Production AI Needs Harnesses, Ledgers, and Search
Reliable AI products require infrastructure that controls reasoning, spending, and access to information before models act.
Summary
Production AI depends on systems around models because software operations, financial controls, and company-document search all involve scale, uncertainty, permissions, and costly failure modes. Resolve AI addresses production operations with six harness pillars: model orchestration, context engineering, causal reasoning, governed actions, learning systems, and evaluations, supporting on-call, incident, and background agents. Stigg applies banking infrastructure to AI spending through synchronous entitlement checks, asynchronous settlement, hold-and-settle controls, double-entry bookkeeping, credit pools, and budget hierarchies. LlamaIndex takes a hybrid path for company documents, combining keyword and vector retrieval with file listing, metadata filters, grep, parsed text, and page screenshots; its demo searched 135 Alphabet filings to build a 2021–2025 cash-flow table.