I hear this often when I talk to people about agentic workflows for their business.
I get it. Setting up an agentic workflow can be frustrating at first. Hallucination, vagueness, wrong decisions, wrong context, AI slop.
But here is the thing. Setting up an agent to deliver real work is similar to teaching a good intern or new hire to do a job. You have to teach it what to do.
That starts with being precise about three things:
1. The result. What are you looking for, what qualifies as a good result, and how can it be measured and verified?
2. The context and tools. What does the agent need to create that result?
3. The steps. What is the best route to get there?
On top of that, you ask the agent to document the pathway it took:
1. The steps it took, and how much time it spent on each.
2. Whether the tools it needed were available, and if not, which workaround it used.
3. Where it made a mistake, and whether it recovered.
4. Whether it verified its result, and how.
5. Which sources it used, and what information it took from each.
Many people stop after the first disappointing output. They judge the result, but never look at the pathway that produced it.
The pathway is what makes the first run valuable, even when the result disappoints.
After the first run, you inspect two things. The result, to find the quality problems. The pathway, to find their root causes. Was the goal unclear? Was context missing? Did a tool fail? Was verification skipped?
Then you adapt the prompt and run it again, until you are satisfied with the result.
Now you have trained your agent to perform a specific task at high quality.
And next time, it can repeat it without your help, at the standard you expect.
Which workflow are you currently trying to automate?