Driftsource
A configuration-level engineering evidence layer separating captured provenance, code-health observations, statistical associations, analyst findings and approved remediation experiments.
Organizations adopting coding agents may see dead code, duplicated helpers, orphaned dependencies or documentation drift without knowing whether those patterns concentrate around a particular harness or instruction version. The supplied research confirms an established code-health category and a maintenance-focused agent product. It found no reviewed product combining captured agent provenance with downstream code-health analysis at the harness or configuration level. That is a bounded feature-gap result.
Driftsource would preserve repository and revision, code-health rule version, observed finding, finding confidence, affected path, maintenance-cost proxy, captured agent identity assertion, harness version, instruction-file digest, configuration cohort, missing-provenance state, confounder, statistical association, analyst interpretation, remediation hypothesis, owner decision, proposed configuration change, controlled rollout, destination readback, post-change observation, rollback and closure as distinct records.
A correlation between a configuration and later findings does not prove that the agent caused them. Repository age, task type, reviewer behavior, human edits, team conventions, generated code and uneven adoption can all confound the result. The product should compare like work over time, show sample size and missing telemetry, and require an authorized engineer to interpret every finding. It must not rank individual developers, infer productivity or intent, automate performance action, rewrite instructions or merge cleanup changes.
The pilot should use synthetic repositories and replayed change histories with known confounders. The likely buyer is an engineering effectiveness, developer-platform or code-quality owner, but organization size, agent adoption, provenance coverage, remediation ownership, budget and willingness to pay beyond established code-health tooling remain unverified.
An engineering-effectiveness, developer-platform or code-quality owner responsible for reducing repeated maintenance problems in agent-assisted repositories.
A recent maintenance-product pivot and rapid agent adoption support timely investigation.
Automated repository analysis and reusable cohorts can scale after integrations and rule calibration.
The supplied record has several references but no grounded cross-vertical cluster.
The input combines confirmed code-health tooling, a recent shift toward agent maintenance and a concrete configuration-level analysis mechanism.
The buyer evidence is incomplete, provenance coverage may be poor, causal attribution is difficult, and established code-analysis vendors can add agent metadata.
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