Atomizer/docs/07_DEVELOPMENT/DEVELOPMENT_ROADMAP.md

Atomizer Development Roadmap

Vision: Transform Atomizer into an LLM-native engineering assistant for optimization

Last Updated: 2025-01-16

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Vision Statement

Atomizer will become an LLM-driven optimization framework where AI acts as a scientist/programmer/coworker that can:

• Understand natural language optimization requests
• Configure studies autonomously
• Write custom Python functions on-the-fly during optimization
• Navigate and extend its own codebase
• Make engineering decisions based on data analysis
• Generate comprehensive optimization reports
• Continuously expand its own capabilities through learning

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Architecture Philosophy

LLM-First Design Principles

1. Discoverability: Every feature must be discoverable and usable by LLM via feature registry
2. Extensibility: Easy to add new capabilities without modifying core engine
3. Safety: Validate all generated code, sandbox execution, rollback on errors
4. Transparency: Log all LLM decisions and generated code for auditability
5. Human-in-the-loop: Confirm critical decisions (e.g., deleting studies, pushing results)
6. Documentation as Code: Auto-generate docs from code with semantic metadata

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Development Phases

Phase 1: Foundation - Plugin & Extension System ✅

Timeline: 2 weeks Status: ✅ COMPLETED (2025-01-16) Goal: Make Atomizer extensible and LLM-navigable

Deliverables

1. Plugin Architecture ✅

   • Hook system for optimization lifecycle
     • pre_solve: Execute before solver launch
     • post_solve: Execute after solve, before extraction
     • post_extraction: Execute after result extraction
   • Python script execution at optimization stages
   • Plugin auto-discovery and registration
   • Hook manager with priority-based execution

2. Logging Infrastructure ✅

   • Detailed per-trial logs (trial_logs/)
     • Complete iteration trace
     • Design variables, config, timeline
     • Extracted results and constraint evaluations
   • High-level optimization log (optimization.log)
     • Configuration summary
     • Trial progress (START/COMPLETE entries)
     • Compact one-line-per-trial format
   • Context passing system for hooks
     • output_dir passed from runner to all hooks
     • Trial number, design variables, results

3. Project Organization ✅

   • Studies folder structure with templates
   • Comprehensive studies documentation (studies/README.md)
   • Model file organization (model/ folder)
   • Intelligent path resolution (atomizer_paths.py)
   • Test suite for hook system

Files Created:

optimization_engine/
├── plugins/
│   ├── __init__.py
│   ├── hook_manager.py                        # Hook registration and execution ✅
│   ├── pre_solve/
│   │   ├── detailed_logger.py                 # Per-trial detailed logs ✅
│   │   └── optimization_logger.py             # High-level optimization.log ✅
│   ├── post_solve/
│   │   └── log_solve_complete.py              # Append solve completion ✅
│   └── post_extraction/
│       ├── log_results.py                     # Append extracted results ✅
│       └── optimization_logger_results.py     # Append to optimization.log ✅

studies/
├── README.md                                   # Comprehensive guide ✅
└── bracket_stress_minimization/
    ├── README.md                               # Study documentation ✅
    ├── model/                                  # FEA files folder ✅
    │   ├── Bracket.prt
    │   ├── Bracket_sim1.sim
    │   └── Bracket_fem1.fem
    └── optimization_results/                   # Auto-generated ✅
        ├── optimization.log
        └── trial_logs/

tests/
├── test_hooks_with_bracket.py                 # Hook validation test ✅
├── run_5trial_test.py                         # Quick integration test ✅
└── test_journal_optimization.py               # Full optimization test ✅

atomizer_paths.py                               # Intelligent path resolution ✅

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Phase 2: Research & Learning System

Timeline: 2 weeks Status: 🟡 NEXT PRIORITY Goal: Enable autonomous research and feature generation when encountering unknown domains

Philosophy

When the LLM encounters a request it cannot fulfill with existing features (e.g., "Create NX materials XML"), it should:

1. Detect the knowledge gap by searching the feature registry
2. Plan research strategy prioritizing: user examples → NX MCP → web documentation
3. Execute interactive research asking the user first for examples
4. Learn patterns and schemas from gathered information
5. Generate new features following learned patterns
6. Test and validate with user confirmation
7. Document and integrate into knowledge base and feature registry

This creates a self-extending system that grows more capable with each research session.

