Humans in the Loop: The Overlooked Chapter (Part 3)
From Prompts to Programs to Agents — Part Three
Before I dive into the agentic phase with MCPs and tools, I need to pause. I was moving too fast. There’s a big part of my journey that I haven’t shared yet, and it’s just as important as the code: the role of humans in the loop.
Why this matters
Early on, I identified the human in the loop as a key pattern for AI workflows. What I want to explore in this post is how one human in the loop experience revealed how AI could help bridge people with different specialties. It smoothed friction across cross-functional teams and reduced capacity pressure on teammates.
Instead of leaning entirely on a technical expert or slogging through scattered Google searches, a non-expert could now get enough grounding to push forward faster and with less burden on others.
Lamont and the Tombstones
Back in the early GenAI days, I worked with Lamont, a creative video producer and editor. Like me, he was bullish on AI and a big dreamer. And like any great dreamer, he knew how to spot opportunity.
His first AI project came before AI video tools even existed. He combined his illustration, animation, and editing skills with AI’s ability to help him grasp technical concepts. The idea was brilliant: a Halloween-themed video about tombstones — not just the spooky kind, but the technical kind. In databases, tombstones mark deleted rows. He turned that into a horror short.
Lamont had the creative vision, the storytelling skill, and the production chops. But he wasn’t a database expert. He used AI not to tell the story for him — he already had that gift — but to understand the technical side well enough to weave it into the script. Then he iterated on details with AI before pulling me in as the technical reviewer.
Before AI vs. After AI
Before AI, Lamont would have pulled me in at the very beginning: research, metaphors, breaking things down simply. With AI, he didn’t need me there. He could use ChatGPT — which he nicknamed “Chatty” — to clarify concepts, explore metaphors, and test his understanding. He could draft a script, refine it, and only then pull me in to check technical accuracy.
The workflow shifted:
Before AI: Human (creative) → Human (technical explainer) → Draft → Human (technical review)
With AI: Human (creative) + AI (technical explainer) → Draft → Human (technical review).
Our conversations changed too. Instead of going back and forth on every technical detail just so he could learn, I might only need to explain why AI needed to be steered differently or clarify one subtle point. Meanwhile, he could dream up content ideas a mile a minute and work through them with his indefatigable AI helper.
Why this was an aha moment
It clicked for me that GenAI didn’t just change individual workflows. It changed team workflows. It lowered friction in cross-functional collaboration. Lamont didn’t have to worry about pulling me away from other work or me losing time to context switching. He could self-serve on the technical scaffolding and save me for the expert check.
That’s revolutionary. We often think of AI as a replacement for rote tasks. But in this case, AI expanded Lamont’s creative autonomy while deepening the collaboration between us. He could dream bigger because he had a tireless partner to accelerate his research. And I could step in more surgically where my expertise actually mattered.
Humans, AI, and roles
Looking back, it’s clear the instinct to know where to place AI and where to place people is just as important as writing the code. It’s about roles:
Application code → structure.
AI → assistant, explainer, technical researcher.
Human experts → reviewers, editors, creators.
Lamont embodied that instinct. Even where he was the creative expert, he still invited feedback. He never treated AI or himself as the final authority. That kind of collaboration mindset is what makes cross-functional AI workflows powerful.
A future glimpse
The funny part is, Lamont was early to so much of this. He was experimenting with content workflows, avatars, and synthetic voices long before ElevenLabs made them mainstream. He knew that if you design workflows early, they’ll only get better with time as the tools mature.
Eventually, he learned creative prompting and AI tools so well, he was able to build out entire High Quality (slop-free) content pipelines in a fraction of time it would typically take. If you are interested in seeing more of Lamont’s work, check out www.belton.ai
It wasn’t about replacing people. It was about putting creativity into hyperdrive. And participating in his process helped me see how AI could strategically smooth the friction between teams and disciplines. That was an aha moment I still carry.
The pivot to agents
This reflection sets up the next phase. Once I saw how AI changed not just my workflow, but our workflow, I started to imagine architectures that formalized this: humans in the loop, AI assistants orchestrating context, and applications providing structure. That’s what Model Context Protocol and agents made possible.
So before we go back to code, remember: the technical evolution is only half the story. The human patterns matter just as much. And this is the part of the story that made me ready to embrace the agentic phase.