DevOps AI Toolkit

# PRD: Remediation Playbooks System **Issue**: #107 **Created**: 2025-01-15 **Status**: Superseded **Closed**: 2025-11-19 **Priority**: Medium **Owner**: TBD ## Work Log ### 2025-11-19: PRD Closure - Superseded **Status**: Superseded / Redesigned **Closure Summary**: This PRD proposed creating a separate "Playbook System" for remediation. After architectural review, we decided to abandon the "Playbook" concept in favor of a unified approach where the Remediation tool integrates with the **existing Pattern & Policy System**. **Reasoning**: - **Unified Knowledge Base**: "Playbooks" are conceptually identical to "Patterns" and "Policies" (codified organizational knowledge). Creating a separate system creates silos. - **Better Architecture**: The Remediation tool should dynamically query existing Deployment Patterns (e.g., "Scaling Pattern") and Policies (e.g., "Resource Limits") based on its intended fix, rather than relying on a separate library of fix scripts. - **Next Steps**: A new PRD will be created to specify the technical integration of the Pattern/Policy search into the Remediation tool's workflow. ## Executive Summary Enhance the existing AI-powered Kubernetes remediation tool with an intelligent playbook system that both leverages existing knowledge and learns from successful remediations. The system matches user issues to proven solutions while capturing new successful remediations as playbooks, creating a continuously improving knowledge base. ## Problem Statement ### Current Challenges - AI remediation tool discovers solutions from scratch for every issue - No mechanism to leverage proven organizational remediation patterns - Successful remediations are lost after resolution - no knowledge capture - Inconsistent approaches to common problems across team members - No way to build institutional memory from remediation successes ### User Impact - **DevOps Teams**: Reinvent solutions for recurring issues and lose successful approaches - **On-call Engineers**: Lack access to proven remediation patterns during incidents - **Platform Teams**: Cannot standardize or share successful remediation knowledge - **Organizations**: Lose remediation expertise with no systematic knowledge capture ## Success Criteria - Reduce time-to-resolution for playbook-matched scenarios by 60% - Achieve 90% user satisfaction with playbook-enhanced remediation quality - Successfully match 70% of common issues to relevant playbooks within 6 months - Capture 80% of successful remediations as new playbooks when prompted - Build 50+ organizational playbooks through organic learning within first year ## Scope ### In Scope - Playbook system integrated with existing remediation tool - Intent matching to identify relevant playbooks for user issues - AI investigation enhancement using playbook context and strategies - **Post-remediation learning**: Convert successful remediations into playbooks - Playbook management and organic knowledge base growth ### Out of Scope - Replacing AI investigation with static playbooks (hybrid approach only) - Complex workflow orchestration beyond remediation guidance - Real-time collaborative playbook editing (initial version) - Integration with external ITSM systems (future enhancement) ## Goals ### Primary Goals 1. **Enhance Remediation Quality** - Provide AI with proven solution patterns from organizational experience - Reduce investigation time for issues with existing playbook knowledge - Improve consistency of remediation approaches across teams 2. **Organic Knowledge Building** - **Core Feature**: Capture successful remediations as new playbooks automatically - Convert real problem-solving successes into reusable organizational knowledge - Build knowledge base through actual usage rather than theoretical planning 3. **Accelerate Resolution Time** - Match user issues to relevant playbooks for faster initial guidance - Provide structured investigation steps based on previously successful approaches - Reduce time spent rediscovering solutions for similar problems 4. **Create Learning Feedback Loop** - Transform every successful remediation into potential organizational knowledge - Enable continuous improvement of remediation approaches - Build institutional memory that persists beyond individual team members ### Secondary Goals 1. **Knowledge Quality Assurance** - Only capture proven, successful remediations as playbooks - Real-world validation before knowledge preservation - User-driven quality control through voluntary playbook creation 2. **User Engagement** - Incentivize knowledge sharing through simple post-remediation prompts - Create ownership and investment in organizational knowledge base - Recognize contributors to collective remediation expertise ## Requirements ### Functional Requirements 1. **Playbook Matching and Enhancement** - Analyze user issue descriptions to identify relevant existing playbooks - Enhance AI investigation with playbook context and proven strategies - Maintain AI flexibility to adapt playbook approaches to specific contexts 2. **Post-Remediation Learning System** (Core Feature) - Prompt users to convert successful remediations into playbooks - Extract remediation patterns and investigation