Agentic AI

The model emits a structured call (id, name, JSON arguments) instead of prose; your code validates, runs it and returns a result tagged with the same id.

Force LLM output to match a regex, JSON schema or grammar by masking, at every step, the tokens that would leave the language.

Keep an agent's growing history inside the model's window by truncating, clearing and summarizing old turns in a cheap-to-lossy ladder, at turn boundaries.

Reuse the attention keys/values of a repeated prompt prefix, so an agent's growing context is prefilled once and later turns pay only for the new tokens.

Index tool descriptions, filter by the caller's permissions, rank against the request and put only the top few definitions in context, in a stable order.

Classify each proposed action as auto, ask or deny; park asks as durable pending records a human approves, edits or rejects, executing once, with timeouts that deny.

Periodically merge near-duplicate memories, resolve contradictions, decay the unused and cap the store, so an agent's long-term memory stays small, current and cheap to search.

Send each request to the cheapest model that will answer it well enough, by rules, a learned router, or a cascade that escalates from small to large on low confidence.

Treat every untrusted string as data, track where values came from, and make tool calls and rendered output depend on provenance, not on what the model says.