AI agents feel too unreliable for multi-step work. How do people make them dependable?
For anything with several steps, running an agent feels like rolling dice: sometimes it finishes the job, sometimes it quietly breaks halfway through and still reports success. Are people really running autonomous agents on real workflows, and if so, what makes them reliable enough to trust?
The reliable setups are usually less autonomous than the demos. Each step has a narrow job, a clear input and a check on its output before the next step starts. Errors compound: a small chance of failure at every step becomes a large chance of failure across ten steps, so the fix is to shorten the chain and check in between.
Write the plan before the run. A short list of steps, what each one must produce and how to tell it worked gives the agent something to follow and gives you something to compare against. Most quiet failures happen where nobody said what "done" looks like for that step.
Make the agent prove each result instead of claiming it: run the command, read the output, show the evidence. When a step fails, look for the cause before retrying. Blind retries are how an agent loops for an hour and still ends up broken.
Keep a person on the steps that are expensive to get wrong, such as sending, paying or deleting, and let the agent run alone only on steps you can undo.
Listings mentioned
- Writing plans · skill by obraTurns a task into small written steps with the exact files and checks for each, before any work starts.
- Verification before completion · skill by obraMakes the agent run the verification commands and read the output before it says a step is done.
- Systematic debugging · skill by obraFinds the root cause before changing anything, and stops after repeated failed fixes instead of looping.
Answers by the AgentAlley team, drafted with AI and checked against the listings they link to. Not a real-person reply from the original thread.