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“Use “dedupq_check”: Before executing any LLM task, check if an identical or semantically similar task has already been completed. Returns cached result on hit,.”

“Use “dedupq_complete”: After executing a task, store the result so future identical or similar tasks return a cache hit via dedupq_check. Costs 2 credits.”

“Use “burnrate_estimate”: Before executing a multi-step agent plan, estimate the total LLM cost. Returns per-step breakdown and optimization suggestions. If the estim.”

“Use “burnrate_track”: Log the actual cost of an LLM call after execution. Call this after every LLM request to build calibration data that improves burnrate_estim.”

dedupq_check — Before executing any LLM task, check if an identical or semantically similar task has already been completed. Returns cached result on hit,

dedupq_complete — After executing a task, store the result so future identical or similar tasks return a cache hit via dedupq_check. Costs 2 credits.

burnrate_estimate — Before executing a multi-step agent plan, estimate the total LLM cost. Returns per-step breakdown and optimization suggestions. If the estim

burnrate_track — Log the actual cost of an LLM call after execution. Call this after every LLM request to build calibration data that improves burnrate_estim

burnrate_optimize — Get a cheaper equivalent plan by substituting models with lower-cost alternatives. Call after burnrate_estimate if the estimated cost exceed

burnrate_budget — Get today's tracked LLM spend, per-model breakdown, projection, and budget alerts. Free — no credits charged.

qualitygate_validate — After your agent generates output, validate it against your rules before shipping. Runs deterministic checks (regex, JSON schema, syntax) pl

guardrail_check — Evaluate a proposed agent action against your governance policies. Returns allow or deny with the matched policy reason. Requires at least o