Key Deliverables

Week 1: Interactive Research Foundation

1. Knowledge Base Structure

   • Create knowledge_base/ folder hierarchy
   • nx_research/ - NX-specific learned patterns
   • research_sessions/[date]_[topic]/ - Session logs with rationale
   • templates/ - Reusable code patterns learned from research

2. ResearchAgent Class (optimization_engine/research_agent.py)

   • identify_knowledge_gap(user_request) - Search registry, identify missing features
   • create_research_plan(knowledge_gap) - Prioritize sources (user > MCP > web)
   • execute_interactive_research(plan) - Ask user for examples first
   • synthesize_knowledge(findings) - Extract patterns, schemas, best practices
   • design_feature(synthesized_knowledge) - Create feature spec from learned patterns
   • validate_with_user(feature_spec) - Confirm implementation meets needs

3. Interactive Research Workflow

   • Prompt templates for asking users for examples
   • Example parser (extract structure from XML, Python, journal scripts)
   • Pattern recognition (identify reusable templates)
   • Confidence tracking (how reliable is this knowledge?)

Week 2: Web Integration & Feature Generation

4. Web Research Integration

   • WebSearch integration for NXOpen documentation
   • NXOpenTSE scraping for code examples
   • Siemens official docs search and parsing
   • Multi-source synthesis (combine user examples + web docs)

5. Feature Generation Pipeline

   • Code generator using learned templates
   • Feature registry auto-update
   • Documentation auto-generation (following FEATURE_REGISTRY_ARCHITECTURE.md format)
   • Unit test scaffolding from examples

6. Knowledge Base Management

   • Research session logging (questions, sources, findings, decisions)
   • Confidence score tracking (user-validated > MCP > web docs)
   • Knowledge retrieval (search past research before starting new)
   • Template library growth (extract reusable patterns from generated code)

Success Criteria

• Materials XML Example: LLM asks for example XML → learns schema → generates new material XMLs
• Knowledge Persistence: Research session saved with rationale, retrievable for future requests
• Multi-Source Synthesis: Combines user example + NXOpenTSE + official docs into one coherent feature
• Template Reuse: Second request for similar feature uses learned template (faster generation)
• User Validation Loop: Generated feature tested, user confirms it works, feedback integrated

Example Workflow

User: "Please create a new material XML for NX with titanium Ti-6Al-4V properties"

LLM (detects gap):
  "I don't have a feature for generating NX material XMLs yet. Let me research this.

  Do you have an example .xml material file I can learn from?"

User: [uploads steel_material.xml]

LLM (analyzes example):
  "Thanks! I've learned the NX material XML schema:
  - Root element: <PhysicalMaterial>
  - Required fields: Name, Density, YoungModulus, PoissonRatio, ThermalExpansion
  - Units specified via attributes

  Let me create titanium Ti-6Al-4V with these properties:
  - Density: 4430 kg/m³
  - Young's Modulus: 113.8 GPa
  - Poisson's Ratio: 0.342
  - Thermal Expansion: 8.6e-6 /K

  [Generates XML]

  Does this look correct?"

User: "Yes, perfect!"

LLM (documents):
  "Great! I've saved:
  1. NX material XML schema to knowledge_base/nx_research/material_xml_schema.md
  2. Template generator to optimization_engine/custom_functions/nx_material_generator.py
  3. Research session log to knowledge_base/research_sessions/2025-01-16_nx_materials/

  Next time you request a material, I can generate it instantly using this template!"

Files to Create

knowledge_base/
├── nx_research/
│   ├── material_xml_schema.md           # Learned from user example
│   ├── journal_script_patterns.md       # Common NXOpen patterns
│   └── best_practices.md                # Engineering guidelines
├── research_sessions/
│   └── 2025-01-16_nx_materials/
│       ├── user_question.txt            # Original request
│       ├── sources_consulted.txt        # User example, NXOpenTSE, etc.
│       ├── findings.md                  # What was learned
│       └── decision_rationale.md        # Why this implementation
└── templates/
    ├── xml_generation_template.py       # Learned from research
    └── journal_script_template.py

optimization_engine/
├── research_agent.py                    # Main ResearchAgent class
└── custom_functions/
    └── nx_material_generator.py         # Generated from learned template

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Phase 3: LLM Integration Layer

Timeline: 2 weeks Status: 🔵 Not Started Goal: Enable natural language control of Atomizer

Key Deliverables

1. Feature Registry - Centralized catalog of all Atomizer capabilities
2. Claude Skill - LLM can navigate codebase and understand architecture
3. Natural Language Parser - Intent recognition and entity extraction
4. Conversational Workflow - Multi-turn conversations with context preservation

Success Vision

User: "Create a stress minimization study for my bracket"
LLM: "I'll set up a new study. Please drop your .sim file in the study folder."

