Tessera implements a skeptical agent architecture

1. The core mechanism of Tessera deviates from standard token-prediction models by functioning as a research-oriented agent. Rather than prioritizing conversational fluidity, the system is architected to refuse prompts unless every claim is supported by verifiable evidence.

2. This approach addresses the fundamental reliability gap in generative AI. By shifting the priority from "helpfulness" (which often results in hallucinations to satisfy user intent) to grounding in source material, the agent operates as an information retrieval engine rather than a simple chatbot. This is particularly relevant for technical or legal domains where the cost of error is high.

3. From a systems design perspective, the project represents a move toward more rigorous agentic workflows. The primary question remains how the model manages ambiguity or subjective queries where the definition of "verifiable evidence" becomes computationally or logically difficult to establish.

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