FTE+AI is an end-to-end program execution framework that guides R&D organizations through the complete journey of vendor replacement—from initial planning through successful cutover and optimization.
개요
FTE+AI is an end-to-end program execution framework that guides R&D organizations through the complete journey of vendor replacement—from initial planning through successful cutover and optimization.
README
FTE+AI: Vendor Replacement Program Framework
A complete 30-60-90 day program for replacing outsourcing vendors with AI-augmented teams.
Overview
FTE+AI is an end-to-end program execution framework that guides R&D organizations through the complete journey of vendor replacement—from initial planning through successful cutover and optimization. Unlike documentation toolkits, this is a hands-on program management system with specialized AI agents that help you plan, execute, and optimize each phase of your vendor replacement initiative.
What You Get:
- ✅ Complete 30-60-90 day execution roadmap
- ✅ 18 specialized AI agents for each program phase
- ✅ 23 reusable skills for program execution and vendor transition
- ✅ Ready-to-use templates, checklists, and tracking tools
- ✅ Risk mitigation and rollback planning
- ✅ Financial modeling and ROI validation
- ✅ NEW: Local AI deployment with complete data sovereignty
- ✅ NEW: Hardware sizing and self-hosted LLM guidance
- ✅ NEW: Open-source model evaluation and licensing compliance
🚀 Program Phases
Phase 1: Planning & Preparation (Days 1-30)
Goal: Build business case, select tools, prepare team
- Executive alignment and budget approval
- Tool evaluation and selection
- Pilot team identification
- Risk assessment and mitigation planning
- Initial training and setup
Phase 2: Pilot & Validation (Days 31-60)
Goal: Prove AI approach works with real projects
- Pilot team execution with AI tools
- Quality and productivity measurement
- Cost validation
- Parallel run with vendor
- Go/No-Go decision
Phase 3: Transition & Scale (Days 61-90)
Goal: Full team rollout and vendor cutover
- Team-wide training and deployment
- Vendor contract wind-down
- Knowledge transfer completion
- Full production cutover
- Post-transition optimization
🤖 Available Agents
Phase 1 Agents: Planning & Preparation (Days 1-30)
1. Program-Manager Agent
Program orchestrator for end-to-end vendor replacement execution.
Use for:
- Creating comprehensive 30-60-90 day program plans
- Tracking milestones and deliverables across all phases
- Managing phase gates and go/no-go decisions
- Coordinating all specialized agents
- Executive status reporting
- Risk escalation and mitigation
Invocation: @Program-Manager
Skills used: #program-planning, #milestone-tracking, #risk-assessment, #stakeholder-management
2. Executive-Strategy-Advisor Agent
Strategic advisor for C-suite and board-level decision making.
Use for:
- Executive summaries and board presentations
- Strategic roadmaps for AI transformation
- Business case development for leadership
- Competitive analysis and market positioning
- Long-term AI strategy and governance frameworks
Invocation: @Executive-Strategy-Advisor
Skills used: #financial-modeling, #data-visualization, #document-structure, #technical-writing
3. Documaster Agent
Primary documentation agent for program documentation and guides.
Use for:
- Program charter and planning documents
- Technical guides and specifications
- Executive presentations
- Reference materials
- Knowledge base content
Invocation: @Documaster
Skills used: #document-structure, #technical-writing, #ai-terminology, #code-examples
4. ROI Calculator Agent
Financial analysis specialist for vendor replacement business cases.
Use for:
- Cost-benefit analyses and ROI models
- Vendor vs. AI cost comparisons
- TCO calculations and payback analysis
- Budget planning and financial justification
Invocation: @ROI-Calculator
Skills used: #financial-modeling, #data-visualization, #document-structure, #technical-writing
5. Tool Evaluation Specialist Agent
Tool selection specialist for objective AI vendor evaluation.
Use for:
- Tool comparison matrices and scorecards
- Proof-of-concept evaluation frameworks
- Build vs. buy decision support
- Vendor stability and lock-in assessments
Invocation: @Tool-Evaluation-Specialist
Skills used: #tool-evaluation, #technical-writing, #data-visualization, #code-examples, #financial-modeling
6. Security-Risk-Compliance-Advisor Agent
Comprehensive security, risk, and compliance specialist for enterprise AI adoption.
