sentinel-e
An AI reasoning engine built on disagreement
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Oomkar Agarkhed

sentinel-e - An AI reasoning engine built on disagreement

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AI models debate each other instead of blindly agreeing. Confidence comes from cross-model analysis, not self-reported certainty. Sentinel-E combines multi-model orchestration, adversarial reasoning, evidence verification, and transparent reasoning into one cognitive AI system.

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Oomkar Agarkhed
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Sentinel-E was inspired by a simple problem: most AI systems sound confident even when their reasoning is weak or hallucinated. I wanted to build a system where models could challenge each other, verify claims, and expose reasoning instead of acting like isolated black boxes. The idea evolved into a multi-model cognitive engine where agreement doesn’t automatically mean truth. That led to debate mode, evidence verification, confidence calibration, and transparency-focused reasoning. One of the biggest challenges was authentication and persistent memory architecture — especially handling cross-session recollection, user session continuity, JWT/auth synchronization, and reliable retrieval of user-specific data from the database without breaking isolation or context integrity. Managing orchestration between multiple models while keeping latency, session state, and memory retrieval stable was also a major engineering challenge.