3 Limits: Why Git-Only Evidence Still Needs CI and Telemetry

Observed Limitation#

The primary limitation identified in today’s review is that decisions based solely on repository change evidence—specifically the Git diff and commit history—do not provide a complete picture of what constitutes an “important” change within the broader system. While the diff shows that seven files were modified, with 340 insertions and 1,314 deletions, the magnitude of these changes in terms of runtime impact, user experience, or system stability cannot be inferred from the source code alone. This limitation manifests in several ways:

1. Contextual Ambiguity – The diff indicates modifications to files such as AGENTS.md, CLAUDE.md, and several TypeScript modules, but it does not reveal whether these edits affect critical production paths or are limited to documentation and test scaffolding. 2. Missing Runtime Metrics – There is no evidence of how the new WorkItem type export or the added P4 priority influence actual job scheduling, throughput, or latency. Without telemetry, it is impossible to quantify the operational significance of these changes. 3. CI Feedback Absence – The repository state shows no indication of continuous integration results, such as test pass rates or linting outcomes. A change that passes static analysis but introduces a runtime bug would remain invisible if only Git evidence is considered. 4. Human Decision Context – The commit messages and author information provide limited insight into the rationale behind each change. Decisions made by developers—such as prioritizing a bug fix over a feature enhancement—are not captured in the diff.

Root Cause Hypothesis#

The root cause of this limitation lies in the inherent separation between code versioning systems and the dynamic environments they target. Git is designed to track textual changes efficiently, but it does not capture semantic information about how those changes interact with the system’s runtime behavior. Several contributing factors reinforce this hypothesis:

• Static vs Dynamic Analysis Gap – Git diff operates on static source files, whereas the system’s behavior emerges from compiled binaries, runtime configurations, and external dependencies. This gap means that a large number of deletions in a source file may have negligible runtime impact if the deleted code is never executed.
• Lack of Execution Context – The repository snapshot does not include the state of environment variables, database contents, or network conditions that influence how code paths are exercised. Consequently, a change that appears minor in the diff could trigger significant side effects under specific runtime conditions.
• Human Factors and Decision-Making – Developers often make trade-offs based on business priorities, risk assessments, or architectural constraints. These decisions are encoded in commit messages or issue trackers but are not reflected in the diff output, leading to an incomplete view of importance.
• CI and Telemetry Integration Deficiency – The current workflow does not automatically associate CI build results or telemetry data with each commit. Without this linkage, it is impossible to correlate code changes with observed performance or error rates.

Why We Did Not Fix It#

Addressing this limitation would require a substantial shift in the development workflow, involving integration of CI pipelines, telemetry dashboards, and possibly automated impact analysis tools. Several constraints prevented a fix in the current cycle:

• Resource Allocation – Implementing comprehensive CI integration and telemetry collection demands dedicated engineering effort, tooling setup, and ongoing maintenance. The current sprint budget prioritizes feature delivery over infrastructure enhancements.
• Operational Complexity – Adding telemetry instrumentation to a large codebase introduces new failure modes, potential data privacy concerns, and increased operational overhead. The risk of destabilizing existing services outweighed the perceived benefit for this iteration.
• Immediate Deliverables – The primary goal of today’s work was to export WorkItem types and add P4 priority, tasks that could be completed with minimal code changes. Extending the scope to include CI and telemetry would have delayed these deliverables beyond the agreed timeline.
• Stakeholder Readiness – There is no consensus among product owners, operations staff, and developers on the specific metrics or thresholds that should drive importance decisions. Without a shared definition of success, investing in new tooling would not yield actionable insights.

Next Conditions for Revisit#

The limitation will be revisited when the following conditions are met, ensuring that any effort to integrate CI and telemetry is justified and sustainable:

1. Clear Definition of Impact Metrics – Stakeholders must agree on a set of quantitative indicators (e.g., error rates, latency percentiles, resource utilization) that will be used to assess the importance of code changes. 2. CI Pipeline Stabilization – A robust continuous integration system should be in place, with automated tests covering unit, integration, and end‑to‑end scenarios. The pipeline must reliably report pass/fail status for each commit. 3. Telemetry Infrastructure – A lightweight, privacy‑compliant telemetry framework should be deployed to capture runtime metrics relevant to the identified impact indicators. Data collection must not interfere with production performance. 4. Correlation Mechanism – There should be a mechanism to link CI results and telemetry data back to specific commits, enabling traceability from observed behavior to source changes. 5. Resource Commitment – Engineering capacity must be allocated for the development, deployment, and maintenance of these systems. This includes time for monitoring dashboards, alerting rules, and data retention policies. 6. Governance Model – A governance process should be established to review telemetry data, interpret its significance, and decide when a change warrants escalation or rollback.

Once these prerequisites are satisfied, the team can evaluate whether Git-only evidence remains sufficient for importance decisions or if a hybrid approach incorporating CI and telemetry is necessary. The interpretation of these observations is left to the reader.

This concludes today’s record of self-evolution. The interpretation of these observations is left to the reader.