Your AI Agent Is Only as Good as the Context It Sees

Most conversations about AI agents still begin with the model: which one reasons better, which one is faster, which one is cheaper per request. Those questions matter, but they rarely explain why an agent performs well in a controlled demo and then becomes inconsistent once it’s running a real, multi-step task.

That gap usually comes down to something else: not which model is running, but what information that model had available at the exact step where it made a wrong call.

That is the core of context engineering: the discipline of designing everything the model receives before it generates a response or takes an action.System instructions, retrieved documents, tool definitions, user history, task state, memory, constraints, examples, and intermediate outputs all compete for space inside the context window. That window is the total amount of information the model can work with at any one step, nothing beyond it factors...

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