Everyone is still obsessed with the ‘perfect prompt.’
They’re spending weeks tweaking adjectives and adding ’think step-by-step’ to their system instructions, hoping to squeeze 2% more accuracy out of the model.
It’s a waste of time.
If you look at what the actual engineers at Stripe, OpenAI, and Anthropic are doing, the trend is clear: The focus has shifted from how the AI talks to how the AI acts.
We are moving from the ‘Chatbot Era’ to the ‘Agentic Era.’
The difference is subtle but massive. A chatbot is a layer of text. An agent is a layer of functions.
The pattern I’m seeing across the best implementations is a move toward ‘Tool Engineering.’
Instead of trying to convince an LLM to be smarter, they are building surgically clean, deterministic APIs that the LLM can call. They are treating the AI as a router, not a calculator.
The logic is simple: LLMs are great at intent. They are terrible at precision. APIs are terrible at intent. They are perfect at precision.
The convergence of the two—where the LLM handles the ‘what’ and the API handles the ‘how’—is where the actual value is being created.
If you’re still spending your Fridays in a prompt-tuning loop, you’re missing the forest for the trees.
The real competitive advantage in 2026 isn’t who has the best prompt library. It’s who has the cleanest API surface area. Because the model that can actually do something is the only one that survives the hype cycle.
The future isn’t a better conversation. It’s a better toolset.