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A principle from information theory that doesn't get enough attention in AI agent design.
The Data Processing Inequality states that information cannot increase through processing — any transformation can only preserve it or reduce it. Applied to chained AI agents, this means accuracy cannot improve as you add steps. Each agent is a lossy channel, and errors compound.
This tool makes that concrete. It shows four decay scenarios across a chain of up to ten agents — independent errors, correlated errors, best-case RAG-augmented pipelines, and worst-case raw recall — alongside real benchmark data from the AA-Omniscience evaluation (Artificial Analysis, Nov 2025).
- Adjust base model accuracy and chain length to see how quickly accuracy degrades
- Compare independent vs. correlated error regimes and understand why the difference matters in production
- See where frontier models actually sit on the accuracy/hallucination spectrum
- Understand why human review at pipeline checkpoints is an architectural requirement, not a workaround
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npm install
npm run dev