AI Agents Don’t Need More Context — They Need Typed Context

AI agents often fail not because of a flawed model, but because their input context is a jumbled mess where instructions, evidence, and tool outputs are flattened into a single string. This loss of structure can lead to critical errors, such as a piece of retrieved data being misinterpreted as a direct command. A proposed "context type system" solves this by assigning an explicit type—like `INSTRUCTION` or `TOOL_OUTPUT`—to each piece of information before it's combined into a prompt. This lightweight runtime then enforces strict rules to prevent invalid transformations, catching structural bugs before a malformed request is ever sent to the model. Ultimately, this approach provides a correctness and observability layer that makes agent behavior more reliable and easier to debug.