I started with a simple marketing problem. The technology evolved because the problem did.

I did not wake up one morning and say:

“I need to implement an agentic AI solution.”

I had a much simpler problem.

I needed help developing marketing content.

Initially, the request was straightforward: take one of my observations about leadership, employee engagement, or IT delivery and turn it into a LinkedIn post.

AI generated the content. I reviewed it, copied it, pasted it into LinkedIn, and published it.

Useful? Absolutely.

But I was still doing the administrative work.

Solving the Next Problem

The next question was obvious:

Can we send the content to Buffer so I do not have to copy and paste it?

We connected the workflow to Buffer. Now the content could be created and handed directly to the platform responsible for publishing it across my marketing channels.

That removed one manual step.

But then I realized the LinkedIn post could become more than a social media update.

The same idea could be expanded into a more complete blog article.

So the workflow evolved again.

One leadership observation now produced:

  • A conversational LinkedIn post
  • A more detailed blog article
  • A branded image
  • Content formatted for the appropriate channel

That created another question:

Can we publish the article directly to my Webflow website?

We connected Webflow.

Now the workflow could develop the original idea, create the social content, produce the branded image, author the blog, format the article for the Webflow CMS, publish everything, and return the live links.

The Moment It Became Agentic

During one publication, Buffer rejected the image because the attachment was not structured in the format the integration expected.

The system did not simply stop and hand the error back to me.

It recognized the failure, corrected the media structure, tried again, completed the publication, and confirmed the result.

That is an important distinction.

A traditional automation follows predefined instructions:

Do A, then B, then C.

An agentic workflow can pursue an outcome:

Complete the publication, observe what happens, and adjust when something does not work.

The workflow had progressed from:

Generate content

to:

Generate → format → publish → observe → correct → verify

That is how a simple content request evolved into supervised agentic AI.

We Started With the Problem

The most important part of this story is what we did not do.

We did not begin by holding meetings to identify an “agentic AI use case.”

We did not start with models, orchestration frameworks, or technical architecture.

We started with friction:

  • Creating content took time.
  • Copying it between systems added no value.
  • Expanding the same idea manually duplicated effort.
  • Publishing separately to multiple platforms created more administration.
  • Checking whether everything worked required another manual step.

Each stage of the solution addressed the next practical constraint.

The technology grew around the business process.

The Business Outcome

One observation can now become an end-to-end, multichannel campaign.

I provide the insight, experience, and strategic direction.

The AI agent helps transform it into content, creates the visual asset, publishes through Buffer and Webflow, handles certain integration problems, and returns the completed results for verification.

The value is not that I can say I use agentic AI.

The value is that I spend more time developing ideas and less time copying, pasting, formatting, uploading, and troubleshooting.

That is the lesson organizations should take from this.

Do not begin with:

“Where can we install agentic AI?”

Begin with:

“What outcome are we trying to achieve, and where does unnecessary friction prevent us from achieving it?”

Solve the first problem.

Then the next one.

Then connect the solutions.

You may discover that you have built an agentic AI workflow—not because someone mandated one, but because it became the most practical way to complete the work.

Key Takeaways

  • Start with a real business problem, not an AI label.
  • Automate individual points of friction incrementally.
  • Connect those automations around an end-to-end outcome.
  • Agentic AI becomes valuable when it can act, observe, adjust, and complete.
  • Human judgment remains essential for insight, direction, and oversight.
  • The best AI solution is not the most technically impressive one. It is the one that quietly removes work that nobody needed to be doing manually.

For more practical reflections on AI, leadership, and IT delivery, visit www.imadlodhi.com.