Skill UI

A comprehensive, low-level Software Development Kit (SDK) for building and managing stateful AI Agents within the Azure AI ecosystem using .NET. This SDK provides core clients for managing agent lifecycles, persistent conversation threads, message history, complex run execution, tool/function calling, and integrating file search through vector stores. It is essential for developing enterprise-grade, multi-turn conversational AI applications.

A comprehensive, low-level Software Development Kit (SDK) for building and managing stateful AI Agents within the Azure AI ecosystem using .NET. This SDK provides core clients for managing agent lifecycles, persistent conversation threads, message history, complex run execution, tool/function calling, and integrating file search through vector stores. It is essential for developing enterprise-grade, multi-turn conversational AI applications.

This SDK enables the development of real-time, conversational AI applications using Azure Cognitive Services. It supports bidirectional communication via WebSockets, allowing for continuous audio streaming (mic input) and receiving synthesized speech output. Ideal for building voice assistants, IVR systems, and interactive voice experiences.

This skill set provides advanced, structured strategies for optimizing Large Language Model (LLM) interactions. It covers techniques such as intelligent context summarization, dynamic trimming, context routing, and precise token budget allocation to prevent 'context rot'. It is essential for building robust, long-running, multi-turn conversational AI systems.

Persistent LLM conversation memory that manages short-term, long-term, and entity-focused layers to store, retrieve, and consolidate memorable context for smarter conversational AI agents.

This skill guides users through the entire lifecycle of developing intelligent AI assistants, chatbots, and conversational interfaces. It specializes in establishing robust natural language understanding, managing multi-turn context, and defining best practices for creating seamless, production-ready AI applications with real-world value.

This skill provides implementation patterns for integrating Retell AI's advanced voice agent and telephony features. It is designed for developers building automated systems that require handling complex voice interactions, making scheduled phone calls, or managing conversational AI workflows within a CI/CD or automation environment.

This bundle provides implementation patterns and debugging utilities for AI voice agents and telephony functions using Retell AI. It is ideal for developers integrating voice-based conversational AI into phone call systems, customer service platforms, or any telephony workflow, covering SDK usage and common error handling.

This skill provides implementation patterns for building enterprise-grade AI voice agents and automating complex phone call workflows using the Retell AI platform. It covers SDK integration, resource management, and handling common telephony APIs errors, making it ideal for developers building conversational AI applications and advanced communication systems.

This skill provides implementation patterns for policy guardrails when utilizing Retell AI for voice agents and phone call automation. It is designed to help ensure that AI interactions in a telephony context adhere strictly to predefined business rules and safety policies, preventing unauthorized actions or deviations from the intended conversational flow.

Provides implementation patterns and reference architecture for building advanced AI voice agents and automating complex phone call workflows using the Retell AI SDK. This skill is designed for developers requiring robust voice interaction, real-time telephony integration, and comprehensive conversational AI capabilities in their applications.

This skill guides the development of advanced voice agents, representing the frontier of natural human-AI interaction. It covers both low-latency Speech-to-Speech (S2S) architectures (like OpenAI Realtime API) and fully controllable STT→LLM→TTS pipelines. Key focus areas include minimizing end-to-end latency (targeting <800ms), handling conversational nuances, background noise, and managing interruptions for applications like customer support, IVR, and live translation.