How common AI agent failure modes show up in data-critical domains, and how ClariTrial's architecture addresses each one.
LLMs fabricate data in critical domains. A deterministic-first architecture makes the agent prove its claims before it synthesizes.
Users cannot trust what they cannot see. Trace panels, provenance badges, and structured answer headings make agent reasoning inspectable.
Unconstrained tool loops cause cost blowouts and unpredictable behavior. Step budgets, role-limited tools, and read-only enforcement keep agents on a leash.
Letting an LLM generate arbitrary SQL is the new injection vector. Allowlisted presets and validated parameters close the gap without losing flexibility.
When an AI agent mixes measured data with speculation, users in high-stakes domains cannot tell what to trust. Answer typing enforces the boundary.
Regulatory and compliance requirements demand reproducibility. Versioned prompts, structured audit events, and scope-tagged postures provide the foundation.