User: "Done. Vary wall_thickness from 3-8mm"
LLM: "Perfect! I've configured:
     - Objective: Minimize max von Mises stress
     - Design variable: wall_thickness (3.0-8.0mm)
     - Sampler: TPE with 50 trials
     Ready to start?"

User: "Yes!"
LLM: "Optimization running! View progress at http://localhost:8080"

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Phase 4: Dynamic Code Generation

Timeline: 3 weeks Status: 🔵 Not Started Goal: LLM writes and integrates custom code during optimization

Deliverables

1. Custom Function Generator

   • Template system for common patterns:
     • RSS (Root Sum Square) of multiple metrics
     • Weighted objectives
     • Custom constraints (e.g., stress/yield_strength < 1)
     • Conditional objectives (if-then logic)
   • Code validation pipeline (syntax check, safety scan)
   • Unit test auto-generation
   • Auto-registration in feature registry
   • Persistent storage in optimization_engine/custom_functions/

2. Journal Script Generator

   • Generate NX journal scripts from natural language
   • Library of common operations:
     • Modify geometry (fillets, chamfers, thickness)
     • Apply loads and boundary conditions
     • Extract custom data (centroid, inertia, custom expressions)
   • Validation against NXOpen API
   • Dry-run mode for testing

3. Safe Execution Environment

   • Sandboxed Python execution (RestrictedPython or similar)
   • Whitelist of allowed imports
   • Error handling with detailed logs
   • Rollback mechanism on failure
   • Logging of all generated code to audit trail

Files to Create:

optimization_engine/
├── custom_functions/
│   ├── __init__.py
│   ├── templates/
│   │   ├── rss_template.py
│   │   ├── weighted_sum_template.py
│   │   └── constraint_template.py
│   ├── generator.py          # Code generation engine
│   ├── validator.py          # Safety validation
│   └── sandbox.py            # Sandboxed execution
├── code_generation/
│   ├── __init__.py
│   ├── journal_generator.py  # NX journal script generation
│   └── function_templates.py # Jinja2 templates

---

Phase 5: Intelligent Analysis & Decision Support

Timeline: 3 weeks Status: 🔵 Not Started Goal: LLM analyzes results and guides engineering decisions

Deliverables

1. Result Analyzer

   • Statistical analysis module
     • Convergence detection (plateau in objective)
     • Pareto front identification (multi-objective)
     • Sensitivity analysis (which params matter most)
     • Outlier detection
   • Trend analysis (monotonic relationships, inflection points)
   • Recommendations engine (refine mesh, adjust bounds, add constraints)

2. Surrogate Model Manager

   • Quality metrics calculation
     • R² (coefficient of determination)
     • CV score (cross-validation)
     • Prediction error distribution
     • Confidence intervals
   • Surrogate fitness assessment
     • "Ready to use" threshold (e.g., R² > 0.9)
     • Warning if predictions unreliable
   • Active learning suggestions (which points to sample next)

3. Decision Assistant

   • Trade-off interpreter (explain Pareto fronts)
   • "What-if" analysis (predict outcome of parameter change)
   • Constraint violation diagnosis
   • Next-step recommendations

Example:

User: "Summarize optimization results"
→ LLM:
   Analyzes 50 trials, identifies best design at trial #34:
   - wall_thickness = 3.2mm (converged from initial 5mm)
   - max_stress = 187 MPa (target: 200 MPa ✓)
   - mass = 0.45 kg (15% lighter than baseline)

   Issues detected:
   - Stress constraint violated in 20% of trials (trials 5,12,18...)
   - Displacement shows high sensitivity to thickness (Sobol index: 0.78)

   Recommendations:
   1. Relax stress limit to 210 MPa OR
   2. Add fillet radius as design variable (currently fixed at 2mm)
   3. Consider thickness > 3mm for robustness