paths automatically - Generate suggested playbook content from successful remediation session - Enable user refinement of auto-generated playbook proposals 3. **Playbook Management** - Store and organize growing collection of organization-specific playbooks - Support playbook versioning and iterative improvement - Enable playbook discovery and search for future use 4. **User Experience Integration** - Seamless integration with existing remediation workflow - Clear indication when playbooks are being used for guidance - Simple yes/no prompt for playbook creation after successful remediation - Optional playbook content refinement interface ### Non-Functional Requirements - **Performance**: Playbook matching within 2 seconds, no impact on remediation speed - **Reliability**: No degradation of existing remediation tool performance - **Usability**: Playbook creation should require minimal user effort - **Storage**: Efficient storage and retrieval of growing playbook collection ## Implementation Milestones ### Milestone 1: Core Learning System ⬜ **Deliverable**: Post-remediation learning with automatic playbook generation - [ ] Integrate playbook creation prompt into existing remediation validation workflow - [ ] Implement automatic extraction of remediation patterns and investigation paths - [ ] Create playbook generation system from successful remediation sessions - [ ] Build playbook storage and retrieval system ### Milestone 2: Playbook Matching and AI Integration ⬜ **Deliverable**: Use existing playbooks to enhance new remediation investigations - [ ] Develop intent matching system to identify relevant playbooks for new issues - [ ] Integrate playbook context into AI investigation workflow - [ ] Implement hybrid approach balancing playbook guidance with AI flexibility - [ ] Create user experience for playbook-enhanced remediation ### Milestone 3: Knowledge Base Management ⬜ **Deliverable**: Tools for managing and organizing growing playbook collection - [ ] Implement playbook search and discovery capabilities - [ ] Create playbook categorization and tagging system - [ ] Develop playbook quality and effectiveness tracking - [ ] Build playbook update and versioning capabilities ### Milestone 4: User Experience Refinement ⬜ **Deliverable**: Polished experience for both playbook usage and creation - [ ] Enhance playbook creation interface with guided refinement - [ ] Implement clear attribution and source tracking for playbooks - [ ] Add playbook effectiveness feedback and improvement suggestions - [ ] Create usage analytics and adoption tracking ### Milestone 5: Production Optimization ⬜ **Deliverable**: System ready for large-scale organizational use - [ ] Performance optimization for large playbook collections - [ ] Advanced matching algorithms and relevance scoring - [ ] Documentation and training for organizational playbook best practices - [ ] Production monitoring and success metrics tracking ## Risks & Mitigations | Risk | Impact | Probability | Mitigation | |------|--------|------------|------------| | Users don't create playbooks when prompted | High | Medium | Make process extremely simple, show value of contributions | | Playbook quality degrades over time | Medium | Medium | User feedback, effectiveness tracking, optional review process | | Performance impact with large playbook collections | Medium | Low | Efficient indexing, caching, performance testing | | Over-reliance on playbooks vs AI reasoning | Medium | Low | Maintain AI primacy, use playbooks as guidance only | ## Dependencies - Existing remediation tool (PRD #97) with validation workflow must be stable - User adoption of existing remediation tool to generate learning opportunities - Understanding that knowledge quality depends on user participation ## Future Enhancements 1. **Machine Learning Enhancement**: Improve playbook matching through usage pattern analysis 2. **Collaborative Refinement**: Multi-user playbook review and improvement workflows 3. **Cross-Organization Sharing**: Anonymous playbook sharing across organizations 4. **Predictive Playbook Creation**: Suggest playbook creation for frequently occurring issue patterns 5. **Integration Analytics**: Detailed effectiveness tracking and ROI measurement ## Open Questions 1. **User Adoption**: What incentives ensure users participate in playbook creation? 2. **Quality Control**: Should playbook creation be completely voluntary or include review options? 3. **Playbook Format**: What level of structure provides best balance of automation and usability? 4. **Privacy**: How should sensitive organizational information be handled in playbooks? 5. **Matching Algorithm**: Priority between keyword-based and semantic similarity matching? ## Resolved Decisions *None yet - decisions will be documented as implementation progresses* ## Progress Log ### 2025-01-15 - Initial PRD created with focus on organic knowledge building - Post-remediation learning identified as core feature rather than enhancement - Feedback loop approach prioritized over pre-built playbook libraries - Integration with existing remediation validation workflow established --- *This PRD is a living document and will be updated as the implementation progresses.*