Use for:
- Security architecture and data protection
- Risk assessment and mitigation frameworks
- Compliance documentation (GDPR, SOC2, HIPAA)
- Vendor security assessments
Invocation: @Security-Risk-Compliance-Advisor
Skills used: #risk-assessment, #legal-compliance, #document-structure, #technical-writing
7. Legal-Contract-Advisor Agent
Legal and contract specialist for AI vendor agreements and compliance.
Use for:
- AI vendor contract review and negotiation
- Intellectual property protection strategies
- Data processing agreement templates
- Vendor exit and transition contract terms
Invocation: @Legal-Contract-Advisor
Skills used: #legal-compliance, #vendor-transition, #risk-assessment, #document-structure
Phase 2 Agents: Pilot & Validation (Days 31-60)
8. Implementation Guide Agent
Tutorial specialist for practical step-by-step guidance.
Use for:
- Tool onboarding and setup tutorials
- Integration guides and workflows
- Troubleshooting documentation
- Quick-start guides and training materials
Invocation: @Implementation-Guide
Skills used: #document-structure, #technical-writing, #code-examples, #ai-terminology
9. Performance-Optimization-Agent
Performance specialist for AI system optimization and efficiency.
Use for:
- AI tool performance benchmarking
- Cost optimization and usage efficiency
- Baseline vs. pilot metrics comparison
- Monitoring and observability setup
Invocation: @Performance-Optimization-Agent
Skills used: #metrics-analytics, #api-integration, #code-examples, #technical-writing
10. Change Management Coach Agent
Organizational adoption specialist for training and rollout.
Use for:
- Pilot team training and enablement
- Adoption metrics and tracking
- Resistance management strategies
- Champion network building
- Stakeholder engagement plans
Invocation: @Change-Management-Coach
Skills used: #change-management, #document-structure, #technical-writing, #data-visualization, #ai-terminology
Phase 3 Agents: Transition & Scale (Days 61-90)
11. Vendor-Transition-Manager Agent
Vendor transition specialist for contract wind-down and knowledge transfer.
Use for:
- 30/60/90-day vendor transition plans
- Knowledge transfer frameworks
- Parallel run and cutover procedures
- Stakeholder communication during transition
- Risk mitigation during vendor replacement
Invocation: @Vendor-Transition-Manager
Skills used: #vendor-transition, #change-management, #risk-assessment, #financial-modeling, #document-structure, #technical-writing
Support Agents: All Phases
12. Case Study Documenter Agent
Success story specialist for real-world examples and metrics.
Use for:
- Vendor replacement success stories
- Before/after comparisons
- Lessons learned documentation
- Reference materials and best practices
Invocation: @Case-Study-Documenter
Skills used: #document-structure, #technical-writing, #data-visualization, #ai-terminology
13. API-Integration-Specialist Agent
Technical integration expert for AI APIs and enterprise connectivity.
Use for:
- AI API integration guides and tutorials
- SDK implementation and abstraction layers
- Authentication and security patterns
- Multi-provider strategies and failover
- Performance optimization for API calls
Invocation: @API-Integration-Specialist
Skills used: #api-integration, #code-examples, #technical-writing, #document-structure
Local AI Infrastructure Agents: Data Sovereignty & Self-Hosted Deployment
14. Local-AI-Infrastructure-Architect Agent
Infrastructure specialist for designing and deploying self-hosted LLM platforms with complete data sovereignty.
Use for:
- Hardware sizing for local AI deployments (GPU, CPU, RAM, storage)
- Platform selection (prefer vLLM or SGLang for serving; use llama.cpp for endpoints)
- Containerized deployment (Docker, Kubernetes)
- Air-gapped and network-isolated deployments
- High availability and load balancing configuration
Invocation: @Local-AI-Infrastructure-Architect
Skills used: #local-ai-deployment, #hardware-sizing, #production-readiness, #risk-assessment
15. Open-Source-Model-Evaluator Agent
Model selection specialist for evaluating and recommending open-source LLMs for enterprise use.
