agent-deployment
by MattMagg
Claude Code plugins for building AI agents across frameworks (Google ADK, OpenAI, and more)
Manusで実行SKILL.md
name: Agent Deployment description: Production deployment workflow for agentic systems. Directs to RAG for implementation.
Agent Deployment Workflow
Deployment Decision Framework
| Framework | Primary Option | Alternative | RAG Query |
|---|---|---|---|
| ADK | Agent Engine (Vertex AI) | Cloud Run, GKE | "ADK deployment agent engine" |
| OpenAI | Any Python hosting | Serverless, Docker | "openai agents deployment" |
| LangChain | LangServe, Cloud Run | Docker, K8s | "langchain langserve deployment" |
| LangGraph | LangGraph Platform | Cloud Run | "langgraph platform deployment" |
| CrewAI | CrewAI Enterprise | Docker | "crewai deployment production" |
| Anthropic | Any Python hosting | Docker, 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
| Metric | Alert Threshold | Why It Matters |
|---|---|---|
| Latency p95 | > 5s | User experience |
| Error rate | > 1% | Reliability |
| Token usage | Spike > 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
| Concern | Solution | RAG Query |
|---|---|---|
| Cold starts | Keep warm instances | "[framework] cold start" |
| Concurrent requests | Queue + workers | "[framework] scaling" |
| Token limits | Request batching | "[framework] rate limiting" |
| State persistence | External store | "[framework] state persistence" |
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