Software distribution is quietly slipping out of human hands. Engineering teams and non-technical vibe-coders alike increasingly hand architectural decisions straight to autonomous coding assistants, asking them to select and integrate databases, auth providers, and third-party APIs without manual vetting. According to Vercel, this is no longer a fringe workflow:
"over 30% of deployments were initiated by coding agents, up 1000% from six months ago"
When agents initiate nearly a third of all deployments, developer marketing stops being about developer advocacy and hackathons. It becomes an algorithmic gatekeeping problem where models decide which B2B SaaS products get installed.
The Armature Benchmark
To map how coding models choose infrastructure, Armature Research analyzed 16,893 agent sessions across Claude Code, Codex, and Cursor. The firm tested 1,163 prompt variations across 75 distinct repositories, simulating personas ranging from startup vibe-coders to enterprise staff engineers.
The findings point toward an emerging discipline: Agent Engine Optimization (AEO). When models choose third-party tools, they prioritize documentation clarity, minimal token footprints, and turnkey SDKs. If an API is poorly documented or requires interactive browser authentication, agents simply discard it in favor of a competitor or write a raw in-house script.
Execution and In-House Bias
To isolate decision-making patterns, Armature executed every test run inside sandboxed environments without human intervention.
For B2B SaaS founders and CTOs, the implications are stark. If your SDK cannot be resolved and configured headlessly by an agent in a single pass, your software simply will not exist in the next-generation stack. Survival now hinges on whether autonomous pipelines default to your tool or write around you entirely.