90-Session Training System

Every agent in the Crella Engine undergoes a rigorous 90-session training program before reaching full operational status.

Training Philosophy

We believe that AI agents, like human employees, require structured training to perform consistently. Our 90-session program ensures:

1. Consistency — Predictable behavior across edge cases
2. Quality — High accuracy on domain-specific tasks
3. Efficiency — Optimized processing times
4. Safety — Proper handling of sensitive data

Training Levels

flowchart LR
    subgraph levels [Training Progression]
        L1[Learning<br/>0-30 sessions]
        L2[Trained<br/>31-60 sessions]
        L3[Expert<br/>61-90 sessions]
    end
    
    L1 --> L2 --> L3

Level 1: Learning (0-30 sessions)

• Basic task execution
• Error pattern identification
• Human supervision required
• Limited autonomous authority

Level 2: Trained (31-60 sessions)

• Consistent task completion
• Reduced error rates
• Partial autonomous operation
• Complex task handling

Level 3: Expert (61-90 sessions)

• Full autonomous operation
• Edge case handling
• Training other agents
• Mission-critical tasks

Current Training Status

Agent	Sessions	Status	Progress
Alpha	8/90	Learning	████░░░░░░ 9%
Bravo	5/90	Learning	██░░░░░░░░ 6%
Charlie	6/90	Learning	███░░░░░░░ 7%
Delta	8/90	Learning	████░░░░░░ 9%
Echo	9/90	Learning	████░░░░░░ 10%
Foxtrot	10/90	Learning	█████░░░░░ 11%
Golf	11/90	Learning	█████░░░░░ 12%
Hotel	12/90	Learning	██████░░░░ 13%
India	13/90	Learning	██████░░░░ 14%
Juliet	14/90	Learning	███████░░░ 16%
Kilo	15/90	Learning	████████░░ 17%
Lima	67/90	Trained	█████████░ 74%

Lima's Experience

Lima (the Orchestrator) has the most training sessions because coordinating workflows requires deep understanding of all other agents. Lima is approaching Expert status.

Training Session Structure

Each training session follows a structured format:

flowchart TB
    subgraph session [Training Session]
        Brief[Briefing<br/>5 min]
        Task[Task Execution<br/>45 min]
        Review[Human Review<br/>5 min]
        Feedback[Feedback Integration<br/>5 min]
    end
    
    Brief --> Task --> Review --> Feedback

1. Briefing (5 minutes)

• Task objectives review
• New capability introduction
• Edge case scenarios

2. Task Execution (45 minutes)

• Real-world task processing
• Performance monitoring
• Error logging

3. Human Review (5 minutes)

• Output quality assessment
• Error analysis
• Success/failure determination

4. Feedback Integration (5 minutes)

• Model weight adjustment
• Prompt refinement
• Knowledge base update

Training Metrics

Each session tracks:

Metric	Target	Description
Task Accuracy	>90%	Correct output percentage
Processing Time	<spec	Under time threshold
Confidence Score	>70%	AI certainty level
Error Rate	<5%	Failed task percentage
Edge Case Handling	>80%	Complex scenario success

Graduation Requirements

To advance from one level to the next:

Learning → Trained (30 sessions)

• 90%+ accuracy on standard tasks
• <3% error rate
• Handle 5+ task types
• Pass 3 consecutive reviews

Trained → Expert (60 sessions)

• 95%+ accuracy on complex tasks
• <1% error rate
• Handle all task types
• Pass 10 consecutive reviews
• Demonstrate edge case mastery

Mission-Specific Training

Beyond general training, agents receive mission-specific sessions:

MoneyMatcher Mission

• DSCR calculation validation
• Loan document terminology
• Lender guideline matching
• Property valuation patterns

Sequoia EHMP Mission

• Healthcare terminology
• Broker qualification criteria
• Email personalization rules
• LinkedIn research patterns

Training Dashboard

Track training progress for all agents:

╔════════════════════════════════════════════════════════════════╗
║                    CRELLA ENGINE TRAINING                       ║
╠════════════════════════════════════════════════════════════════╣
║  Agent    │ Level    │ Sessions │ Next Session │ Accuracy      ║
╠═══════════╪══════════╪══════════╪══════════════╪═══════════════╣
║  Alpha    │ Learning │   8/90   │ Scheduled    │ 98.2%         ║
║  Bravo    │ Learning │   5/90   │ Scheduled    │ 97.5%         ║
║  Charlie  │ Learning │   6/90   │ Scheduled    │ 95.1%         ║
║  Delta    │ Learning │   8/90   │ Scheduled    │ 92.3%         ║
║  Echo     │ Learning │   9/90   │ Scheduled    │ 88.4%         ║
║  Foxtrot  │ Learning │  10/90   │ Scheduled    │ 96.1%         ║
║  Golf     │ Learning │  11/90   │ On Hold      │ 90.2%         ║
║  Hotel    │ Learning │  12/90   │ Scheduled    │ 94.3%         ║
║  India    │ Learning │  13/90   │ Scheduled    │ 97.1%         ║
║  Juliet   │ Learning │  14/90   │ Scheduled    │ 99.2%         ║
║  Kilo     │ Learning │  15/90   │ Scheduled    │ 95.6%         ║
║  Lima     │ Trained  │  67/90   │ Scheduled    │ 99.1%         ║
╚════════════════════════════════════════════════════════════════╝

Next Steps

• Meet the Agents — Individual agent profiles
• Missions — Active deployments