AI Systems & Agents
How modern AI systems are actually built: agent architectures, model capabilities and limits, evaluation, retrieval, and the engineering that separates a demo from something that survives production.
Enterprise AI agent evaluation starts where APIs meet documents
Enterprise agents fail less at isolated retrieval or function calling than at the join between policy text, records and executable systems. Evaluation has to score the whole path, not the prettiest step.
Adaptive reasoning LLMs turn token budgets into routing problems
Fixed reasoning budgets waste tokens on easy prompts and starve hard ones. Adaptive reasoning LLMs shift the problem from “how long can the model think?” to “when is extra thinking worth the cost?”
AI scientific agents need evidence chains, not workflow coverage
AI scientific agents become auditable when claims are tied to evidence as they are produced. Chain-of-Evidence, omni-modal access and tool traces help, but only if the audit path is designed into the system.