Research

Coordinating many AI agents across machines is less about smarter prompts and more about the boundaries between them.

The gap between a demo that impresses and a system that performs reliably is where most AI projects quietly stall.

Fine-tuning is not automatically better than a good prompt. Choosing the wrong one wastes time and money on both sides.

Offline benchmarks tell you an agent can pass a test. They rarely tell you it will do the job.

The Model Context Protocol makes it easy to expose tools to an AI. Designing those tools well is what keeps them fast and maintainable.

In the enterprise, trust is the feature. Accuracy, oversight, and security are not add-ons - they are the product.