Use for:
- Open-source model comparison and benchmarking
- License compliance assessment for commercial use
- Model selection for specific use cases (code, chat, reasoning)
- Fine-tuning feasibility and ROI analysis
- Model upgrade and deprecation planning
Invocation: @Open-Source-Model-Evaluator
Skills used: #open-source-licensing, #tool-evaluation, #risk-assessment, #technical-writing
16. Data-Sovereignty-Advisor Agent
Compliance specialist for ensuring complete data control and regulatory compliance in AI systems.
Use for:
- GDPR, HIPAA, CCPA compliance for AI systems
- Data classification and handling policies
- PII detection and protection strategies
- Audit trail and access control design
- Data residency and localization requirements
Invocation: @Data-Sovereignty-Advisor
Skills used: #data-sovereignty, #risk-assessment, #legal-compliance, #document-structure
17. Vendor-Relationship-Manager Agent
Relationship specialist for managing vendor negotiations, contracts, and professional transitions.
Use for:
- AI vendor contract negotiation
- Pricing model analysis and optimization
- SLA design and enforcement
- Vendor exit strategies and professional transitions
- Multi-vendor portfolio management
Invocation: @Vendor-Relationship-Manager
Skills used: #vendor-negotiation, #vendor-transition, #risk-assessment, #financial-modeling
18. MLOps-Engineer Agent
Operations specialist for running production local AI infrastructure with enterprise-grade reliability.
Use for:
- Production deployment and operations
- Monitoring, alerting, and observability setup
- Incident management and response procedures
- Capacity planning and scaling strategies
- Model lifecycle management and updates
Invocation: @MLOps-Engineer
Skills used: #mlops-operations, #local-ai-deployment, #production-readiness, #metrics-analytics
🎯 Available Skills
Core Program Execution Skills
1. Program Planning Skill (#program-planning)
Expertise: Creating and executing 30-60-90 day vendor replacement programs
Provides: 30-60-90 day planning frameworks, milestone management, resource allocation, phase gates, program charters
2. Milestone Tracking Skill (#milestone-tracking)
Expertise: Tracking program milestones, deliverables, and dependencies
Provides: Tracking templates, dependency mapping, status reporting, dashboards
3. Stakeholder Management Skill (#stakeholder-management)
Expertise: Managing stakeholder engagement and communication
Provides: Power-interest matrix, engagement strategies, resistance management, communication plans
Documentation & Communication Skills
4. Document Structure Skill (#document-structure)
Expertise: Information architecture and organization patterns
Provides: Hierarchies, templates, navigation, readability optimization
5. Technical Writing Skill (#technical-writing)
Expertise: Clear, concise technical communication
Provides: Writing guidelines, active voice, audience adaptation, quality checklists
6. AI Terminology Skill (#ai-terminology)
Expertise: Consistent AI/ML terminology and definitions
Provides: AI/ML glossary, vendor-neutral language, concept explanations
7. Code Examples Skill (#code-examples)
Expertise: Code samples, patterns, and security guidelines
Provides: Working code samples, integration patterns, security best practices, error handling
Financial & Analysis Skills
8. Data Visualization Skill (#data-visualization)
Expertise: Charts, tables, and metrics presentation
Provides: Chart selection, dashboard design, metrics visualization, executive summaries
9. Financial Modeling Skill (#financial-modeling)
Expertise: ROI calculations, TCO analysis, and cost models
Provides: ROI frameworks, TCO models, cost-benefit analysis, payback calculations, budget planning
Enterprise Transition Skills
10. Vendor Transition Skill (#vendor-transition)
Expertise: Exit planning, knowledge transfer, cutover strategies
Provides: Transition frameworks, knowledge transfer templates, parallel run procedures, cutover checklists
11. Risk Assessment Skill (#risk-assessment)
Expertise: Security, compliance, and business risk analysis
Provides: Risk identification, security frameworks, compliance mapping, risk scoring, mitigation planning
12. Change Management Skill (#change-management)
Expertise: Adoption strategies, training, stakeholder engagement
Provides: Change readiness, training design, adoption metrics, resistance management, communication strategies
13. Tool Evaluation Skill (#tool-evaluation)
Expertise: Scorecards, POC frameworks, selection criteria