Files to Create:

optimization_engine/
├── analysis/
│   ├── __init__.py
│   ├── statistical_analyzer.py  # Convergence, sensitivity
│   ├── surrogate_quality.py     # R², CV, confidence intervals
│   ├── decision_engine.py       # Recommendations
│   └── visualizers.py           # Plot generators

---

Phase 6: Automated Reporting

Timeline: 2 weeks Status: 🔵 Not Started Goal: Generate comprehensive HTML/PDF optimization reports

Deliverables

1. Report Generator

   • Template system (Jinja2)
     • Executive summary (1-page overview)
     • Detailed analysis (convergence plots, sensitivity charts)
     • Appendices (all trial data, config files)
   • Auto-generated plots (Chart.js for web, Matplotlib for PDF)
   • Embedded data tables (sortable, filterable)
   • LLM-written narrative explanations

2. Multi-Format Export

   • HTML (interactive, shareable via link)
   • PDF (static, for archival/print)
   • Markdown (for version control, GitHub)
   • JSON (machine-readable, for post-processing)

3. Smart Narrative Generation

   • LLM analyzes data and writes insights in natural language
   • Explains why certain designs performed better
   • Highlights unexpected findings (e.g., "Counter-intuitively, reducing thickness improved stress")
   • Includes engineering recommendations

Files to Create:

optimization_engine/
├── reporting/
│   ├── __init__.py
│   ├── templates/
│   │   ├── executive_summary.html.j2
│   │   ├── detailed_analysis.html.j2
│   │   └── markdown_report.md.j2
│   ├── report_generator.py      # Main report engine
│   ├── narrative_writer.py      # LLM-driven text generation
│   └── exporters/
│       ├── html_exporter.py
│       ├── pdf_exporter.py      # Using WeasyPrint or similar
│       └── markdown_exporter.py

---

Phase 7: NX MCP Enhancement

Timeline: 4 weeks Status: 🔵 Not Started Goal: Deep NX integration via Model Context Protocol

Deliverables

1. NX Documentation MCP Server

   • Index full Siemens NX API documentation
   • Semantic search across NX docs (embeddings + vector DB)
   • Code examples from official documentation
   • Auto-suggest relevant API calls based on task

2. Advanced NX Operations

   • Geometry manipulation library
     • Parametric CAD automation (change sketches, features)
     • Assembly management (add/remove components)
     • Advanced meshing controls (refinement zones, element types)
   • Multi-physics setup
     • Thermal-structural coupling
     • Modal analysis
     • Fatigue analysis setup

3. Feature Bank Expansion

   • Library of 50+ pre-built NX operations
   • Topology optimization integration
   • Generative design workflows
   • Each feature documented in registry with examples

Files to Create:

mcp/
├── nx_documentation/
│   ├── __init__.py
│   ├── server.py                # MCP server implementation
│   ├── indexer.py               # NX docs indexing
│   ├── embeddings.py            # Vector embeddings for search
│   └── vector_db.py             # Chroma/Pinecone integration
├── nx_features/
│   ├── geometry/
│   │   ├── fillets.py
│   │   ├── chamfers.py
│   │   └── thickness_modifier.py
│   ├── analysis/
│   │   ├── thermal_structural.py
│   │   ├── modal_analysis.py
│   │   └── fatigue_setup.py
│   └── feature_registry.json    # NX feature catalog

---

Phase 8: Self-Improving System

Timeline: 4 weeks Status: 🔵 Not Started Goal: Atomizer learns from usage and expands itself

Deliverables

1. Feature Learning System

   • When LLM creates custom function, prompt user to save to library
   • User provides name + description
   • Auto-update feature registry with new capability
   • Version control for user-contributed features

2. Best Practices Database

   • Store successful optimization strategies
   • Pattern recognition (e.g., "Adding fillets always reduces stress by 10-20%")
   • Similarity search (find similar past optimizations)
   • Recommend strategies for new problems

3. Continuous Documentation

   • Auto-generate docs when new features added
   • Keep examples updated with latest API
   • Version control for all generated code
   • Changelog auto-generation

