[Feature] AI Copilot - In-Session Intelligence #69

New Issue

2026-02-10T16:01:36Z

chihlasm commented

2026-02-10 16:01:36 +00:00

(Migrated from github.com)

Summary

An AI assistant within sessions that can: suggest next steps based on symptoms, generate tailored PowerShell/CLI commands, explain error messages, recommend relevant past sessions, and eventually auto-generate tree drafts.

Why

Makes junior engineers perform like seniors. MSPs struggle with training — juniors escalate too quickly, seniors are bottlenecked. An AI copilot that understands tree context + session notes + client history could dramatically reduce escalation rates.

Layers (Phased)

Layer 1: Smart Tree Suggestions

Paste ticket description → AI suggests tree + starting branch
Match ticket text against tree node content and tags

Layer 2: Session-Driven Tree Evolution

Aggregate data: "35% of engineers add 'Check MFA Token' after 'Auth Failed'"
Generate suggestions to tree author with one-click approval
Trees evolve from real usage

Layer 3: AI Tree Generation

Describe a problem → AI generates complete tree draft
Uses: similar trees, custom steps, session history, real commands
Senior reviews, tweaks, publishes

Key Risks

Accuracy matters enormously — bad IT advice can cause outages
Needs confidence indicators and human-in-the-loop
Cost control for LLM API calls
Cold start: needs session data to be useful

Depends On

#56 Step-Level Time Tracking (data foundation)
#65 Intelligence Loop (analytics engine)
Sufficient session volume (6+ months of real usage for Layer 2+)

Sources

`docs/plans/2026-02-04-feature-ideas-brainstorm.md` — Idea 10: AI Tree Intelligence
`.claude/docs/ai/resolutionflow/10x/session-1.md` — Massive Opportunity #4

Priority

Backlog — transformative but premature. Build after analytics foundation exists.

## Summary An AI assistant within sessions that can: suggest next steps based on symptoms, generate tailored PowerShell/CLI commands, explain error messages, recommend relevant past sessions, and eventually auto-generate tree drafts. ## Why Makes junior engineers perform like seniors. MSPs struggle with training — juniors escalate too quickly, seniors are bottlenecked. An AI copilot that understands tree context + session notes + client history could dramatically reduce escalation rates. ## Layers (Phased) ### Layer 1: Smart Tree Suggestions - Paste ticket description → AI suggests tree + starting branch - Match ticket text against tree node content and tags ### Layer 2: Session-Driven Tree Evolution - Aggregate data: "35% of engineers add 'Check MFA Token' after 'Auth Failed'" - Generate suggestions to tree author with one-click approval - Trees evolve from real usage ### Layer 3: AI Tree Generation - Describe a problem → AI generates complete tree draft - Uses: similar trees, custom steps, session history, real commands - Senior reviews, tweaks, publishes ## Key Risks - Accuracy matters enormously — bad IT advice can cause outages - Needs confidence indicators and human-in-the-loop - Cost control for LLM API calls - Cold start: needs session data to be useful ## Depends On - #56 Step-Level Time Tracking (data foundation) - #65 Intelligence Loop (analytics engine) - Sufficient session volume (6+ months of real usage for Layer 2+) ## Sources - \`docs/plans/2026-02-04-feature-ideas-brainstorm.md\` — Idea 10: AI Tree Intelligence - \`.claude/docs/ai/resolutionflow/10x/session-1.md\` — Massive Opportunity #4 ## Priority **Backlog** — transformative but premature. Build after analytics foundation exists.

chihlasm commented

2026-03-21 15:24:37 +00:00

(Migrated from github.com)

Completed — FlowPilot AI copilot is fully implemented with context-aware guidance, next-step suggestions, confidence-tiered model routing, and auto-documentation.

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Reference: chihlasm/resolutionflow#69