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MattMagg

agent-deployment

by MattMagg

Claude Code plugins for building AI agents across frameworks (Google ADK, OpenAI, and more)

2🍴 0📅 Jan 18, 2026

SKILL.md


name: Agent Deployment description: Production deployment workflow for agentic systems. Directs to RAG for implementation.

Agent Deployment Workflow

Deployment Decision Framework

FrameworkPrimary OptionAlternativeRAG Query
ADKAgent Engine (Vertex AI)Cloud Run, GKE"ADK deployment agent engine"
OpenAIAny Python hostingServerless, Docker"openai agents deployment"
LangChainLangServe, Cloud RunDocker, K8s"langchain langserve deployment"
LangGraphLangGraph PlatformCloud Run"langgraph platform deployment"
CrewAICrewAI EnterpriseDocker"crewai deployment production"
AnthropicAny Python hostingDocker, Serverless"anthropic agent deployment"

Pre-Deployment Checklist

Code Readiness

  • All tests passing
  • Error handling complete
  • Logging configured
  • Input validation in place
  • Output guardrails active

Configuration

  • Environment variables documented
  • Secrets in secret manager (NOT in .env or code)
  • Rate limiting configured
  • Token limits set
  • Timeout values appropriate

Security

  • API key rotation plan
  • Audit logging enabled
  • PII handling documented
  • Input sanitization active
  • Output filtering configured

Monitoring

  • Health check endpoint
  • Metrics collection
  • Alerting rules defined
  • Log aggregation setup

Deployment Workflow

Step 1: Environment Configuration

RAG Query: mcp__agentic-rag__search("[framework] environment configuration", mode="explain")

Production differs from development:

  • LOG_LEVEL: INFO (not DEBUG)
  • TRACE_ENABLED: false (or sampling)
  • Secrets: Use secret manager, not .env

Step 2: Containerization (if applicable)

RAG Query: mcp__agentic-rag__search("[framework] dockerfile", mode="build")

Step 3: Platform Deployment

RAG Query: mcp__agentic-rag__search("[framework] [platform] deployment", mode="explain")

Step 4: Monitoring Setup

RAG Query: mcp__agentic-rag__search("[framework] monitoring observability", mode="explain")

Key Production Metrics

MetricAlert ThresholdWhy It Matters
Latency p95> 5sUser experience
Error rate> 1%Reliability
Token usageSpike > 200%Cost control
Tool failures> 5%Agent effectiveness
Routing accuracy< 90%Multi-agent health

Security Considerations

Input Validation

  • Sanitize user input before passing to agent
  • Limit input length
  • Filter known attack patterns

RAG Query: mcp__agentic-rag__search("agent input validation security", mode="explain")

Output Guardrails

  • Filter sensitive information
  • Prevent prompt leakage
  • Validate tool outputs

RAG Query: mcp__agentic-rag__search("agent guardrails output filtering", mode="explain")

Secret Management

  • Never hardcode API keys
  • Use platform secret managers (GCP Secret Manager, AWS Secrets Manager, etc.)
  • Rotate keys regularly

RAG Query: mcp__agentic-rag__search("[framework] secret management", mode="explain")

Scaling Considerations

ConcernSolutionRAG Query
Cold startsKeep warm instances"[framework] cold start"
Concurrent requestsQueue + workers"[framework] scaling"
Token limitsRequest batching"[framework] rate limiting"
State persistenceExternal store"[framework] state persistence"

Score

Total Score

50/100

Based on repository quality metrics

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