Provides: Comparison matrices, evaluation scorecards, POC planning, build vs. buy frameworks
Technical Integration Skills
14. API Integration Skill (#api-integration)
Expertise: API/SDK integration patterns and best practices
Provides: Authentication strategies, error handling, rate limiting, multi-provider architectures
15. Legal Compliance Skill (#legal-compliance)
Expertise: Contract law, IP protection, and vendor agreements
Provides: Contract review, IP protection, data processing agreements, licensing compliance, vendor exit terms
16. Metrics Analytics Skill (#metrics-analytics)
Expertise: Productivity measurement and ROI validation
Provides: Productivity frameworks, baseline measurement, performance tracking, ROI validation, continuous improvement
17. Production Readiness Skill (#production-readiness)
Expertise: Enterprise deployment and operational excellence
Provides: Production checklists, deployment strategies, monitoring, incident response, operational runbooks
Local AI & Data Sovereignty Skills
18. Local AI Deployment Skill (#local-ai-deployment)
Expertise: Self-hosted LLM platforms and enterprise deployment
Provides: Platform comparison (vLLM, SGLang, TGI, llama.cpp), Docker/Kubernetes configs, air-gapped deployment, API gateway setup
19. Hardware Sizing Skill (#hardware-sizing)
Expertise: GPU and server specification for AI workloads
Provides: GPU selection (RTX 4090 to H100), server configs by team size, VRAM requirements, TCO modeling
20. Open Source Licensing Skill (#open-source-licensing)
Expertise: License compliance for AI models and tools
Provides: License classification (permissive OSS, copyleft, provider open-weights, RAIL-style, commercial EULA), commercial use assessment, compliance documentation
21. Data Sovereignty Skill (#data-sovereignty)
Expertise: Data residency, privacy, and regulatory compliance
Provides: Data classification frameworks, GDPR/HIPAA/CCPA checklists, PII handling, audit trail design
22. MLOps Operations Skill (#mlops-operations)
Expertise: Production AI system operations and maintenance
Provides: Monitoring configuration, alerting rules, operational runbooks, capacity planning, cost optimization
23. Vendor Negotiation Skill (#vendor-negotiation)
Expertise: Contract negotiation and vendor relationship management
Provides: Negotiation frameworks, pricing analysis, contract terms, SLA design, multi-vendor strategies
📚 Documentation Library
- docs/Introduction.md
- docs/Enterprise-Deployment-Guide.md
- docs/ROI-Calculator-Template.md
- docs/production-readiness/README.md
- docs/programs/dcs-microshift-vendor-replacement/README.md
Quick Start
Creating the 30-60-90 Program Plan
@Program-Manager Create a 30-60-90 day vendor replacement program plan
Context: We currently use [vendor] for [scope]
Constraints: [data policy], [timeline], [no downtime]
Output: Phase 1/2/3 plan + milestones + phase gates + risks + stakeholder map
Creating a Program Charter / Internal Docs
@Documaster Create the program charter for our FTE+AI initiative
Target audience: R&D managers and developers
Include: scope, assumptions, governance, milestones, phase gates, and success metrics
Creating a Financial Analysis
@ROI-Calculator Create a cost comparison between our offshore vendor
and AI-augmented FTEs
Current vendor: 3 developers at $50/hour
Team size: 5 FTEs
Calculate 3-year ROI
Creating a Tutorial
@Implementation-Guide Create a step-by-step tutorial for
"Integrating GPT-4 into our code review workflow"
Include: Setup, authentication, code examples, and troubleshooting
Creating a Case Study
@Case-Study-Documenter Document our recent vendor replacement project
Team: QA team (4 people)
Vendor replaced: Offshore testing service
Results: 60% cost reduction, 2x faster testing
Creating a Vendor Transition Plan
@Vendor-Transition-Manager Create a 60-day transition plan
Vendor: Offshore QA team (6 people)
Scope: Regression testing + test automation
Constraints: No production downtime
Include: Knowledge transfer, parallel run, cutover checklist
Creating a Risk & Compliance Assessment
@Security-Risk-Compliance-Advisor Assess AI tool risks for code review
Tools: GitHub Copilot + GPT-4 API
Data: Internal repositories, no PII allowed
Regulatory: SOC2 + GDPR
Output: Risk register + mitigation plan
Deploying Local AI for Data Sovereignty
@Local-AI-Infrastructure-Architect Design local AI infrastructure
Team size: 30 developers
Requirements: Complete data sovereignty, no external API calls
Constraints: $50K budget, GDPR compliant
