Tier 2: Under Evaluation

Patterns and frameworks currently undergoing critical analysis before promotion to proven practice.

Evaluation Framework

All patterns in this tier are evaluated using:

Risk Assessment Matrix

  • Technical Risk: Implementation complexity and failure modes
  • Security Risk: Data exposure and vulnerability potential
  • Operational Risk: Maintenance burden and scalability concerns
  • Business Risk: Cost implications and vendor lock-in

Client Context Analysis

  • Conservative Profile: Risk-averse enterprises and regulated industries
  • Moderate Profile: Growth-stage companies balancing innovation and stability
  • Aggressive Profile: Startups and innovation labs prioritizing speed

Implementation Feasibility

  • Resource Requirements: Team skills, time, and infrastructure needed
  • Integration Complexity: Compatibility with existing systems
  • Migration Path: Effort required to adopt or abandon

Currently Under Evaluation

Recent Analyses

Psychology of Trust in AI Systems

Status: Framework evaluation complete Risk Level: Managed Priority: HIGH - Addresses critical user adoption challenges

Key Framework:

  • Four-pillar trust model (Ability, Benevolence, Integrity, Predictability)
  • Calibrated trust approach (avoiding both under and over-trust)
  • Practical measurement methods for UX teams

ACE-FCA Context Engineering

Status: Technical evaluation complete Risk Level: Moderate Priority: HIGH - Addresses production codebase integration

Key Approach:

  • Frequent Intentional Compaction methodology
  • Research-Plan-Implement workflow
  • Context window optimization for 300k+ LOC codebases

Pattern: Autonomous Agent Orchestration

Status: Testing in controlled environments Risk Level: High Evaluation Period: Q1 2025

Key Questions:

  • How to maintain deterministic behavior?
  • What guardrails prevent runaway processes?
  • How to audit and trace agent decisions?

Initial Findings:

  • Promising for repetitive tasks
  • Requires extensive monitoring
  • Not suitable for critical path operations

Pattern: Context Window Optimization

Status: Gathering performance metrics Risk Level: Managed Evaluation Period: Q4 2024 - Q1 2025

Key Questions:

  • What's the optimal context size for different tasks?
  • How to manage context switching efficiently?
  • When does context size impact quality?

Initial Findings:

  • Significant cost implications
  • Quality plateaus around 50K tokens
  • Chunking strategies show promise

Pattern: Hybrid Human-AI Workflows

Status: Client pilot programs Risk Level: Managed Evaluation Period: Ongoing

Key Questions:

  • Where are the optimal handoff points?
  • How to maintain context across transitions?
  • What approval mechanisms work best?

Initial Findings:

  • Clear ownership boundaries essential
  • Async workflows more successful than sync
  • Review fatigue is real concern

Pattern: Multi-Model Ensembles

Status: Cost-benefit analysis Risk Level: Managed Evaluation Period: Q1 2025

Key Questions:

  • When do ensembles outperform single models?
  • How to manage increased latency?
  • What's the cost multiplication factor?

Initial Findings:

  • Useful for critical decisions
  • 3-5x cost increase typical
  • Consensus mechanisms complex

Evaluation Pipeline

Stage 1: Initial Assessment (2-4 weeks)

  • Literature review and vendor claims
  • Technical feasibility analysis
  • Initial risk assessment

Stage 2: Proof of Concept (4-8 weeks)

  • Controlled environment testing
  • Performance benchmarking
  • Security review

Stage 3: Pilot Program (8-12 weeks)

  • Limited production deployment
  • Real-world metrics collection
  • User feedback gathering

Stage 4: Decision Point

  • Promote: Move to Tier 3 (Proven Practice)
  • Iterate: Return to earlier stage with modifications
  • Reject: Document reasons and archive

Rejected Patterns

Pattern: Fully Autonomous Code Deployment

Rejection Date: December 2024 Reason: Unacceptable risk profile

Key Issues:

  • No reliable rollback mechanisms
  • Insufficient testing coverage
  • Regulatory compliance violations
  • Loss of human oversight

Pattern: Cross-Repository Context Sharing

Rejection Date: November 2024 Reason: Security and privacy concerns

Key Issues:

  • IP leakage between projects
  • GDPR/privacy violations
  • Insufficient access controls
  • Context pollution problems

Upcoming Evaluations

Q1 2025 Pipeline

  1. Semantic Code Search - Using embeddings for code discovery
  2. Automated PR Reviews - AI-driven code review automation
  3. Predictive Resource Scaling - AI-based capacity planning

Q2 2025 Pipeline

  1. Voice-Driven Development - Natural language programming
  2. AI Pair Programming - Real-time collaborative coding
  3. Automated Documentation Generation - Context-aware docs

Contributing to Evaluations

Submission Criteria

Patterns submitted for evaluation must:

  • Address a specific, documented problem
  • Have at least one reference implementation
  • Include risk assessment documentation
  • Provide measurable success criteria

Evaluation Participation

Teams can participate by:

  • Joining pilot programs
  • Providing usage metrics
  • Submitting feedback reports
  • Sharing implementation experiences

Metrics and Success Criteria

Quantitative Metrics

  • Productivity Impact: Time saved, velocity improvement
  • Quality Metrics: Bug reduction, test coverage
  • Cost Analysis: ROI calculation, TCO assessment
  • Performance Data: Latency, throughput, reliability

Qualitative Assessments

  • Developer Satisfaction: Survey scores, adoption rates
  • Maintainability: Code review feedback, technical debt
  • Team Dynamics: Collaboration improvement, knowledge sharing
  • Risk Mitigation: Incident reduction, compliance adherence

Contact and Resources

Evaluation Committee

For questions about the evaluation process or to submit patterns:

Additional Resources