Retries on AI VOID

Mastering Basic Workflows: Events, Tasks, and Retries

Wed, 20 May 2026 00:00:00 +0000

Welcome back! In the previous chapter, we successfully set up our Trigger.dev project, getting ready to build powerful automated systems. Now, it’s time to dive into the fundamental building blocks that make Trigger.dev workflows so resilient and effective: Events, Tasks, and Retries. These three concepts are the bedrock for creating robust, automated workflows and AI agents that gracefully handle the complexities and inevitable failures of real-world production environments.

This chapter will guide you through understanding what events are, how tasks execute reliably, and how Trigger.dev automatically handles failures through intelligent retries. By the end, you’ll be able to create your first resilient workflow, capable of reacting to external signals and executing durable, fault-tolerant operations, boosting your confidence in building production-ready systems.

Building Resilient Systems: Retries, Timeouts, and Circuit Breakers

Fri, 15 May 2026 00:00:00 +0000

Distributed systems are powerful, allowing us to scale applications and handle immense loads by breaking them into smaller, interconnected services. But here’s a secret: they will fail. Networks are unreliable, services can crash, and dependencies can slow down. The real challenge isn’t preventing all failures (an impossible task), but designing systems that can tolerate failures and continue to function gracefully.

This chapter dives into three fundamental patterns that form the bedrock of resilient distributed systems: Retries, Timeouts, and Circuit Breakers. You’ll learn what each pattern is, why it’s crucial, and how to apply it effectively to build applications that can withstand the chaos of a distributed environment. We’ll also explore how these timeless principles are vital for emerging AI and agentic workflows, where interactions with external tools and models are frequent and often unpredictable.

Chapter 11: Error Handling, Robustness, and Retries

Mon, 05 Jan 2026 00:00:00 +0000

Chapter 11: Error Handling, Robustness, and Retries

Welcome back, intrepid data explorer! So far, we’ve learned how to set up LangExtract, define schemas, and perform extractions with various LLM providers. You’re getting good at asking LLMs to do your bidding!

But here’s a little secret: even the smartest LLMs and the most robust libraries aren’t perfect. In the real world, things can go wrong. Network glitches, API rate limits, unexpected model behavior, or even a moment of LLM “confusion” can lead to failed extractions or malformed output. If we’re building applications that rely on these extractions, we need them to be as reliable as possible.