Output: Hardware specs, platform selection, deployment architecture
Evaluating Open Source Models
@Open-Source-Model-Evaluator Select model for code generation
Use case: Code completion and review
Requirements: Apache 2.0 or MIT license, <48GB VRAM
Quality target: 90% of GPT-4 on code tasks
Output: Model comparison, license analysis, recommendation
Ensuring Data Sovereignty Compliance
@Data-Sovereignty-Advisor Create compliance framework
Regulations: GDPR + HIPAA
Data types: Source code, internal documents
AI deployment: Self-hosted Qwen-Next / MiniMax-M2 / GLM-4.6 (served via vLLM/SGLang; optional llama.cpp endpoints)
Output: Data classification, compliance checklist, audit procedures
Project Structure
FTE+AI/
├── AGENTS.md # Agent & skill reference
├── README.md # This file
├── LICENSE # MIT License
├── agents/ # 18 specialized program agents
├── skills/ # 23 reusable skills
└── docs/ # Guides, templates, and readiness artifacts
├── Introduction.md
├── Enterprise-Deployment-Guide.md
├── ROI-Calculator-Template.md
└── production-readiness/ # Enterprise readiness package
├── README.md
├── 00-Executive-Summary.md
├── 01-Tool-Evaluation-Scorecard.md
├── 02-POC-Plan.md
├── 03-Risk-Assessment-Register.md
├── 04-Security-Compliance-Checklist.md
├── 05-Vendor-Transition-Plan.md
├── 06-Training-Adoption-Plan.md
├── 07-Operations-Monitoring-Plan.md
├── 08-Incident-Response-Runbook.md
└── 09-Go-No-Go-Signoff.md
Usage Patterns
Pattern 1: Multi-Agent Collaboration
For comprehensive documentation requiring multiple perspectives:
1. @ROI-Calculator Create financial justification for AI adoption
2. @Implementation-Guide Create setup tutorial
3. @Case-Study-Documenter Document pilot program results
4. @Documaster Compile everything into cohesive guide
Pattern 2: Skill-Enhanced Requests
Reference specific skills for focused expertise:
@Documaster Using #financial-modeling and #data-visualization,
create an ROI dashboard for executives showing vendor replacement savings
Pattern 3: Iterative Refinement
Build documentation incrementally:
1. @Documaster Create outline for "AI Code Review Guide"
2. [Review outline with team]
3. @Documaster Expand section 3 with #code-examples
4. [Review and iterate]
5. @Documaster Finalize with #technical-writing quality check
Pattern 4: Enterprise Readiness Bundle
For production readiness in enterprise environments:
1. @Tool-Evaluation-Specialist Create tool evaluation scorecard
2. @Security-Risk-Compliance-Advisor Produce security and compliance checklist
3. @Vendor-Transition-Manager Create transition plan and cutover checklist
4. @Change-Management-Coach Build training and adoption plan
5. @Documaster Compile into executive-ready package
Pattern 5: Local AI Deployment (Data Sovereignty)
For organizations requiring complete data sovereignty:
1. @Local-AI-Infrastructure-Architect Size hardware and select platform (vLLM, SGLang, llama.cpp)
2. @Open-Source-Model-Evaluator Select models with license compliance
3. @Data-Sovereignty-Advisor Ensure GDPR/HIPAA compliance
4. @MLOps-Engineer Deploy and configure production infrastructure
5. @Vendor-Relationship-Manager Negotiate any cloud vendor transitions
6. @Implementation-Guide Create training materials for the team
Pattern 6: Cloud to Local AI Migration
For transitioning from cloud APIs to self-hosted models:
1. @ROI-Calculator Compare cloud API vs. local AI TCO
2. @Open-Source-Model-Evaluator Find open-source alternatives
3. @Local-AI-Infrastructure-Architect Design local infrastructure
4. @Data-Sovereignty-Advisor Validate data handling compliance
5. @Vendor-Transition-Manager Plan cloud vendor wind-down
6. @MLOps-Engineer Execute deployment and migration
Documentation Standards
All agents follow these principles:
- Clarity: Simple, direct language
- Accuracy: Technically correct information
- Consistency: Unified terminology and style
- Actionability: Practical, implementable guidance
- Context: Appropriate for R&D audiences
Enterprise Readiness (Production)
This framework is built for enterprise production use. Top concerns are addressed through dedicated agents, skills, and checklists:
- Security and compliance: @Security-Risk-Compliance-Advisor with #risk-assessment, #legal-compliance
- API integration: @API-Integration-Specialist with #api-integration, #code-examples
- Legal and contracts: @Legal-Contract-Advisor with #legal-compliance, #vendor-transition
- Vendor transition: @Vendor-Transition-Manager with #vendor-transition, #change-management
- Tool evaluation: @Tool-Evaluation-Specialist with #tool-evaluation, #financial-modeling
