Office
Office of AI Throughput, Governance & Cost Control
High-intent AI operations playbooks: governance guardrails, monitoring controls, throughput tuning, and reproducible reliability workflows.
Published: 2026-01-01 · Last updated: 2026-02-10
Institutional notes on removing avoidable idle time in AI/LLM inference pipelines to reduce cost per successful completion.
Start here if you are answering one of these:
- Why is our inference stack slow even with a strong model?
- Which controls raise throughput without exploding retries?
- How do we convert crawl evidence into auditable setup windows?
Latest work
- AI governance for autonomous agents: bgmon guardrails, monitoring, and policy control
- Autonomous AI agent background jobs: a self-healing bgmon factory
- How to scale LLM throughput past 500k TPM with 8-shard concurrency
- How to keep Codex background jobs running without stale terminals
- How to turn institutional crawl signals into high-quality trade opportunity windows
Start with one action
- Get Office updates — weekly notes and workflow releases.
- Request private collaboration — workflow audit, technical architecture help, and implementation support.
Office notes
- AI governance for autonomous agents: bgmon guardrails, monitoring, and policy control
- Autonomous AI Agent Background Jobs: A Self-Healing bgmon Workflow for Codex
- How do you turn institutional crawl signals into high-quality trade opportunity windows?
- Must-have Codex tool: how to keep background jobs running without stale terminals
- How CTOs increase LLM throughput (8-shard proof)
- How to stop LLM HTTP connection hangs
- What LLM concurrency cap should you run?
- Why enterprise LLM inference is slow
- How a phone terminal survives reconnects
- Terminal session streaming: minimal message shape
- Output buffering and replay for long-running sessions
- Session IDs and admin controls for browser terminals
- Mobile terminal rotation: resize without corruption
Office Briefings
Office Briefings are evidence-backed technical decision aids for specific throughput and cost-control questions. They are scoped, non-promissory, and designed to be reviewed by a senior engineer before adoption.
Scope boundary
This index covers Office materials on throughput and cost control via idle-time elimination. That includes both:
- inference pipeline controls (connections, concurrency, backpressure)
- operator workstation surfaces that reduce recovery friction (persistent sessions, reconnect, replay)
It excludes: training and fine-tuning, prompt/content strategy, commercial claims, and benchmark numbers that are not backed by a reproducible harness and raw measurements.
Build narrative
Follow a coherent path from thesis to lab notes to proof-of-work instead of isolated pages.