ai-agent-tool-calling

What problem does it solve?

Standard AI chatbots are limited to answering questions from static training data and cannot interact with live databases or external systems. This Skill enables developers to build sophisticated AI agents that can execute tools, query databases, maintain conversation memory, and perform multi-step reasoning to complete complex business tasks like managing CRM records, generating quotations, and updating deal pipelines.

Core Features & Use Cases

• Tool-Calling Architecture: Define a registry of typed tools with Zod schemas that the LLM can invoke to perform actions like searching companies, creating quotations, or logging sales activities.
• Memory & RAG: Implement layered memory with short-term conversation history, long-term user fact extraction, and semantic search over application data using vector embeddings.
• Multi-Channel Deployment: Deploy the same agent logic across web, WhatsApp, Slack, and email using an adapter pattern.
• Production Hardening: Built-in safeguards including RBAC, rate limiting, streaming SSE responses, audit logging, PII redaction, and token cost caps.

Quick Start

Use the ai-agent-tool-calling skill to build a CRM assistant that can search for customers, draft quotations, and log sales activities through natural language chat.