Faros AI published a comparison of webhooks and API connectors as ways to feed an engineering-intelligence platform, and it resists the usual framing where one of them wins. A connector pulls: the platform holds credentials to GitHub, GitLab, Bitbucket, Jira, PagerDuty and the rest, and fetches on a schedule, which means it can reach backwards and transform data in transit. A webhook pushes: the source system sends events as they happen, and — the property the post leans on — “system credentials never leave the client’s infrastructure.” The catch is symmetric. A webhook cannot tell you about anything that happened before you subscribed, and a connector cannot tell you about anything that happened since it last ran.
So the recommendation is sequencing rather than choice: run the connectors once to backfill history, then switch to webhooks for everything after. The honest sentence in the piece is the one about what that hybrid costs when it breaks — “if you miss events, then someone needs to run a data connector to pull the missed data.” Push delivery has no memory, so the pull path never really retires; it becomes the repair mechanism. That is the right mental model and one most webhook documentation leaves out. The credential argument is also worth taking seriously on its own terms. For a platform ingesting from sixty-odd engineering tools, a webhook-first design means holding sixty fewer sets of keys to other people’s systems, which is a security position rather than a convenience.
The catalog carries six API pages for Faros AI, and the post maps onto them precisely — with one detail worth getting right. The Faros AI Webhooks API is an inbound surface: each webhook definition created in the Faros app has a unique URL that source systems POST to. Faros receives webhooks; it does not emit them. The Events API and Ingestion API are the push path described in the post, and the Graphs API and GraphQL API are where the canonical model the data lands in is queried. The agentic access profile counts 12 operations with 7 acting — mostly write, as an ingestion platform should be.
Faros scores 29.8, thin on the Kin Score, and the facet to read first is contract quality at 9.0. The reason is on the record: the OpenAPI we hold is modelled from Faros’s public documentation and client libraries, and says so in its own description, rather than published by Faros. Agent Readiness is 19.8, agent-aware. event_surface_described reads unlit, and given the inbound detail above that is accurate rather than a miss — Faros consumes events and publishes none. The gap the post itself points at is a different one. Its whole recovery story depends on knowing which events were missed, and nothing in the contract says what a delivery ID is, whether redelivery is idempotent, or what an ingest endpoint does with the same event twice. idempotency is unlit, on the surface where the hybrid either heals or double-counts.