- Change management: @Change-Management-Coach with #change-management
- Executive strategy: @Executive-Strategy-Advisor with #financial-modeling, #data-visualization
- Performance optimization: @Performance-Optimization-Agent with #metrics-analytics, #api-integration
- Cost control and ROI: @ROI-Calculator with #financial-modeling
- Implementation guides: @Implementation-Guide with #code-examples
- Local AI infrastructure: @Local-AI-Infrastructure-Architect with #local-ai-deployment, #hardware-sizing
- Model selection and licensing: @Open-Source-Model-Evaluator with #open-source-licensing, #tool-evaluation
- Data sovereignty and compliance: @Data-Sovereignty-Advisor with #data-sovereignty, #risk-assessment
- Vendor negotiations: @Vendor-Relationship-Manager with #vendor-negotiation, #vendor-transition
- MLOps and operations: @MLOps-Engineer with #mlops-operations, #production-readiness
Customization
Adding New Agents
Create a new file in agents/:
---
description: 'Your agent description'
tools: []
---
# Agent Name
## Purpose
[What this agent does]
## Core Responsibilities
[Key tasks]
## When to Use This Agent
[Use cases]
[Additional sections...]
Adding New Skills
Create a new file in skills/:
# Skill Name
## Overview
[Brief description]
## Key Capabilities
[What this skill provides]
## Best Practices
[Guidelines and patterns]
[Additional sections...]
Example Documentation Outputs
Technical Guide Example
- Setup prerequisites
- Step-by-step instructions
- Code examples with explanations
- Troubleshooting section
- Next steps and references
Financial Analysis Example
- Executive summary
- Cost comparison tables
- ROI calculations
- 3-year projections
- Sensitivity analysis
Case Study Example
- Challenge overview
- Solution approach
- Implementation details
- Quantified results
- Lessons learned
Contributing
When adding new documentation:
- Choose appropriate agent for the task
- Reference relevant skills
- Follow established patterns
- Maintain consistent terminology
- Include code examples where helpful
- Add visualizations for complex data
Best Practices
For Authors
- Start with Documaster for general program plan needs
- Use specialized agents for specific tasks (ROI, tutorials, case studies)
- Reference skills explicitly when you need specific expertise
- Iterate in stages rather than requesting everything at once
- Provide context about your audience and goals
For Quality
- Test all code examples before publishing
- Verify financial calculations with actual data
- Review for consistency in terminology
- Check links and references work correctly
- Get peer review before finalizing
For Maintainability
- Keep agents focused on their core responsibilities
- Update skills as best practices evolve
- Document assumptions in financial models
- Version control major documentation changes
- Track feedback from users for improvements
Getting Help
For Agent Questions
- Review the agent’s
.agent.mdfile for capabilities and boundaries - Check “When to Use This Agent” section for applicability
For Skill Questions
- Consult the skill’s
.skill.mdfile for detailed guidance - Review examples and templates within the skill
For Documentation Standards
- Reference #technical-writing skill for style guidelines
- Reference #ai-terminology skill for consistent terminology
- Review existing documentation for patterns
Success Metrics
Business Impact
- Cost Reduction: 60-80% vendor cost savings
- Productivity Gain: 1.5-2.5x FTE productivity increase
- Adoption Rate: 80%+ team adoption within 6 months
- ROI Achievement: 200%+ ROI in first year
- Payback Period: Less than 6 months
Program Execution
- Phase Gate Success: Pass all Go/No-Go criteria
- Milestone Completion: 90%+ on-time delivery rate
- Risk Mitigation: All identified risks addressed
- Stakeholder Satisfaction: 85%+ satisfaction score
Local AI Deployment
- Data Sovereignty: 100% local processing, zero external data transfer
- Availability: 99.5%+ uptime for self-hosted infrastructure
- Cost vs. Cloud: 60-80% lower TCO over 3 years
- License Compliance: 100% of models properly licensed
- MTTR: <30 minutes for critical issues
Roadmap
Q1 2026
- [ ] Add 3+ real-world case studies with metrics
- [ ] Create interactive ROI calculator tool
- [ ] Enhance rollback and contingency planning
- [ ] Expand competitive landscape analysis
- [ ] Add 30-60-90 day roadmap document
Q2 2026
- [ ] Community contributions and case studies
- [ ] Industry-specific adaptations (fintech, healthcare, etc.)
