Abhi Arya describes an initial MCPModel Context Protocol is a standard way for AI applications to connect with external tools and data sources through consistent interfaces. server that exposed every application endpoint as a tool. A curated demonstration worked, but less precise real requests produced confident workflows that neither the user nor the agent could explain. Abhi Arya frames agent-first software as an architecture problem: who notices a mistake, and who is accountable for its consequences?
Abhi Arya's redesign uses fewer tools carrying known workflow patterns, including a combined classification-and-extraction operation. A snapshot tool supplies actual pipeline state and specific errors, reducing reliance on guessed variable names or prompt-only guidance. The aim is to make the valid path simple and the resulting work legible to a reviewer.
Abhi Arya also describes exposing extraction confidenceAI confidence estimation produces a score or distribution intended to represent how strongly a model's evidence supports a particular prediction or output. and ambiguous readings, evaluating unsupported assumptions as failures, and asking targeted clarifying questions. Instrumentation of questions, low confidence and human overrides feeds later guidanceAn AI feedback loop occurs when AI outputs or their consequences become inputs that influence later model or agent behavior.. These are Reducto's reported experiences; the general lesson is scoped autonomy with inspectable outcomes, not a guarantee of flawless document processing.
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