Streamlining AI-Assisted Development: Separate Investigation from PR Creation for Better Productivity

AI-assisted development tools like GitHub Copilot are rapidly redefining the engineering landscape. They promise unprecedented boosts in productivity, but as with any powerful innovation, their integration into existing workflows requires careful optimization. A recent GitHub Community discussion, initiated by user codeputer, brings a critical friction point into sharp focus: the current intertwining of AI-powered investigation with the immediate creation of pull requests (PRs).

Untangling AI Investigation from PR Creation for Enhanced Productivity

For many developers, the initial phase of tackling an issue is all about understanding. This involves deep dives into codebases, tracing execution flows, pinpointing root causes, and brainstorming potential solutions. Codeputer's observation is spot-on: invoking an AI assistant for this investigative work often prematurely implies code changes and, consequently, the creation of a PR. This generates unnecessary "friction and noise" in the development process, diverting focus from the core problem.

Crucially, it also scatters invaluable investigation notes across various ephemeral chats, commit messages, or even abandoned partial PRs. This fragmentation makes it incredibly difficult to maintain a coherent software project dashboard of progress and findings, hindering knowledge transfer, future debugging efforts, and the ability to accurately track development metrics related to issue resolution.

The Case for an "Investigation Only" Mode

The core proposal is elegantly simple yet profoundly impactful: introduce a distinct "Investigation Only" mode for AI assistants. This mode would serve a singular, focused purpose: understanding the problem deeply before any action is taken. Its outputs would be purely analytical—a comprehensive summary of findings, identified risks, and clearly defined next steps—all meticulously documented directly within the relevant issue thread.

The key differentiator? In this mode, PR creation and any code modifications would be explicitly disabled. This clear distinction allows teams to leverage AI for deep dives without the overhead of premature implementation steps, significantly improving development metrics related to PR quality, cycle time, and overall team efficiency. It ensures that the AI acts as a true assistant, providing insights without forcing an immediate, potentially misguided, course of action.

Defining the Workflow: Investigation vs. Resolution

This separation formalizes two critical phases of software development that are often conflated, leading to a more structured and predictable workflow:

Investigation Mode: Thinking First

• Purpose: Deep understanding of the problem.
• Inputs: Repository context, logs, code paths, configurations, commit history.
• Outputs: Detailed analysis, root cause candidates, evidence (with file paths and references), observed behavior, open questions, identified risks, and recommended next steps.
• Documentation: All findings are written directly back to the issue thread, making it a living document of the investigation.
• Key Constraint: PR creation and code modifications are explicitly disabled. The AI may ask clarifying questions within the issue thread.

Resolution Mode: Fixing Second

• Purpose: Implementation of a decided fix.
• Trigger: Initiated only after the issue is fully documented with investigation findings and a clear path forward.
• Outputs: Code changes, tests, and a focused Pull Request.
• Key Benefit: PRs remain clean, concise, and focused solely on the agreed-upon solution, making them easier and faster to review.

Why This Matters for Your Team and Your Software Project Dashboard

The implications of adopting such a workflow are far-reaching, touching every aspect of a modern engineering organization:

• Issues Become the System of Record: The issue thread evolves into a comprehensive knowledge base, capturing the entire journey from problem identification to resolution, including all AI-generated insights. This significantly enhances the utility of your software project dashboard as a source of truth.
• PRs Stay Focused on Execution: By separating investigation, PRs become leaner and more purposeful. Reviewers can concentrate on the quality of the solution rather than sifting through investigative tangents.
• Reduced Premature or Abandoned PRs: Eliminating the pressure to immediately code reduces the number of 'work-in-progress' PRs that are later abandoned or require significant reworks, positively impacting development metrics like PR churn and cycle time.
• Copilot Aligns with Real Engineering Workflow: This mode allows AI tools to truly augment how engineers think and solve problems, rather than forcing a linear code-first approach.
• Investigation Becomes Auditable and Reviewable: With all findings documented in the issue, the investigative process itself becomes transparent, allowing for team learning, validation, and improved decision-making.

By making issues the definitive software project dashboard for all investigative knowledge, teams gain unparalleled clarity and traceability. This approach directly impacts development metrics. Imagine a reduction in premature PRs that get closed without merge, or a decrease in the average time spent reviewing PRs that contain investigative tangents. These tangible improvements contribute directly to a more efficient and predictable delivery pipeline, helping teams better understand their performance and identify areas for continuous improvement.

The Control Surface: Simple and Intuitive

The beauty of this proposed feature lies in its simplicity. Codeputer suggests a straightforward instruction or toggle, such as "Investigate only. Document findings in this issue. No PR." or "Analysis mode. Issue documentation only." This intuitive control surface ensures that developers can easily switch between modes, aligning the AI's behavior precisely with their current intent without ambiguity.

Conclusion: Separate Thinking from Fixing for Smarter Development

The request from the GitHub community is clear: treat investigation as a first-class workflow. Separate thinking from fixing. Let issues hold the knowledge. As AI tools become increasingly sophisticated, their integration must evolve to truly augment, rather than complicate, the human engineering process. By embracing an "Investigation Only" mode, we can unlock a new level of productivity, foster clearer communication, and build a more robust, auditable foundation for our software projects.

This isn't just about a new feature; it's about refining the very interaction model with AI in development, ensuring that these powerful tools serve our workflows, not dictate them. It's a critical step towards a future where every engineering decision is informed, documented, and contributes meaningfully to the overall health of the software project dashboard and the continuous improvement of development metrics. Technical leaders and project managers should champion this distinction, recognizing its potential to transform how their teams deliver value.