Complete procedures with a way back.

Create deterministic JSONL requests, upload and submit a bounded batch, poll without duplication, reconcile output and error files by custom ID, report cost, and safely resume only missing work.

Run a workload-owned, evidence-based comparison between a pinned local Qwen3-4B GGUF served by llama.cpp and current OpenAI and Anthropic cloud API candidates. Measure quality, privacy boundaries, latency, reliability, operations, capacity, and total cost without pretending differently sized models or delivery models are identical.

Add explicit prompt-cache breakpoints to stable Claude tool, system, document, and conversation prefixes; measure creation and read tokens, test invalidation and minimum-size behavior, compare uncached and cached latency, and roll back without corrupting the prompt contract.

Build an asynchronous Claude batch pipeline with deterministic custom IDs, durable manifests, bounded request construction, polling and cancellation, unordered JSONL result ingestion, per-item success and failure states, usage accounting, retention-aware export, and safe replay of only unresolved work.

Instrument complete agent trajectories with privacy-safe OpenAI Agents SDK traces and OpenTelemetry, correlate model calls, handoffs, guardrails, approvals, tools, retries, and side effects, measure end-to-end latency and all token cost, evaluate unsafe behavior deterministically, and release dashboards and alerts with the agent.

Measure production token economics and user latency, design cacheable prompts and context budgets, and ship only quality-gated optimizations.