Files to Create:

optimization_engine/
├── learning/
│   ├── __init__.py
│   ├── feature_learner.py       # Capture and save new features
│   ├── pattern_recognizer.py    # Identify successful patterns
│   ├── similarity_search.py     # Find similar optimizations
│   └── best_practices_db.json   # Pattern library
├── auto_documentation/
│   ├── __init__.py
│   ├── doc_generator.py         # Auto-generate markdown docs
│   ├── changelog_builder.py     # Track feature additions
│   └── example_extractor.py     # Extract examples from code

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Final Architecture

Atomizer/
├── optimization_engine/
│   ├── core/                    # Existing optimization loop
│   ├── plugins/                 # NEW: Hook system (Phase 1) ✅
│   │   ├── hook_manager.py
│   │   ├── pre_solve/
│   │   ├── post_solve/
│   │   └── post_extraction/
│   ├── research_agent.py        # NEW: Research & Learning (Phase 2)
│   ├── custom_functions/        # NEW: User/LLM generated code (Phase 4)
│   ├── llm_interface/           # NEW: Natural language control (Phase 3)
│   ├── analysis/                # NEW: Result analysis (Phase 5)
│   ├── reporting/               # NEW: Report generation (Phase 6)
│   ├── learning/                # NEW: Self-improvement (Phase 8)
│   └── feature_registry.json    # NEW: Capability catalog (Phase 1) ✅
├── knowledge_base/              # NEW: Learned knowledge (Phase 2)
│   ├── nx_research/             # NX-specific patterns and schemas
│   ├── research_sessions/       # Session logs with rationale
│   └── templates/               # Reusable code patterns
├── .claude/
│   └── skills/
│       └── atomizer.md          # NEW: Claude skill (Phase 1) ✅
├── mcp/
│   ├── nx_documentation/        # NEW: NX docs MCP server (Phase 7)
│   └── nx_features/             # NEW: NX feature bank (Phase 7)
├── docs/
│   ├── FEATURE_REGISTRY_ARCHITECTURE.md  # NEW: Registry design (Phase 1) ✅
│   └── llm/                     # NEW: LLM-readable docs (Phase 1)
│       ├── capabilities.md
│       ├── examples.md
│       └── api_reference.md
├── dashboard/                   # Existing web UI
└── examples/                    # Example projects

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Example Use Cases

Use Case 1: Natural Language Optimization Setup

User: "Let's make a new optimization"

LLM: "I'll help you set up a new optimization study. What would you like to name it?"

User: "bracket_weight_reduction"

LLM: "Study created! Please drop your .sim and .prt files into:
     C:\Atomizer\optimization_results\bracket_weight_reduction\sim\"

User: "Done. I want to minimize displacement, play with wall_thickness (3-8mm)
      and fillet_radius (1-5mm)"

LLM: "Got it! Let me configure:
     - Objective: Minimize max displacement
     - Design variables:
       * wall_thickness: 3.0 - 8.0 mm
       * fillet_radius: 1.0 - 5.0 mm

     How many iterations would you like? (Recommend 50 for 2 variables)"

User: "50 sounds good"

LLM: "Perfect! Configuration saved. Ready to start optimization?
     This will run 50 FEA simulations (estimated time: 2-3 hours)"

User: "Start it!"

LLM: "Optimization launched! 🚀
     - Live dashboard: http://localhost:8080/dashboard
     - Study: bracket_weight_reduction
     - Status: Trial 1/50 running..."

Use Case 2: Custom Metric via LLM

User: "Let's implement a function to calculate the RSS of stress and displacement"

LLM: "I'll create a Root Sum Square (RSS) function for you. Let me confirm:

     RSS = sqrt(stress² + displacement²)

     Should I normalize the metrics first? (Recommended for different units)"

User: "Yes, normalize to their targets"

LLM: "Creating custom objective function... Done!

     File: optimization_engine/custom_functions/rss_stress_displacement.py

     def rss_objective(stress, displacement, stress_target=200, disp_target=1.0):
         norm_stress = stress / stress_target
         norm_disp = displacement / disp_target
         return sqrt(norm_stress**2 + norm_disp**2)

     Added to feature registry. You can now use 'rss_stress_displacement'
     as an objective in your optimization config."

Use Case 3: Surrogate Model Usage

User: "Can you use the surrogate model to predict stress for these parameters:
      wall_thickness = [3.5, 4.0, 4.5, 5.0, 5.5]"

LLM: "Checking surrogate model quality...

