Description

In part two of the Enterprise AI Agent Design Principles series, Andres and I dig into context. It is the information you pass to the model through system and user prompts, and it is what makes agents useful or useless.

Too little context and agents guess, hallucinate, or leave gaps. Too much and you get instruction dilution, recency bias, and sluggish performance. So we test the limits by overloading an agent to see what actually breaks, then share practical strategies for keeping agents sharp.

We cover when to offload data loading to tools, how to structure instructions so prompt engineering works for you instead of against you, and how to find the balance between precision and performance.