Running an AI agent in production is hard. Running AI agents, in production, across multiple functions, that need to continuously communicate with each other is the challenge ZyG has taken on.  

ZyG builds scale for DTC brands end to end, agentically, from building the online storefront to generating ad creatives, running user acquisition, handling organic growth, running customer service, and optimizing logistics. And to build scale, agents need to continuously communicate so that signals from one point in the customer journey (UA, for example) inform all other parts of the journey (creatives and customer support, for example) 

In the real world, where real money is on the line, the “happy path” is the rare reality. These are long-running workflows where everything from multiple systems failures to LLM providers that return a 429 get in the way. What happens in reality: 

• A campaign loop runs for days, waiting on performance data that lands in minutes or in days out of order, revised, or not at all.

• Asset generation that chains together model calls, human reviews and the interaction between them suffer from failed calls, batches that are partially complete, and reviewers that respond days later.

• Building a storefront in hours with catalog imports, theme generation, and partner callbacks is straightforward, but surviving every deploy that lands mid-run becomes extremely complex. 

In order for us to focus on the places where ZyG can truly move the needle, we work with Temporal to ensure the most durable execution:

• A workflow can run for days, weeks, even months, outliving every deploy in between, with no checkpointing code and nobody babysitting it.

• Crashes, restarts, and rate limits all become Temporal’s problem. Work resumes where it stopped and completes exactly once, mid-run. A workflow never loses track of what it was doing for a brand.

• Waiting is free. A workflow can sit blocked on a webhook, a callback, or by a human and burn nothing - no cron, no polling, no “is it done yet” jobs.

With this reliability, we are free to focus on intelligence. Instead of building systems to survive failures, we can focus on the factors that  impact our specific business, from improving storefront performance to choosing winning creatives to extracting signals from noise across the customer journey. Those are the problems that matter to brands, and the ones we care most about solving.