     Surrogate Model Status:
     - Training data: 50 trials
     - R² score: 0.94 (excellent)
     - CV score: 0.91 (good generalization)
     - Confidence: HIGH ✓

     Predictions:
     | Thickness | Predicted Stress | Confidence Interval |
     |-----------|------------------|---------------------|
     | 3.5 mm    | 245.2 MPa       | ±12.3 MPa          |
     | 4.0 mm    | 198.7 MPa       | ±8.1 MPa           |
     | 4.5 mm    | 165.4 MPa       | ±7.2 MPa           |
     | 5.0 mm    | 142.1 MPa       | ±9.5 MPa           |
     | 5.5 mm    | 126.8 MPa       | ±11.8 MPa          |

     Note: 4.0mm appears optimal (stress closest to 200 MPa target)"

Use Case 4: Optimization Report

User: "Make an optimization report for bracket_weight_reduction"

LLM: "Generating comprehensive optimization report...

     Analyzing 50 completed trials...
     Writing narrative insights...
     Generating plots...

     Report complete!

     📄 HTML: optimization_results/bracket_weight_reduction/report.html
     📄 PDF: optimization_results/bracket_weight_reduction/report.pdf

     Key Findings:
     - Best design: Trial #34
       * wall_thickness = 4.2 mm
       * fillet_radius = 3.1 mm
       * Displacement = 0.78 mm (22% below target)
       * Mass = 0.51 kg (18% lighter than baseline)

     - Sensitivity: Thickness has 3x more impact than fillet radius
     - Recommendation: Lock thickness at 4.2mm, explore other variables

     Open report? [Y/n]"

---

Success Metrics

Phase 1 Success ✅

• Hook system operational with 5 plugins created and tested
• Plugin auto-discovery and registration working
• Comprehensive logging system (trial logs + optimization log)
• Studies folder structure established with documentation
• Path resolution system working across all test scripts
• Integration tests passing (hook validation test)

Phase 2 Success (Research Agent)

• LLM detects knowledge gaps by searching feature registry
• Interactive research workflow (ask user for examples first)
• Successfully learns NX material XML schema from single user example
• Knowledge persisted across sessions (research session logs retrievable)
• Template library grows with each research session
• Second similar request uses learned template (instant generation)

Phase 3 Success (LLM Integration)

• LLM can create optimization from natural language in <5 turns
• 90% of user requests understood correctly
• Zero manual JSON editing required

Phase 4 Success (Code Generation)

• LLM generates 10+ custom functions with zero errors
• All generated code passes safety validation
• Users save 50% time vs. manual coding

Phase 5 Success (Analysis & Decision Support)

• Surrogate quality detection 95% accurate
• Recommendations lead to 30% faster convergence
• Users report higher confidence in results

Phase 6 Success (Automated Reporting)

• Reports generated in <30 seconds
• Narrative quality rated 4/5 by engineers
• 80% of reports used without manual editing

Phase 7 Success (NX MCP Enhancement)

• NX MCP answers 95% of API questions correctly
• Feature bank covers 80% of common workflows
• Users write 50% less manual journal code

Phase 8 Success (Self-Improving System)

• 20+ user-contributed features in library
• Pattern recognition identifies 10+ best practices
• Documentation auto-updates with zero manual effort

---

Risk Mitigation

Risk: LLM generates unsafe code

Mitigation:

• Sandbox all execution
• Whitelist allowed imports
• Code review by static analysis tools
• Rollback on any error

Risk: Feature registry becomes stale

Mitigation:

• Auto-update on code changes (pre-commit hook)
• CI/CD checks for registry sync
• Weekly audit of documented vs. actual features

Risk: NX API changes break features

Mitigation:

• Version pinning for NX (currently 2412)
• Automated tests against NX API
• Migration guides for version upgrades

Risk: User overwhelmed by LLM autonomy

Mitigation:

• Confirm before executing destructive actions
• "Explain mode" that shows what LLM plans to do
• Undo/rollback for all operations

---

Last Updated: 2025-01-16 Maintainer: Antoine Polvé (antoine@atomaste.com) Status: 🟢 Phase 1 Complete | 🟡 Phase 2 (Research Agent) - NEXT PRIORITY

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For Developers

Active development tracking: See DEVELOPMENT.md for:

• Detailed todos for current phase
• Completed features list
• Known issues and bug tracking
• Testing status and coverage
• Development commands and workflows