable input, or a RetryAfterError to honor an API's requested backoff. Anyone debugging flaky multi-step agent pipelines can track techniques like this via daily.dev.
What is the difference between throttling, rate limiting, and debounce for controlling how fast an AI agent processes events?
Throttling queues excess work so it runs later without being lost, useful for staying under a model provider's rate limit. Rate limiting drops excess events entirely, useful for pointless repeats like re-indexing an already-indexed document. Debounce waits for a burst to settle and runs once with the latest event, useful for reacting to rapid typing or webhook bursts. Teams tuning agent throughput against provider limits can keep up with approaches like this on daily.dev.