Kilo (KILO001)

🧠 Learning Agent

The optimizer. Analyzes outcomes and suggests improvements.

Overview

Kilo is the continuous improvement engine. By analyzing outcomes across all agents and missions, Kilo identifies patterns, suggests optimizations, and helps the entire system get smarter over time.

Agent Profile

Attribute	Value
Codename	KILO001
Name	Kilo
Role	Learning Agent
Status	Online
Color	#1ABC9C (Emerald)

Capabilities

• Outcome Analysis — Correlates inputs with results
• Pattern Recognition — Identifies success/failure patterns
• Model Retraining — Suggests prompt/model improvements
• A/B Analysis — Evaluates experiment results
• Performance Optimization — Recommends efficiency gains

Training Status

Metric	Value
Sessions Completed	15 / 90
Training Level	Learning
Last Training	2026-01-19

Progress: ████████░░ 17%

Performance Stats

23

Total Tasks

95.6%

Success Rate

2s

Avg Time

$0.02

Cost/Task

Active Missions

Platform Optimization

• Status: Active
• Tasks Processed: 23
• Function: System-wide performance optimization

Workflow Position

flowchart LR
    Juliet[Juliet] --> Kilo
    Kilo --> |Insights| Lima[Lima - Orchestrator]
    Kilo --> |Recommendations| Agents[All Agents]
    Kilo --> |Alerts| Human[Human Review]
    
    style Kilo fill:#1ABC9C,color:#000

Learning Loop

flowchart TB
    Data[Collect Outcome Data] --> Analyze[Analyze Patterns]
    Analyze --> Identify[Identify Improvements]
    Identify --> Recommend[Generate Recommendations]
    Recommend --> Test[A/B Test Changes]
    Test --> Measure[Measure Impact]
    Measure --> Deploy{Improvement?}
    Deploy -->|Yes| Implement[Deploy to Production]
    Deploy -->|No| Iterate[Iterate]
    Implement --> Data
    Iterate --> Analyze

Optimization Areas

1. Prompt Engineering

Kilo analyzes agent prompts and suggests improvements:

Metric	Current	After Optimization
Success Rate	92%	96%
Processing Time	800ms	650ms
Token Usage	1,200	950

2. Routing Efficiency

Optimizing Bravo's routing decisions:

Pattern	Observation	Recommendation
Document → Delta	15% rerouted	Route via Charlie first
Research → Foxtrot	Low engagement	Add enrichment step
Complex → Human	30% could be automated	Train Delta on edge cases

3. Content Optimization

Improving Foxtrot's email performance:

Variable	Test A	Test B	Winner
Subject Line	Question format	Statement format	Question (+12%)
Email Length	150 words	100 words	100 words (+8%)
CTA Position	End	Middle	Middle (+5%)

Example Task

Input

{
  "analysis_type": "outcome_correlation",
  "agent": "FOXTROT001",
  "time_range": "last_30_days",
  "success_metric": "response_rate",
  "variables": ["subject_line_type", "email_length", "personalization_score"]
}

Output

{
  "analysis": {
    "sample_size": 1823,
    "success_metric": "response_rate",
    "baseline": 2.8
  },
  "findings": [
    {
      "variable": "personalization_score",
      "correlation": 0.72,
      "insight": "Higher personalization strongly correlates with responses",
      "recommendation": "Increase personalization threshold from 70 to 80"
    },
    {
      "variable": "subject_line_type",
      "correlation": 0.45,
      "insight": "Question-based subjects outperform statements",
      "recommendation": "Default to question format for cold outreach"
    },
    {
      "variable": "email_length",
      "correlation": -0.31,
      "insight": "Shorter emails perform better",
      "recommendation": "Target 80-120 words for cold emails"
    }
  ],
  "projected_improvement": {
    "response_rate": "+1.2%",
    "confidence": 0.85
  },
  "_meta": {
    "analysis_depth": "detailed",
    "processing_time_ms": 1850,
    "processed_by": "KILO001"
  }
}

Insight Categories

Category	Description	Action
Performance	Agent speed/accuracy	Prompt tuning
Efficiency	Cost optimization	Route optimization
Quality	Output improvements	Template updates
Pattern	Recurring issues	Process changes
Anomaly	Unusual behavior	Investigation

Machine Learning Integration

Kilo leverages ML for advanced analysis:

flowchart LR
    subgraph data [Data Collection]
        Logs[Agent Logs]
        Outcomes[Task Outcomes]
        Feedback[User Feedback]
    end
    
    subgraph ml [ML Pipeline]
        Features[Feature Extraction]
        Train[Model Training]
        Predict[Prediction]
    end
    
    subgraph action [Action]
        Recommend[Recommendations]
        Alert[Anomaly Alerts]
    end
    
    data --> ml --> action

Continuous Improvement Metrics

Metric	Baseline	Current	Improvement
Overall Success Rate	89%	96%	+7%
Avg Processing Time	500ms	380ms	-24%
Cost per Task	$0.012	$0.009	-25%
Human Escalation Rate	15%	8%	-47%

Next Agent

Kilo's insights feed back to Lima (Orchestrator) for system-wide optimization.