Feature request: Copilot request source visibility and diagnostics across tools

[miikkamakela-into]

🏷️ Discussion Type

Product Feedback

💬 Feature/Topic Area

Other

Body

Context

We are using GitHub Copilot across a team of ~18 developers in a variety of environments (IDE, CLI, agents, etc.).

Our goal is to understand what is actually driving request usage, and to optimize environments and workflows—not to monitor individual developers.

Problem

Currently, Copilot usage is aggregated in a way that does not clearly show where requests are coming from.

In practice, requests can originate from multiple sources:

• IDE (e.g. VS Code inline suggestions, chat)
• CLI / terminal usage
• Copilot agents
• External agents (e.g. Codex, Claude via Copilot integrations)
• Other tools or extensions using Copilot APIs

We lack visibility into:

• Which tools/interfaces generate the most requests
• Whether certain tools produce disproportionately high request volumes
• Whether high request usage is meaningful or caused by inefficiencies

Key concern

We need to be able to identify:

• Which interface is generating requests
  • VS Code (inline vs chat)
  • CLI / terminal
  • Agent-based workflows (Copilot, Codex, etc.)
• Request patterns per tool
• Potential inefficiencies, such as:
  • Loops or repeated request patterns
  • Tools or agents getting stuck
  • Automated or unintended request generation
  • High request volume with low acceptance

Requested features

Request source breakdown

• Requests categorized by source:
  • IDE (inline, chat)
  • CLI
  • Agents (Copilot, Codex, etc.)
• Per-source request volume over time

Diagnostics

• Detection of abnormal patterns (spikes, loops)
• Correlation between request volume and acceptance rate per source
• Signals for inefficient usage:
  • Repeated retries
  • Stuck agent/tool behavior
  • Low-value interactions

Environment-level insights

• Breakdown by:
  • IDE/editor
  • Programming language
• Comparison across environments and setups

Cost alignment

• Visibility into which tools drive the majority of request usage
• Identification of inefficient or unintended request sources
• Usage aligned with billing periods

Data access

• Exportable usage data (CSV)
• API access for internal diagnostics

Expected value

These capabilities would allow us to:

• Understand what is actually generating Copilot request usage
• Identify inefficient patterns (loops, retries, stuck tools)
• Optimize tool configuration and workflows
• Ensure Copilot is used where it provides real value
• Align cost with meaningful usage

Additional context

Without visibility into request sources, it is difficult to distinguish between:

• Productive usage
• Inefficient workflows
• Misbehaving tools or integrations

[github-actions[bot]]

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