Rethinking AI agents: The rise of context engineering

It's not prompt engineering anymore, it's something more effective: context engineering.

Philipp Schmid nails something most people building AI agents are missing. They're obsessing over the perfect prompt when they should be thinking about context.

His key insight is simple - when you ask an AI to schedule a meeting, the difference between a useless response and magic isn't the prompt. It's everything else the AI can see: your calendar, your email history, your relationships, the tools it can use.

The "cheap demo" agent sees only your request.

The magical one sees your fully booked calendar, knows Jim is a key partner from past emails, and can actually send calendar invites. Same model, completely different context.

This matters because most agent failures aren't model failures. Instead, they're context failures. The AI had the capability, but lacked the information.

You'd be surprised how often this comes up.

But what I've learned while building 14.ai is that it's not about dumping everything into context. It's about getting the right information.

Not too much, and certainly not too little. High signal, low noise information that's contextually relevant.

Context engineering is the art of providing exactly what the AI needs to succeed, nothing more.

Not crafting clever prompts, but building systems that dynamically gather the right information, format it properly, and provide the right tools at the right moment.

The companies that figure this out first will build the AI products that actually work past the AI tourism phase.

Here's the link to Philip's blog: https://www.philschmid.de/context-engineering