ai-agents

RAG agents combine retrieval-augmented generation with tool use to fetch data and return structured output. This Skill provides production-grade patterns for designing RAG agents, including guardrails, memory management, and integration with frameworks like LangGraph and MCP for safe, observable orchestration.

Multi-agent orchestration requires safe handoff logic and coordinated decision-making across agents. This Skill covers handoff patterns, multi-layer guardrails, and decision rules to route tasks between agents reliably, with templates ready for CI/CD deployment.

Observability in agent systems requires integrated tooling and tracing across orchestration steps. This Skill provides patterns for memory tracking, event logging, and OpenTelemetry-compatible observability practices so you can monitor and debug multi-agent pipelines.

Yes. This Skill includes templates and patterns for integrating MCP (Model Context Protocol) with LangGraph and other frameworks, enabling modular tool definitions, interoperable schemas, and reusable agent blueprints across different orchestration tools.

Production agents require validation logic, governance rules, and safety constraints at multiple layers. This Skill provides decision rules, modular templates, and guardrail patterns that satisfy functional and technical requirements for regulated or high-stakes deployments.

Deployment readiness means modular templates, schema validation, and pipeline integration. This Skill provides CI/CD-ready agent blueprints, reusable schemas, and governance structures that interoperate with standard deployment frameworks.