Trigger.dev

Trigger.dev is an open-source background job platform that has become increasingly popular for running AI agent workloads. It provides the infrastructure for executing long-running tasks — exactly the kind of work AI agents do — with built-in retries, scheduling, concurrency control, and observability, all deployable as serverless functions.

The platform solves a fundamental problem in agent deployment: AI agent tasks often take seconds to minutes (or even hours for complex workflows), far exceeding typical serverless function timeouts and HTTP request limits. Trigger.dev provides long-running execution environments with configurable timeouts up to hours, automatic retries with exponential backoff, and real-time status updates.

Tasks are defined as TypeScript functions with decorators that specify retry behavior, timeout limits, concurrency settings, and scheduling. The developer experience is excellent — write your agent logic as a normal function, and Trigger.dev handles the infrastructure complexity of reliable background execution.

For AI agent use cases, Trigger.dev offers several critical features: task queuing for handling bursts of agent requests, fan-out/fan-in patterns for parallel agent execution, scheduled triggers for recurring agent tasks, and webhook triggers for event-driven agent activation. The platform includes built-in integrations with popular services and APIs.

The observability dashboard shows real-time task execution with detailed traces, making it easy to debug agent workflows. You can see exactly what each task did, how long each step took, and where failures occurred. This is essential for production agent systems where understanding execution flow is critical.

Trigger.dev offers both a cloud-hosted version and self-hosted deployment via Docker. The cloud version provides managed infrastructure with generous free tiers, while self-hosting gives full data control.

The platform has found strong adoption in AI agent deployments because it addresses the gap between 'my agent works in a notebook' and 'my agent runs reliably in production.' It handles the infrastructure concerns — execution duration, retries, scheduling, scaling, monitoring — that are orthogonal to agent logic but essential for production reliability.