Implement critical production readiness features for AI integration: per-request provider config, admin dashboard, workflow inspection, and plugin framework foundation.
Per-Request Provider Config:
- Add ProviderConfig struct to all AI request types
- Update queue to resolve provider/model/API key from cascade (repo > org > system)
- Pass resolved config to AI sidecar on every request
- Fixes multi-tenant issue where all orgs shared sidecar's hardcoded config
Admin AI Dashboard:
- Add /admin/ai page with sidecar health status
- Display global operation stats (total, 24h, success/fail/escalated counts)
- Show operations by tier, top 5 repos, token usage
- Recent operations table with repo, operation, status, duration
- Add GetGlobalOperationStats model method
Workflow Inspection:
- Add InspectWorkflow client method and types
- Implement workflow-inspect queue handler
- Add notifier trigger on workflow file push
- Analyzes YAML for syntax errors, security issues, best practices
- Returns structured issues with line numbers and suggested fixes
Plugin Framework (Phase 5 Foundation):
- Add external plugin config loading from app.ini
- Define ExternalPlugin interface and manager
- Add plugin.proto contract (Initialize, Shutdown, HealthCheck, OnEvent, HandleHTTP)
- Implement health monitoring with auto-restart for managed plugins
- Add event routing to subscribed plugins
- HTTP proxy support for plugin-served routes
This completes Tasks 1-4 from the production readiness plan and establishes the foundation for managed plugin lifecycle.
Implement core AI service infrastructure including agent operations, escalation handling, and asynchronous queue processing.
New services:
- Agent service: Handles Tier 2 AI operations with action runner integration
- Queue service: Asynchronous processing of AI operations with retry logic
- Escalation service: Routes complex issues to staff with configurable rules
- Notifier service: Sends notifications for AI operation results
Additional changes:
- Add GitCaddy AI system user (ID: -3) for bot operations
- Add AIConfig to repository units
- Add AI-specific error codes (rate limiting, service errors, etc.)
- Extend AI client with GenerateIssueResponse method
- Add AISettingsV2 struct for repository-level AI configuration
The queue system enables non-blocking AI operations with proper error handling and rate limiting.
- Fix gofumpt extra-rules: combine repeated types in function signature
- Fix gofmt formatting in setting/ai.go and web.go
- Add explanations to nolint:dupl directives as required by nolintlint
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Replace fmt.Errorf with errors.New for static error messages
- Use modules/json instead of encoding/json (depguard)
- Add nolint:dupl for intentionally similar API handlers
- Explicitly ignore Session.Set return values (errcheck)
- Apply gofmt formatting
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Integrate GitCaddy AI service with support for code review, issue triage, documentation generation, code explanation, and chat interface. Add AI client module with HTTP communication, configuration settings, API routes (web and REST), service layer, and UI templates for issue sidebar. Include comprehensive configuration options in app.example.ini for enabling/disabling features and service connection settings.
