J4C Enhanced Agent Framework

Overview

J4C (Jeeves for Coders) Enhanced Agent Framework is a comprehensive AI agent enhancement platform that provides cognitive tools for structured thinking, team-wide learning, and intelligent decision-making.

Mental Models Engine

The J4C framework provides 22 mental models organized into 7 categories, each designed to enhance AI agent decision-making for specific problem types.

Model Categories

Model Details

Analytical Models

• First Principles: Break down complex problems into fundamental truths and build up from there
• Systems Thinking: Understand interconnections, dependencies, and feedback loops
• Pattern Recognition: Identify recurring patterns across different domains
• Analogical Reasoning: Draw insights from similar situations in other contexts
• Circle of Competence: Know and operate within your areas of expertise

Creative Models

• Lateral Thinking: Generate creative, non-obvious solutions by thinking sideways
• Inversion: Think backwards - start from the problem to find solutions

Decision Models

• Cost-Benefit Analysis: Systematically weigh pros and cons
• Probabilistic Thinking: Reason with uncertainty and probabilities
• Pareto Principle: Focus on the vital 20% that produces 80% of results
• Occam's Razor: Prefer simpler explanations over complex ones
• Cynefin Framework: Match response strategy to problem complexity domain

Risk Models

• Risk Assessment: Evaluate potential risks and their impacts
• Second-Order Thinking: Consider downstream effects and consequences
• Feedback Loops: Identify and understand reinforcing/balancing cycles
• Constraints Analysis: Identify and manage bottlenecks
• Margin of Safety: Build in buffers for uncertainty

Chain of Thought Reasoning

J4C implements 7 sophisticated reasoning strategies for step-by-step explainable decision-making.

Reasoning Strategies

Reasoning Step Types

1. Observation - Initial facts and evidence gathering
2. Hypothesis - Proposed explanation or solution
3. Deduction - Logical inference from premises
4. Induction - Generalization from specific cases
5. Abduction - Best explanation inference
6. Analysis - Breaking down complex elements
7. Synthesis - Combining elements into whole
8. Evaluation - Assessing quality and validity
9. Decision - Making choices based on analysis
10. Verification - Confirming results
11. Backtrack - Revising previous steps

Team Learning System

The team learning system enables AI agents to share experiences and learn from each other across the entire team.

Key Features

• Problem-Solution Storage: Successful solutions are stored with full context
• Pattern Recognition: Common patterns across team are identified and shared
• Model Effectiveness Tracking: Track which models work best for which problems
• Similar Solution Search: Find relevant past solutions for new problems
• Recommendations Engine: Get AI-powered model recommendations

Learning Data Structure

{
  "problemType": "performance",
  "problemDescription": "N+1 query pattern causing slow API",
  "domain": "database",
  "context": {
    "framework": "TypeORM",
    "database": "PostgreSQL"
  },
  "solution": "Use eager loading with .leftJoinAndSelect()",
  "modelsUsed": ["first_principles", "systems_thinking"],
  "effectiveness": 0.95,
  "tags": ["n+1", "performance", "typeorm"]
}

Team Insights

Team insights aggregate learning across all agents:

• Model usage statistics by domain
• Success rates by model and problem type
• Recent learnings summary
• Cross-team pattern analysis

Confidence Calibration

J4C implements sophisticated confidence calibration to ensure AI agents accurately estimate their certainty.

Features

• Bayesian Updates: Confidence scores are updated based on new evidence
• Expected Calibration Error (ECE): Measures how well confidence matches accuracy
• Multi-Model Aggregation: Combines confidence from multiple models
• Historical Tracking: Tracks accuracy over time for continuous improvement

Confidence Levels

Analytics & Reporting

Comprehensive analytics and reporting for monitoring system health and usage patterns.

Real-Time Analytics

• Request counts and rates
• Hourly usage breakdown
• Top endpoints
• Error tracking and rates
• Response time monitoring

Available Reports

Prometheus Metrics

# Available metrics at /api/v3/metrics
j4c_requests_total          - Total API requests
j4c_uptime_seconds          - Server uptime
j4c_memory_heap_bytes       - Memory usage
j4c_agent_sessions_total    - Active agent sessions
j4c_model_effectiveness     - Model effectiveness scores

GNN Recommendation Engine

J4C v3.2 introduces a Graph Neural Network-inspired recommendation system that models relationships between mental models, problem domains, and historical success patterns.

Architecture

• Knowledge Graph: Models relationships between 22 mental models with synergy weights
• Message Passing: 3 layers propagate relevance scores through the graph
• Attention Mechanism: 4 heads weight model importance based on context
• Domain Embeddings: Dense vector representations for domains and models

GNN Endpoints

Reasoning Chain Types

Example: Get Recommendations

curl "http://j4c.aurigraph.io/api/v3/agent/recommend?domain=debugging&problemType=bug"

# Response
{
  "engine": "GNN",
  "domain": "debugging",
  "recommendations": [
    {
      "model": "first_principles",
      "score": 0.95,
      "category": "analytical",
      "synergies": [
        {"model": "systems_thinking", "strength": 0.9},
        {"model": "inversion", "strength": 0.85}
      ],
      "explanation": "High effectiveness for debugging (95%)"
    }
  ]
}

SPARC Project Management

SPARC is a structured project methodology built into J4C that breaks projects into 5 distinct phases.

The SPARC Phases

Views

SPARC Endpoints

GET  /api/v3/sparc/dashboard   - Dashboard statistics
GET  /api/v3/sparc/projects    - List all projects
POST /api/v3/sparc/projects    - Create new project
GET  /api/v3/sparc/projects/:id - Get project details
PUT  /api/v3/sparc/projects/:id - Update project

Sprint Planning

Agile sprint management with burndown charts, velocity tracking, and backlog management.

Sprint Features

• Kanban Board: To Do → In Progress → In Review → Done
• Burndown Charts: Track remaining work vs. ideal burndown
• Velocity Tracking: Measure team velocity across sprints
• Backlog Management: Prioritize and groom product backlog
• Sprint History: Review past sprint performance

Sprint Views

Sprint Endpoints

GET  /api/v3/sprints                 - List sprints
GET  /api/v3/sprints/current         - Current sprint details
POST /api/v3/sprints                  - Create new sprint
GET  /api/v3/sprints/backlog/items   - Backlog items
GET  /api/v3/sprints/velocity/history - Velocity history

Story Points

J4C uses Fibonacci-style story points for effort estimation:

Authentication & RBAC

J4C implements robust authentication and role-based access control.

Authentication Methods

• Google OAuth2: Sign in with Google account
• API Keys: For automated agents (X-API-Key header)
• Bearer Tokens: JWT tokens for session management

Roles and Permissions

API Reference

Quick reference for J4C API endpoints.

Public Endpoints

Agent Endpoints (Requires API Key)

Example Request

curl -X POST http://j4c.aurigraph.io/api/v3/agent/session \
  -H "X-API-Key: j4c_YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "agentId": "claude-code-v1",
    "context": {
      "project": "my-app",
      "task": "debugging"
    }
  }'