Agent-native feedback intake & consolidation for Copilot (the missing "receiver")

[ebol2000]

🏷️ Discussion Type

Product Feedback

💬 Feature/Topic Area

Copilot in GitHub

Body

TL;DR

Copilot agents already hold rich, in-context knowledge of what I was doing, what failed, and my environment. What's missing isn't the ability to draft great feedback — it's a first-party Microsoft/GitHub endpoint to receive it. Please build the receiving + consolidation side so agents have a real destination for structured, consented, AI-assisted feedback.

Problem

Today's feedback paths (feedback boxes, thumbs up/down, community posts) make the user manually reconstruct context the agent already has — errors, repro steps, versions, environment. Worse, even if a client packaged perfect structured feedback, there is no agent-addressable intake API to send it to. Enterprise admins can't meaningfully route feedback upward because there's no documented Microsoft-side receiver for this. The sending half is trivial. The receiver is the gap.

Proposed solution

A Microsoft/GitHub-hosted feedback intake + LLM consolidation pipeline:

1. Intake API (the missing receiver). Authenticated, first-party endpoint accepting a structured feedback object: category, title, description, repro_steps, redacted_context, surface, version, severity, sentiment.
2. User-driven submission. The agent drafts a high-quality report from session context; the user reviews, edits, and explicitly approves before anything is sent. Nothing auto-forwards, ever.
3. LLM-side consolidation. Cluster, dedupe, and rank reports into themes; surface emerging issues in near-real-time on a PM-facing dashboard.
4. Closed loop. Return a reference/status so users can see whether a suggestion was received, themed, or actioned.

Privacy model

• Explicit, granular consent. Opt-in per submission; visibly user-driven; never silent.
• Local scrubbing first. Automatic secret/PII detection and redaction before anything leaves the machine; user sees exactly what will be sent.
• Enterprise governance. Admin policy controls, audit trail, data-residency guarantees for Business/Enterprise seats.
• Least data. Send only approved fields; prefer redacted/aggregated context over raw rows or full logs.

Consolidation approach

• Embed + cluster reports to group duplicates and near-duplicates.
• Rank themes by frequency × severity × sentiment; flag fast-emerging spikes.
• Auto-summarize each cluster into a PM-ready brief with representative examples.
• Feed ranked themes into triage/roadmap tooling to compress feedback → action from months to days.

Why it's high-value / low-cost

• The expensive context is already in the agent's hands — capture is nearly free.
• AI-normalized submissions massively raise signal-to-noise.
• LLM consolidation turns a firehose into ranked, deduped themes.

Requested outcome

Build and document the receiving + consolidation side so agent clients across Copilot surfaces have a first-party destination for structured, consented, AI-assisted feedback.

[github-actions[bot]]

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