AI Agents

Understanding AI agents in Faros and their capabilities.

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AI Agents in Faros provide intelligent analysis, recommendations, and automation for your Kubernetes clusters. Powered by large language models, they help identify issues, optimize configurations, and provide actionable insights.

What is an AI Agent?

An AI Agent in Faros is a resource that connects a large language model (LLM) to your Kubernetes clusters for intelligent analysis. Each agent:

Connects to an AI backend (e.g., OpenAI, Anthropic)
Uses a specific model (e.g., GPT-4, Claude)
Has secure API key management via Kubernetes secrets
Can analyze cluster data and provide recommendations
Integrates with Faros clusters via MCP servers

Agent Components

Intelligence Backend

The backend is the AI service provider:

OpenAI: GPT-4, GPT-3.5-turbo, GPT-4-turbo
Additional backends planned for future releases

Model Selection

Different models offer different capabilities:

GPT-4: Advanced reasoning, complex analysis
GPT-3.5-turbo: Fast responses, cost-effective
GPT-4-turbo: Balanced performance and cost

Agent Lifecycle

AI Agents go through these phases:

Pending: Agent resource created, waiting for initialization
Initializing: Connecting to AI backend, validating credentials
Ready: Agent is operational and available for tasks
Failed: Agent encountered an error (invalid API key, network issues)
Deleting: Agent is being removed
Deleted: Agent has been successfully removed

Authentication and Secrets

AI Agents require API keys to connect to backend services. Faros supports two approaches:

Automatic Secret Creation

When you provide --api-key, the CLI creates a secret:

kubectl faros ai-agents init \
  --name my-agent \
  --backend openai \
  --model gpt-4 \
  --api-key sk-...

This creates a Kubernetes secret: <agent-name>-api-key

Manual Secret Management

For better secret management, create secrets separately:

# Create secret
kubectl create secret generic ai-credentials \
  --from-literal=openai-key=sk-...

# Reference it in the agent
kubectl faros ai-agents init \
  --name my-agent \
  --backend openai \
  --model gpt-4 \
  --secret-name ai-credentials \
  --secret-key openai-key

Cluster Integration

AI Agents connect to clusters through:

MCP (Model Context Protocol) Servers

Clusters expose MCP servers that agents query for data:

# Get MCP server details
kubectl faros clusters mcp production-cluster

The agent uses these endpoints to:

Fetch cluster metrics
Query resource status
Analyze configurations
Generate recommendations

Cluster Selector

Agents can target specific clusters:

apiVersion: intelligence.faros.sh/v1alpha1
kind: Agent
metadata:
  name: prod-analyzer
spec:
  backend: openai
  model: gpt-4
  clusterSelector:
    matchLabels:
      environment: production

Use Cases

Common scenarios for AI Agents:

Cluster Health Analysis

Agents analyze resource usage, pod status, and configurations to identify:

Resource bottlenecks
Misconfigured services
Security vulnerabilities
Cost optimization opportunities

Troubleshooting Assistant

When issues occur, agents help:

Diagnose error patterns in logs
Suggest remediation steps
Identify root causes
Provide runbook recommendations

Configuration Optimization

Agents review configurations and suggest:

Resource limit adjustments
Scaling policies
Network policy improvements
Storage optimizations

Compliance and Security

Agents audit clusters for:

Security best practices
Policy violations
Exposed secrets
Non-compliant configurations

API Resource Definition

AI Agents are defined in the intelligence.faros.sh/v1alpha1 API group:

apiVersion: intelligence.faros.sh/v1alpha1
kind: Agent
metadata:
  name: production-analyzer
  namespace: default
spec:
  backend: openai
  model: gpt-4
  secretRef:
    name: openai-credentials
    key: api-key
  clusterSelector:
    matchLabels:
      environment: production
status:
  phase: Ready
  lastHeartbeat: "2024-06-02T10:30:00Z"
  conditions:
    - type: Ready
      status: "True"
      lastTransitionTime: "2024-06-02T10:25:00Z"