AI-powered agents promise faster, easier software delivery, but their unpredictable behavior often makes engineers hesitant to fully trust them with critical workflows. Sam Alba, Co-founder of Dagger (and previously co-creator of Docker), explains how teams can reliably integrate agents into their delivery pipelines by shifting how they structure and manage automation.

He'll share four practical strategies learned from real-world experience:

1. Treat agents as workflow participants, not isolated tools.
Stop using agents as disconnected scripts or IDE plugins. Treating them as first-class parts of your delivery process simplifies your architecture, reduces hidden complexity, and makes agent outcomes more predictable.

2. Use many small agents instead of one big one.
Just as software evolved from monoliths to microservices, software delivery benefits from smaller, specialized agents with clearly defined responsibilities. Smaller agents are easier to understand, maintain, and integrate.

3. Define clear environments—the real lever for reliability.
Instead of chasing perfect prompts or models, focus on clearly defining the tools, resources, and permissions around your agents. Precisely controlling their environments makes agents behave consistently and reliably.

4. Design workflows for easy debugging and observability.
Agents will sometimes fail unexpectedly. Sam will share simple, effective ways to build clear tracing and observability into your workflows from the start, making debugging quicker and less frustrating.

You'll leave with practical, immediately usable techniques that give you the confidence to trust AI agents in your software delivery pipelines.