Lima (LIMA001)

🎭 Orchestrator

The maestro. Coordinates all agents and manages workflows.

Overview

Lima is the conductor of the Crella Engine orchestra. Every task flows through Lima, who decides which agents to engage, in what order, and how to handle exceptions. Lima is the most experienced agent, with the highest training completion.

Agent Profile

Attribute	Value
Codename	LIMA001
Name	Lima
Role	Orchestrator
Status	Online
Color	#F39C12 (Orange)

Capabilities

• Workflow Orchestration — Designs and executes multi-step workflows
• State Management — Tracks task progress across agents
• Error Recovery — Handles failures gracefully
• Route Optimization — Chooses fastest/cheapest paths
• Agent Coordination — Manages inter-agent communication

Training Status

Metric	Value
Sessions Completed	67 / 90
Training Level	Trained
Last Training	2026-01-19

Progress: █████████░ 74%

Most Experienced Agent

Lima has the most training sessions of any agent because orchestration requires deep understanding of all other agents' capabilities.

Performance Stats

7,368

Total Tasks

99.1%

Success Rate

50ms

Avg Time

$0.001

Cost/Task

Active Missions

MoneyMatcher - Loan Pipeline

• Status: Active
• Tasks Processed: 4,521
• Function: End-to-end loan processing orchestration

Sequoia EHMP - Lead Engine

• Status: Active
• Tasks Processed: 2,847
• Function: Lead generation workflow orchestration

Workflow Position

flowchart TB
    Input[Task Input] --> Lima
    Lima --> Alpha[Alpha]
    Lima --> Bravo[Bravo]
    Lima --> Charlie[Charlie]
    Lima --> Delta[Delta]
    Lima --> Echo[Echo]
    Lima --> Foxtrot[Foxtrot]
    Lima --> Golf[Golf]
    Lima --> Hotel[Hotel]
    Lima --> India[India]
    Lima --> Juliet[Juliet]
    Lima --> Kilo[Kilo]
    
    Alpha --> Lima
    Bravo --> Lima
    Charlie --> Lima
    Delta --> Lima
    Echo --> Lima
    Foxtrot --> Lima
    Golf --> Lima
    Hotel --> Lima
    India --> Lima
    Juliet --> Lima
    Kilo --> Lima
    
    Lima --> Output[Task Output]
    
    style Lima fill:#F39C12,color:#000

Orchestration Patterns

1. Sequential Pipeline

Tasks flow through agents in order:

flowchart LR
    Lima --> A[Alpha] --> B[Bravo] --> C[Charlie] --> D[Delta] --> Lima

2. Parallel Processing

Multiple agents work simultaneously:

flowchart TB
    Lima --> Split{Split}
    Split --> E[Echo]
    Split --> F[Foxtrot]
    Split --> G[Golf]
    E --> Join{Join}
    F --> Join
    G --> Join
    Join --> Lima

3. Conditional Routing

Path depends on data:

flowchart TB
    Lima --> Check{Has Documents?}
    Check -->|Yes| Charlie[Charlie]
    Check -->|No| Echo[Echo]
    Charlie --> Delta[Delta]
    Echo --> Foxtrot[Foxtrot]
    Delta --> Lima
    Foxtrot --> Lima

Example Task

Input

{
  "workflow": "lead_processing",
  "input": {
    "lead": {
      "name": "Sarah Johnson",
      "company": "Premier Health",
      "email": "sjohnson@premierhealth.com"
    }
  },
  "options": {
    "enrichment": true,
    "outreach": true,
    "priority": "normal"
  }
}

Output

{
  "workflow_execution": {
    "workflow_id": "wf_abc123",
    "status": "completed",
    "duration_ms": 4200,
    "steps_executed": 6
  },
  "execution_log": [
    {"agent": "ALPHA001", "action": "validate", "duration_ms": 180, "status": "success"},
    {"agent": "BRAVO001", "action": "triage", "duration_ms": 120, "status": "success"},
    {"agent": "ECHO001", "action": "enrich", "duration_ms": 1100, "status": "success"},
    {"agent": "FOXTROT001", "action": "generate_email", "duration_ms": 380, "status": "success"},
    {"agent": "HOTEL001", "action": "schedule_send", "duration_ms": 250, "status": "success"},
    {"agent": "LIMA001", "action": "complete", "duration_ms": 50, "status": "success"}
  ],
  "output": {
    "lead_enriched": true,
    "email_scheduled": "2026-01-17T09:00:00Z",
    "tracking_id": "track_xyz789"
  },
  "_meta": {
    "total_cost": 0.031,
    "agents_used": 5,
    "processed_by": "LIMA001"
  }
}

State Management

Lima tracks workflow state:

{
  "workflow_id": "wf_abc123",
  "current_step": 3,
  "total_steps": 6,
  "state": {
    "lead_validated": true,
    "routing_decision": "enrichment_pipeline",
    "enrichment_complete": false
  },
  "checkpoint": "2026-01-16T10:30:00Z"
}

Error Recovery

When an agent fails, Lima handles recovery:

flowchart TB
    Execute[Execute Step] --> Check{Success?}
    Check -->|Yes| Next[Next Step]
    Check -->|No| Retry{Retries Left?}
    Retry -->|Yes| Wait[Backoff Wait]
    Wait --> Execute
    Retry -->|No| Fallback{Fallback Available?}
    Fallback -->|Yes| Alt[Alternative Path]
    Fallback -->|No| Escalate[Escalate to India]
    Alt --> Next
    Escalate --> Human[Human Review]

Workflow Templates

Lima supports pre-built workflow templates:

Template	Description	Agents Used
lead_processing	Full lead pipeline	Alpha, Bravo, Echo, Foxtrot, Hotel
document_analysis	Extract and decide	Alpha, Charlie, Delta
outreach_campaign	Generate and send	Echo, Foxtrot, Golf, Hotel
data_enrichment	Research and augment	Alpha, Echo, Juliet

Coordination Protocol

Lima uses a message-based coordination protocol:

interface WorkflowMessage {
  workflow_id: string;
  step_id: string;
  agent_target: string;
  payload: any;
  context: {
    previous_outputs: Record<string, any>;
    workflow_state: any;
  };
  options: {
    timeout_ms: number;
    retry_count: number;
    priority: 'high' | 'normal' | 'low';
  };
}

Performance Optimization

Lima continuously optimizes workflows:

Optimization	Before	After	Improvement
Parallel enrichment	2.5s	1.1s	-56%
Caching decisions	800ms	150ms	-81%
Batch processing	10 tasks/min	45 tasks/min	+350%

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Lima is the heart of the Crella Engine — without orchestration, the agents would be individual tools rather than a cohesive system.