Designing Bounded Agentic Workflows
Understand how to design multi-step AI workflows that involve planning, acting, observing, and deciding with proper limits. Learn to prevent error compounding by implementing step caps and clear completion signals. This lesson helps you build bounded agentic workflows that manage complex tasks reliably and avoid common engineering pitfalls.
The previous lesson gave a model one action, proposed once and confirmed once before it ran. Some tasks need more than that, checking something, deciding what it implies, acting on that decision, and possibly acting again based on what happened. That loop, plan, act, observe, decide, repeat, is a genuinely different thing from a single tool call, and it carries a risk the previous lesson never had to deal with.
In this lesson, we will cover:
What actually changes between one bounded action and a multi-step workflow
Why errors compound across steps in a way a single tool call never allowed
Building in a step limit and a clear definition of done
A full bounded workflow in code, including what happens when it can’t finish
How the same pattern shows up in a real product
The three ways engineers get this wrong ... ...