One thing I've learned from building AI agents is that intelligence alone isn't enough. The closer the relevant context is to an agent, the better it performs.
At first, I thought better models would automatically produce better results. But after working with retrieval systems, memory, and tool calling, I noticed something interesting. Agents often fail not because they can't reason, but because the information they need is too far away from their current context.
If the right document, instruction, or piece of data is already present when the agent starts reasoning, the quality of the output improves dramatically. If that same information sits in a database waiting to be retrieved, performance becomes less predictable.
The more I thought about it, the more I realized humans work the same way.
I write better code when the relevant documentation is open beside me. I debug faster when logs are visible. I learn quicker when examples are right in front of me. The knowledge itself hasn't changed—only its proximity has.
This led me to a simple idea:
The effectiveness of an agent is often proportional to how close the required context is to its decision-making process.
As developers, we spend a lot of time asking how to build smarter systems. Increasingly, I think a better question is: how do we bring the right information closer to the system?
Because in my experience, whether it's humans or AI, the closest context usually wins.
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