Let me cut to the chase: today I’m zooming in on conversational AI, mostly the LLMs. Those models that speak, write, and “think” alongside us. Why? Because this space is moving faster than most product teams, and it’s suddenly relevant to anyone who types into an AI chatbox… or wants their workflow to run without them.
What’s the Real Gap: Great Prompt vs. Manual Agent
I’ve built a lot of prompts. Maybe you have too a template for user stories, competitor benchmarking, release checklists. But I’m no stranger to agents either. Here’s the gist:
A rich, reusable prompt is like a supercharged search query. I run it, get my result, tweak it, and maybe save for another day.
A manual agent? This is next-level: I hit “Run,” and it does a bunch of stuff for me… decisions, context, maybe several steps in a row. Sometimes, it even tells me what’s next.
Here’s how I see it:
- Every prompt needs me :/ every time, start to finish.
- An agent remembers why it exists. It’s got decision trees, action lists, context carryover.
- Prompts answer questions. Agents actually do stuff.
How Do I Turn My Epic Prompt Into a Manual Agent?
Some days, my prompts feel so advanced it’s a shame not to call them agents. So I made the upgrade, and you can too. Here’s my go-to checklist for this transition:
- I clarify my agent’s goal. Not just “answer this,” but “do this, in this way, so I don’t have to micromanage.”
- I restructure the prompt to include context, instructions, inputs, and possible next actions.
- I add logic. If X, then do Y (like “if negative feedback, escalate to user research”).
- I let it remember stuff…context, previous answers, anything useful.
- I teach it how to format and deliver results (like, “output table for Jira” or “send summary to Slack”).
- I connect it to a manual trigger: button is goood enough for the beggining. Forget about API and webhook… for now 🙂
- I run tests, fix bugs, repeat until smooth enough
Manual triggering means I’m still in control. But after that? My agent runs a show, not just answers a question.
The Critique: It’s NOT Just About More Prompts
Most guides focus on better prompts, bigger libraries, smoother templates. But honestly, that misses a few elephants in the room:
- Not all AI skills live inside prompt engineering. Ethics, validation, project management… this all matter.
- Work with agents and LLMs is rarely linear. Sometimes I jump from prompt to agent, sometimes zigzag according to project needs.
- Teams need versioning, sharing, actual process. A prompt library doesn’t cut it if I want repeatable business results.
- New models keep popping up. Prompt skills must evolve with them.
Lessons I Stick To:
- Prompting is where I start, Agents is where I scale.
- Sometimes, a killer prompt is enough. Sometimes an agent saves me hours.
- Choice depends on my process, not on the “ultimate AI workflow.”
- Testing, context, and iterating win every time.
Final Thoughts
Looking back, what moved the needle for me wasn’t just writing more advanced prompts. It was building mini-systems that gave me leverage, saved my time, and made AI a background player instead of an inbox. Agents aren’t for everyone or every scenario. But if you want to level-up, they’re worth the leap.
If you need some mentoring – feel free to hit me.
For you its free. For teams its not 🙂