typed-decisions-around-llmsv1.0.1

An engineering team is building an autonomous task agent that can call external tools like file writes, payments, and email sends. They need to place typed judgment models at preflight and postflight boundaries so no LLM ever authorizes its own side effects. The skill helps them map which components produce proposals, which produce confidence scores, and force independent authorization through deterministic policy.

A platform currently uses a single LLM prompt to both draft moderation rationales and decide whether content should be removed. They want to convert this into a typed decision: bounded judgment model returns a distribution over known categories, deterministic code applies policy thresholds, and humans handle edge cases. This eliminates the conflict of interest where the same model gated its own output.

A fintech is adding an AI copilot that recommends transfers, refunds, or credit adjustments. They need a hardened order of operations: schema validation, capability checks, amount allowlists, then typed semantic review, then operator confirmation before execution with least privilege. The skill guides where a judgment model belongs so its confidence is treated as evidence, never authority to move money.

A health system deploys an LLM to handle patient intake, symptom summarization, and appointment routing. They need typed preflight classifiers for intent, sensitivity, and risk so the generative model never chooses among forbidden actions. Postflight bounded checks verify the output before any consequential scheduling or escalation occurs, with documented fallbacks when the judgment service is unavailable.

A developer platform lets agents propose patches, generate deployment plans, and trigger CI/CD steps. The team must ensure the proposer is never the approver: a typed judgment model reviews bounded properties of the patch, policy decides sufficiency, and deterministic checks run first. Immutable receipts capture who authorized each stage for audit and replay.

Sell a typed judgment layer as a drop-in SDK and hosted API that plugs into existing LLM orchestration frameworks like LangChain, LlamaIndex, or custom agents. Customers pay for bounded, auditable decision primitives rather than another general-purpose model. The value proposition is placement architecture and authority separation, not raw model quality.

Offer architecture reviews and implementation services focused on the self-approval audit, capability boundaries, and fallback design for regulated industries. Deliverables include pipeline diagrams, threshold policy documents, and independent verification harnesses. This is a high-trust advisory offering tied to compliance and risk reduction rather than model access.

Ship an opinionated open-core framework that enforces the proposal-to-receipt order, typed preflight and postflight batteries, and immutable authorization records out of the box. Enterprises adopt it to pass audits and demonstrate separation of duties in AI workflows. Paid tiers add policy authoring, dashboards, and certified integrations.