- [ ] Advanced automation tooling
- [ ] Integration with common enterprise systems
- [ ] Certification program for FTE+AI practitioners
License
MIT License. See LICENSE file for details.
Version: 4.1.0 (Production-Ready Agent Framework) Last Updated: December 31, 2025 Maintained by: FTE+AI Project Team
Summary of v4.1.0 Updates
ENHANCEMENT: Production-Ready Agent Framework with Modern Patterns
- All 18 agents upgraded to v2.0.0 with enhanced YAML frontmatter
- All 23 skills upgraded to v2.0.0 with standardized metadata
- Modern Orchestration Patterns: Orchestrator-Workers, Hub-and-Spoke, Evaluator-Optimizer, Pipeline, Specialist
- Enhanced Agent Metadata: Version tracking, categories, updated dates
- Agent Interaction Models: Receives From / Provides To tables for clear coordination
- Memory and Context Sections: Explicit context management for each agent
- Guardrails: Quality Gates, Escalation Triggers, Hard Boundaries for all agents
- Handoff Protocols: Standardized agent-to-agent communication
- Skill Composability: Prerequisite skills and composable_with relationships
Agent Enhancements in v4.1.0
All agents now include:
- Orchestration Pattern diagrams
- Agent Interaction Models (inputs/outputs)
- Memory and Context management
- Guardrails (Quality Gates, Escalation Triggers, Hard Boundaries)
- Handoff Protocols for agent coordination
Skill Enhancements in v4.1.0
All skills now include:
- YAML frontmatter with version, category, complexity
- Prerequisite skills for learning paths
- Composable_with relationships for skill composition
Summary of v4.0.0 Updates
MAJOR RELEASE: Local AI Infrastructure & Complete Data Sovereignty
- 18 agents (up from 13): Added 5 new agents for local AI deployment
- 23 skills (up from 17): Added 6 new skills for self-hosted infrastructure
- Local AI Focus: Complete guidance for vLLM/SGLang serving and llama.cpp endpoints
- Data Sovereignty: GDPR, HIPAA, CCPA compliance frameworks
- Hardware Sizing: GPU selection (RTX 4090 to H100) and server configurations
- Open Source Models: Model evaluation, benchmarking, and license compliance
- MLOps Operations: Production deployment, monitoring, and incident response
- Vendor Negotiations: Contract negotiation and relationship management
New Agents in v4.0.0
- @Local-AI-Infrastructure-Architect - Hardware and platform design
- @Open-Source-Model-Evaluator - Model selection and licensing
- @Data-Sovereignty-Advisor - Compliance and data protection
- @Vendor-Relationship-Manager - Contract negotiation
- @MLOps-Engineer - Production operations
New Skills in v4.0.0
- #local-ai-deployment - Self-hosted LLM platforms
- #hardware-sizing - GPU and server specification
- #open-source-licensing - Model license compliance
- #data-sovereignty - Data residency and privacy
- #mlops-operations - Production AI operations
- #vendor-negotiation - Contract and relationship management
Previous Release: v3.0.0
Program Execution Framework
- Complete 30-60-90 day vendor replacement program execution
- 13 agents organized by program phase
- 17 skills for program execution and vendor transition
- Phase gates, milestone tracking, and stakeholder engagement
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