Torras.ai

While researching agentic AI use cases in the wine industry, I expected a straightforward session with ChatGPT. What I didn’t expect was to catch the model thinking out loud — revealing not only its problem-solving process but also a glimpse into the invisible infrastructure behind it.

This wasn’t just an error. It was a rare opportunity to observe the internal mind of a modern AI — an experience made possible by OpenAI’s introduction of chain-of-thought (CoT) reasoning in the o1 model (September 2024), and expanded further in o3 (April 2025).

And it made me realize: today's AI doesn’t just answer. It reflects, adapts, and — in some cases — quietly unveils its secrets.

The Moment the System Started Thinking

The task seemed simple: I asked ChatGPT to pull together examples of agentic AI in the wine industry, covering vineyard operations, winemaking, sales, supply chain, and more. It began smoothly:

I’m piecing together agentic AI use cases in wine, dividing the industry into vineyard operations, winemaking, supply chain, sales, retail, compliance, and predictive analytics.

Then, something strange happened. Instead of continuing with results, the model paused — and diagnosed a problem:

Interestingly enough, the error likely stems from the query or a related aspect, leading to the identified problem.

Rather than a traditional “I couldn’t find anything” message, the model reasoned through the problem in front of me. It started considering what went wrong — and how to fix it. This was no longer a basic interaction. It was a live debugging session.

A Glimpse into ChatGPT’s Hidden Machinery

The model didn't just think about the error — it exposed part of its own operational structure. It mentioned relying on a system called "web_with_bing", suggesting it primarily uses Bing for searches and considers Google as a fallback:

I'm trying to find out if the error is a glitch or a problem with "web_with_bing."

I'm pulling together ideas to refine the search strategy, considering options like specifying recency or switching to Bing or Google for better connection.

At that moment, it became clear: behind the polished, seamless interface of ChatGPT is a multi-layered architecture, blending internal knowledge with live external searches. And when one layer fails, the system doesn’t just crash — it tries to reason its way around the problem.

While this openness was fascinating to observe, it also hinted at risks. Revealing internal services, logic structures, and fallback mechanisms could become a security concern if exposed to bad actors.

How much should AI models disclose when thinking out loud? That’s a new ethical and security question we’ll need to answer.

Reasoning Under Pressure

The search engine was failing — either down, blocked, or unreachable. But the model didn't quit. Instead, it actively pivoted:

Given the search tool's limitations, I’m re-evaluating my strategy, possibly leveraging prior knowledge or conceptual insights about "agentic AI" in wine.

It brainstormed ways to refine the query:

Problem-solving alternative ways like focusing on specific search queries, such as "AI wine industry use cases 2024," to resolve the issue effectively.

I'm thinking about honing the search criteria to "AI sommelier chatbot" to accurately pinpoint the intended functionality and target audience.

It even questioned itself, wondering if it was the query, not the tool, that was at fault:

Hmm, I’m thinking about why the search might be off. It could be a glitch or an improperly framed query.

The experience was like watching an engineer adapt strategies mid-project — except the engineer was a machine.

Not Just Using AI — Collaborating With It

Ultimately, what struck me most was that the model didn’t treat the failure as the end of the conversation. It adapted, strategized, and kept moving forward. It even started suggesting alternative paths, like using recognizable sources:

I’m thinking about using recognizable references or direct approaches, like press releases or articles, since the search functionality is down.

It wasn't a simple question-and-answer interaction anymore. It felt like working alongside an intelligent partner — one that could think, troubleshoot, and propose new ideas when faced with roadblocks.

This, to me, marks the next real leap in AI: not just smart outputs, but shared problem-solving. A world where AI isn’t just a passive assistant, but an active collaborator.

Final Thoughts

The glitch I encountered wasn’t just a technical hiccup. It was a window into the evolving soul of AI.

Thanks to chain-of-thought reasoning, modern LLMs like ChatGPT don’t just generate responses — they narrate their reasoning, adapt when systems fail, and hint at a future where AI works with us, not just for us.

Sometimes, when the system breaks, that's when the magic happens.