Documentation Index Fetch the complete documentation index at: https://docs.cloudshipai.com/llms.txt
Use this file to discover all available pages before exploring further.
Why Multi-Agent Teams?
Single Agent Limitations:
Complex prompts become unwieldy (2000+ lines)
Hard to test and maintain
Generic responses for diverse tasks
Multi-Agent Benefits:
Specialization : Each agent focuses on one domain
Coordination : Coordinator delegates to specialists
Maintainability : Small, focused prompts
Testability : Test specialists independently
Team Structure
Incident Coordinator (Orchestrator)
├── Kubernetes Expert
├── Log Analyzer
├── Metrics Analyzer
├── Database Troubleshooter
└── Remediation Executor
How It Works
1. User Reports Issue
"API latency spiked from 200ms to 5s starting 10 minutes ago."
2. Coordinator Analyzes & Delegates
Coordinator :
- Analyzes : Performance degradation
- Delegates to : Metrics, Logs, K8s specialists
3. Specialists Execute
Metrics Analyzer :
- Queries Prometheus
- Finds : DB query time increased 10x
Log Analyzer :
- Searches application logs
- Finds : "connection timeout" errors
Kubernetes Expert :
- Checks pod status
- Finds : App pods restarting
4. Coordinator Synthesizes
ROOT CAUSE: Database connection pool exhausted
RECOMMENDATION: Scale DB connection pool
Creating Teams
The easiest way to create multi-agent teams:
"Create an incident response team with a coordinator that delegates to
kubernetes, logs, metrics, and database specialists"
Station uses these MCP tools:
Tool Purpose create_agentCreate coordinator or specialist agents add_agent_as_toolLink specialist to coordinator remove_agent_as_toolUnlink specialist from coordinator
Example conversation:
You: Create a kubernetes-expert agent
Station: ✅ Created kubernetes-expert (ID: 12)
You: Create an incident-coordinator that uses kubernetes-expert
Station: ✅ Created incident-coordinator (ID: 13)
✅ Added kubernetes-expert as tool __agent_kubernetes_expert
Via .prompt File
The coordinator agent calls specialist agents as tools:
---
metadata :
name : "Incident Coordinator"
description : "Orchestrates incident response"
model : gpt-5-mini
max_steps : 15
agents :
- "kubernetes_expert"
- "log_analyzer"
- "metrics_analyzer"
- "database_troubleshooter"
---
{{ role "system" }}
You are an Incident Response Coordinator.
When investigating incidents :
1. Gather initial context from the report
2. Delegate to appropriate specialists
3. Synthesize findings into root cause
4. Recommend remediation steps
Available specialists :
- kubernetes_expert : Pod, deployment, service issues
- log_analyzer : Application and system logs
- metrics_analyzer : Prometheus/Grafana metrics
- database_troubleshooter : DB performance issues
{{ role "user" }}
{{ userInput }}
Creating Specialists
Specialists are regular agents focused on one domain:
---
metadata :
name : "Kubernetes Expert"
description : "Diagnoses Kubernetes issues"
model : gpt-5-mini
max_steps : 8
tools :
- "__kubectl_get_pods"
- "__kubectl_describe"
- "__kubectl_logs"
---
{{ role "system" }}
You are a Kubernetes expert. Diagnose pod, deployment, and service issues.
Focus on :
- Pod status and restarts
- Resource constraints
- Network policies
- Recent deployments
{{ role "user" }}
{{ userInput }}
When an agent is used as a tool, its name becomes:
__agent_{agent_name_snake_case}
Example: “Kubernetes Expert” → __agent_kubernetes_expert
Best Practices
Each specialist should do one thing well. 5-8 tools max per specialist.
Clear Handoff Instructions
Coordinator prompts should clearly explain when to use each specialist.
Test each specialist before testing the full team.
Avoid coordinators calling other coordinators. Keep hierarchy flat.
Next Steps
Workflows Orchestrate agents in durable pipelines with parallel execution
Evaluation Test and score your agent teams
Bundles Package teams for distribution