Route customer-support intents and orchestrate tools in LangGraph agent workflows.
npx skills add https://github.com/pepeccz/msi-a --skill msia-agent
Please help me install this Agent Skill. Skill: msia-agent Source: https://github.com/pepeccz/msi-a/tree/main/skills/msia-agent Command: npx skills add https://github.com/pepeccz/msi-a --skill msia-agent
MSI-a pattern for building scalable LangGraph-based conversational agents to route intents, manage state, and orchestrate tools in customer-support flows.
Instantiate a LangGraph-based agent pattern for customer support with mode-based routing and tool orchestration.
High-intent search queries and answers about installing and using this skill.
Build a LangGraph customer support agent by classifying user intent and routing queries to specific modes like CONSULTA or EXPEDIENTE. This pattern orchestrates tools for chatbot workflows, handling prompt assembly and state persistence automatically.
Intent routing in LangGraph state-management directs user queries to designated agent modes. It evaluates intent classification to switch between workflows like PRESUPUESTO or EVALUATION_GATEWAY, ensuring chatbots execute the correct tool-driven state contracts.
Manage state persistence across agent modes by applying a tool-driven state-management contract. The agent enforces per-mode retry logic and reusable prompt loading, ensuring reliable state tracking during mode switching for EXPEDIENTE and PRESUPUESTO workflows.
Yes, the agent pattern supports automotive homologation workflows by automating pricing, data collection, and customer inquiries. It uses mode-based architecture to handle specific processes like EXPEDIENTE and PRESUPUESTO within the automotive domain.
Handle failed tool calls in a LangGraph agent by implementing per-mode retry policies. The architecture defines retry logic within each specific agent mode, ensuring reliable tool orchestration and state management during customer support interactions.
Assemble prompts dynamically by using reusable prompt loading combined with per-mode context assembly. This approach constructs specific prompts based on the active agent mode, optimizing LLM usage and managing costs efficiently.
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