Streamlining AI-Assisted Development: Separating Investigation for Clearer Software Project Dashboards

In the rapidly evolving landscape of AI-assisted development, tools like GitHub Copilot are transforming how engineers approach coding. However, as with any powerful tool, optimizing its integration into existing workflows is crucial for maximizing developer productivity. A recent GitHub Community discussion highlights a significant friction point: the current intertwining of AI-powered investigation with the immediate creation of pull requests (PRs).

Untangling AI Investigation from PR Creation

The discussion, initiated by user codeputer, brings to light a common challenge faced by developers using AI for initial problem diagnosis. Often, the first step in resolving an issue involves extensive investigation—reading code, tracing flows, identifying root causes, and outlining potential solutions. Codeputer notes that invoking Copilot for this purpose frequently leads to implied code changes and premature PR creation, generating "friction and noise." This also scatters vital investigation notes across various chats, commits, or partial PRs, making it difficult to maintain a coherent software project dashboard of progress and findings.

The Case for an "Investigation Only" Mode

The core proposal is to introduce a distinct "Investigation Only" mode for AI assistants. This mode would serve a singular purpose: understanding. Its outputs would be analytical—findings, risks, and next steps—all meticulously documented directly within the issue thread. Crucially, in this mode, PR creation and code modifications would be explicitly disabled. This clear distinction would allow teams to leverage AI for deep dives without the overhead of premature implementation steps, significantly improving development metrics related to PR quality and cycle time.

Defining the Workflow: Investigation vs. Resolution

• Investigation Mode:
  • Purpose: Understanding, analysis, root cause identification.
  • Outputs: Summaries, observed behavior, likely root causes, evidence (with file paths), open questions, and a recommendation on whether a PR is required.
  • Action: All output written back to the issue thread.
  • Restriction: PR creation and code changes are explicitly disabled.
• Resolution Mode:
  • Purpose: Implementation and fixing.
  • Outputs: Code changes and a PR.
  • Trigger: Only activated after the issue is fully documented and a clear path forward is established by the investigation.

This separation ensures that issues become the definitive system of record, holding all the knowledge gathered during the investigative phase. PRs, in turn, remain focused purely on execution, leading to cleaner, more targeted submissions. The proposed control surface could be as simple as an instruction like "Investigate only. Document findings in this issue. No PR." or an explicit toggle.

Why This Matters for Developer Productivity and Project Visibility

The benefits of this proposed feature are substantial:

• Enhanced System of Record: Issues evolve into comprehensive knowledge bases, making investigations auditable and reviewable. This provides a clearer software project dashboard for understanding problem evolution.
• Focused PRs: Eliminates premature or abandoned PRs, reducing noise and improving the signal-to-noise ratio in code reviews.
• Aligned Workflow: Better aligns AI tools with the natural engineering workflow, where thinking often precedes fixing.
• Improved Development Metrics: By reducing wasted effort and clarifying stages, teams can see better metrics around issue resolution time, PR quality, and overall efficiency. This could even serve as a valuable feature for those seeking a more integrated Logilica alternative for workflow management.

The success criteria are straightforward: no PRs during investigation, all AI output visible in the issue timeline, and developers retaining control over the switch to resolution. This insight underscores a critical need to treat investigation as a first-class workflow, allowing AI to empower thinking without prematurely forcing fixing, ultimately leading to more robust and transparent development processes.