An agent that works once in a demo and an agent that runs unattended every morning are different pieces of software, and the gap between them is not model quality. Posting this here because the three things that actually close it are the least discussed parts of the stack.
The Workflow Is The Part You Can Reason About
A workflow is just a sequence of steps, and conditional logic is what turns that sequence into something that can handle real inputs. Read the data, ask the model to classify it, branch on the answer, act, log. Once the branches are explicit you can point at the exact step where a run went wrong instead of re-reading a transcript.
Variables passed across steps and loops over records are where most of the real complexity lives, along with what happens when a step fails partway through a loop. The guide on workflows and conditional logic in agents walks through branching, variables, looping and error handling with a full worked example.
Scheduling Turns A Demo Into A System
The moment an agent runs on a timer rather than a click, two new problems appear. The first is state: the run at 9am needs to know what the run at 8am already handled, or it does the same work twice. The second is cost, because a schedule multiplies your per-run price by however many runs you just committed to.
Failure handling changes too. A manual run that errors gets noticed immediately. A scheduled run that errors at 3am gets noticed on Thursday. Setting up scheduled agents covers the timing options, state tracking between runs, and what to do when a scheduled run fails.
Testing Is Two Different Jobs
Testing an agent means testing two things that fail in different ways. The workflow logic is ordinary software: known input, expected output, check the branches. The model judgment is not, and it needs a set of representative examples scored against what a person would have decided.
Edge cases are where the split shows. An empty result set, a malformed record and an ambiguous input each break a different layer. Working through how to test and debug agents before launch, and what to monitor after, is the difference between finding those on your terms and finding them in production.
What It Adds Up To
None of this is exciting work and all of it is the reason some agents quietly run for months while others get switched off after two weeks. Get the workflow explicit, the schedule honest about state and cost, and the tests split between logic and judgment, and the model you picked stops being the interesting variable.
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