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Selects a production experiment based on user exposure, side effects, measurement needs, cost, and rollback risk.
Checks model, feature, API, region, quota, commercial access, lifecycle, and dependent-service compatibility before rollout.
Builds a provider-neutral model gateway that owns request deadlines, retry classification, budget enforcement, fallback policy, and versioned telemetry.
Instruments an AI workflow with correlated stage spans, low-cardinality versions, usage and outcome signals, and explicit content-capture controls.
Runs containment and recovery for an agent that ingested malicious instructions from retrieved content and attempted an unauthorized outbound tool call.
Combines quality, reliability, cost, security, privacy, and recovery evidence into a realistic launch and incident game day.
Diagnoses schema-valid quality regressions across inputs, context, prompts, examples, models, schemas, tools, and downstream interpretation.
Separates product policy, orchestration, model access, data and tools, validation, state, and operations into explicit application-owned boundaries.
Distinguishes exact, provider, semantic, and intermediate caches while preserving identity, freshness, versioning, and correctness.
Separates what a model can perform from whether the complete AI feature behaves dependably under real inputs, failures, and constraints.
Treats model, prompt, schema, tools, routing, retrieval, policy, and evaluators as one observable behavior release.
Connects application, model, retrieval, tools, validation, retries, and outcomes while controlling sensitive telemetry.
Chooses request-response, streaming, or durable background execution based on duration, user interaction, reliability, and side effects.
Centralizes cross-cutting model access concerns when multiple applications or providers justify the added hop and operational ownership.
Balances task quality, safety, reliability, latency, tokens, cost, refusals, repairs, and fallbacks by route and slice.