Skill UI

Automates creation of A/B test configurations for ML deployment, guiding users through best practices, code samples, and validation steps so production models are served with monitoring and rollout control.

Generates production-ready Claude-powered agents with Google’s ADK, bundling React-style loops, tool-augmented capabilities, multi-agent orchestration, workflow automation, and test scaffolding.

Deploys and orchestrates Vertex AI ADK agents via the A2A protocol, covering AgentCard discovery, sandboxed code execution, memory banks, orchestration patterns, and Vertex AI Agent Engine rollout for CI/CD-style automation.

A comprehensive toolkit for designing, architecting, and evaluating complex multi-agent systems. It provides structured patterns—including Supervisor, Swarm, and Pipeline—for system architecture, defines agent roles (Coordinator, Specialist), and covers best practices for communication (Event Sourcing, Message Passing), tool design, and safety guardrails, ensuring robust and scalable AI workflows.

A comprehensive framework for rigorously testing and benchmarking LLM agents. It moves beyond simple pass/fail checks to assess complex behaviors, reliability metrics, and capability consistency across multiple runs. Ideal for production monitoring and identifying subtle failure modes in advanced AI agents.

Build persistent agents on Azure AI Foundry through the Microsoft Agent Framework Python SDK, hooking into AzureAIAgentsProvider for tools, hosted services, streaming, and threaded conversations.

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

Deliver production-ready LLM applications, RAG systems, and multi-agent orchestration with vector databases, embedding strategies, safety controls, and observability for enterprise deployments.

Deliver production-ready LLM applications, RAG systems, and multi-agent orchestration with vector databases, embedding strategies, safety controls, and observability for enterprise deployments.

Helps teams ship reliable AI-powered products by detailing LLM integration patterns, RAG architecture, scalable prompt engineering, UX resilience, and cost-aware design.

Defines how to build AI wrapper products that solve specific customer problems, covering prompt engineering, API cost control, usage metering, and defensible UX so AI becomes a paid, reliable feature.

Defines how to build AI wrapper products that solve specific customer problems, covering prompt engineering, API cost control, usage metering, and defensible UX so AI becomes a paid, reliable feature.