Engineering metrics vs AI workflow governance.
LinearB tells you what happened — DORA metrics, cycle times, AI adoption rates. LoomStack governs what is allowed to happen — which AI agents run, under what policies, with what human oversight. Both are valuable; they solve different problems.
LinearB is an engineering productivity analytics platform — it measures outputs. LoomStack governs the processes that produce those outputs. Knowing that AI tools were used and correlating usage with cycle time improvements is measurement. Determining whether appropriate policies were followed, whether the right person reviewed AI-generated changes to high-risk services, and whether your AI governance program is actually working — that's control. Both matter, and they complement each other.
Governance, not just measurement
LinearB shows you that AI tools were used and correlates usage with cycle time improvements. gitStream provides PR-level policy enforcement. Neither extends to governing which AI agents run, under what policies, or whether cross-tool governance requirements are met.
Cross-tool AI attribution without metadata gaps
LinearB relies on PR/commit metadata to identify AI-assisted code — tools that don't tag their output are invisible. LoomStack tracks AI agent activity at the workflow level, not just the PR metadata level.
Risk-calibrated autonomy
LinearB has no model for change risk — all PRs are equal in its worldview. It routes reviewers and measures throughput, but doesn't ask whether this AI-generated PR to your payment service should require a different review process.
Active governance vs passive analytics
gitStream can enforce review policies and block merges based on rules — but it operates only at the PR level. LoomStack governs at a higher layer: blocking AI-generated code from proceeding until governance requirements are met.
LoomStack and LinearB are complementary in an AI-native engineering organization. LinearB provides the metrics and productivity analytics that help engineering leaders understand outputs. LoomStack provides the governance layer that shapes how those outputs are produced. Data can flow between them: LoomStack's workflow data can enrich LinearB's productivity picture with governance context. Engineering leaders can use LoomStack to answer "is our AI program governed correctly?" and LinearB to answer "is our AI program improving productivity?"