Context Engineering: Future of AI Agents and Retrieval

We will soon get to a point, as AI model progress continues, that almost any time something doesn’t work with an AI agent in a reasonably sized task, you will be able to point to a lack of the right information that the agent had access to. This is why context engineering is the future. Basically you’re reverse engineering what an insanely smart human, would need to perform a particular task. The caveat is this super smart person is an expert at almost any type of field of work, but one day they’re a lawyer at a Fortune 500 and the next day they’re an engineer at a startup. And they forget what they did between each task. And they can only keep track of one medium-sized thing at a time. Super fun challenge. This means they need a ton of context - but not too much to get confused - about what they’re doing and why. So the job then is to try and build the system or set of systems necessary to deliver that data to the model as efficiently and quickly as possible. This is why so much time is just going to straight into search and retrieval systems, heuristics for ranking information, system prompts, ways of keeping track of the work that’s being done to save context window space, and so on. One cool thing, though, is that unlike a person, this agent can process vastly more data at once, so all of a sudden you can apply more compute to the problem than would otherwise be helpful with people. An insanely fun time right now to be building agents.