A few months ago, I found myself trying to wrangle a large language model (LLM) into doing my bidding with what can only be described as a “mega-prompt.” If you’ve ever tried to fit an entire workflow, context, and all possible edge cases into a single AI prompt, you’ll know the feeling: somewhere between optimism and mild dread.

The results were…mixed. Sometimes the model would spit out something useful. Other times, it would get lost, hallucinate details, or just run out of steam halfway through. The more I tried to stuff into the prompt, the less reliable things became.

Lately, there’s been talk of agentic AI - the idea that instead of treating your AI like a one-shot oracle, you break work into a series of smaller, specialized agents that each do one thing well. It’s a shift from monolithic prompts to modular, step-by-step workflows…similar to how software architecture moved from monoliths to microservices a few decades ago.

Why We Love Mega-Prompts

There’s a certain appeal to the idea of a single, all-knowing prompt. It feels efficient - just tell the AI everything up front, and let it handle the rest. But in practice, it’s a bit like writing a master SQL query to handle all your analytics needs. Technically possible, but you probably won’t like the maintenance.

The more complex the task, the more brittle the mega-prompt becomes. If something goes wrong, it’s hard to debug. If you want to tweak just one part, you’re back to editing a massive wall of text.

The Micro-Agent Approach

After too many disappointing outputs, I started breaking my workflows into smaller, focused steps. This is at the heart of what the industry calls agentic AI - setting up a chain of “micro-agents,” each responsible for a single, well-defined job.

Using a product management example:

  • One prompt to summarize user feedback.
  • Another to analyze product usage trends.
  • A third to cluster related themes.
  • And finally, one to draft a PRD based on all of the above.

Each micro-agent gets a clear input and produces a specific output. If something goes sideways, I know exactly where to look. If I want to swap out the analytics step for a different approach, I can do that without reworking the whole chain.

What Changed

The biggest shift was in reliability. Smaller prompts are easier for the model to handle, and chaining them together gives me more control. It also feels closer to how I tackle things with my team: parse the problem, assign ownership, execute, review the results, and iterate.

There’s also a nice side effect - this modular approach makes it easier to experiment. If I want to try a new method for clustering feedback, I can do that in isolation. If I want to automate just the analytics summary, I don’t need to rewrite everything else.

Lessons Learned

  • Start with the workflow, not the prompt: map out the steps you’d take manually, then assign each to a micro-agent.
  • Keep prompts focused and specific: the less ambiguity, the better the results.
  • Iterate on each step: don’t be afraid to tweak and test individual agents before chaining them together.
  • Expect surprises: sometimes the AI will connect dots you didn’t see. Sometimes it’ll just get confused. Both are useful feedback.

Final Thoughts

Moving from mega-prompts to micro-agents isn’t revolutionary, but its made my experiments with LLMs a lot more productive — and a little less mysterious. If you’re finding your agentic AI workflows getting unwieldy, try dividing and conquering. It’s not as flashy as the “one prompt to rule them all” approach, but it’s a lot easier to live with.