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Tutorials, how-tos and deep dives on AI, .NET, C#, cloud and developer tools, written by the community.
Learn how to design GPTs with Actions that securely connect to APIs, databases, and workflows, with guidance on governance, testing, scalability, and monitoring.
Gate2ASI AI's AgentFactory redefines enterprise AI execution with a governed digital workforce, moving beyond chatbots for dependable business outcomes.
Unlock the secrets of agentic AI system design: explore building blocks like model routing, tools, memory, orchestration, and production principles for reliable AI agents.
Build modular, scalable multi-agent AI systems in C# with Microsoft Semantic Kernel for complex business workflows.
AI governance evolves from model control to governing autonomous digital actors, emphasizing identity, authorization, and accountability for responsible operations.
Gate2ASI AI's AgentFactory builds governed digital teams with persistent memory, operational intelligence, and human oversight for reliable enterprise AI.
Deconstruct enterprise AI autonomy: explore its four-stage pipeline, essential guardrails, and how platforms streamline complex deployments.
Gate2ASI AI's AgentFactory revolutionizes enterprise AI with no-code design, empowering non-technical users to build intelligent agents effortlessly.
Deploy .NET 10 AI agents on Azure Container Apps for scalable, secure, and cost-effective production solutions.
AgentFactory by Gate2ASI AI offers enterprise AI setup without technical overhead, using natural language and wizards for instant productivity.
Unlock AI's potential with multi-agent architecture. Build a specialized digital workforce, not a single AI, for enterprise success.
Build and deploy powerful AI agents with the Azure AI Projects SDK for .NET. Streamline development, integrate services, and create intelligent applications.
Learn how Model Context Protocol (MCP) standardizes connections between AI applications and external tools, resources, and services, including its architecture, workflows, benefits, and security considerations.
Agent Social gives AI agents professional profiles for sharing insights, debating decisions, and collaborating with people in a transparent, auditable workspace.
Learn what Model Context Protocol (MCP) Registries are, how they work, and how enterprises use them to manage, secure, and scale AI tool ecosystems.
Moving Beyond Monolithic LLM-Centric Systems to Private, Specialized and Governed Digital Intelligence THE CHALLENGE: MONOLITHIC LLM-CENTRIC AGENTS Monolithic agent architectures place a large, general-purpose language model at the center of reasoning, orchestration and execution. Although this approach can accelerate experimentation, it introduces significant enterprise risks when deployed without strong architectural boundaries. These risks include prompt injection, unintended data exposure, opaque reasoning, excessive tool permissions, unpredictable feedback loops, variable latency and escalating operating costs.
Build structured AI workflows in .NET with Semantic Kernel's Process Framework. Orchestrate AI, logic, tools, and human interaction for complex applications.
Secure AI agents from Prompt Injection & HalluSquatting. Learn .NET strategies for input validation, tool control, and dependency verification.
Turning a Physically Verified Data Warehouse into a Trusted, Business-Owned Enterprise Capability Building an enterprise data warehouse is a significant achievement, but it is not the final destination. A warehouse may contain accurate dimensions, measurable business facts, historical information, reporting views, audit evidence, and reconciled source data. It may have passed technical validation and may be ready to support executive dashboards. Yet none of this guarantees that the organization is prepared to trust it, adopt it, operate it, or use it consistently in decision-making.
The original purpose of Zero Trust was clear and rational. Every identity should be verified explicitly. Every workload should receive only the access it requires. Every interaction should be observable, attributable, and governed. These are sound architectural principles.
Build scalable, event-driven AI agents with Azure Service Bus and .NET. Automate workflows, process asynchronously, and enhance enterprise AI.
Learn how to build a persistent memory layer for AI agents with PostgreSQL, embeddings, and semantic search to retrieve relevant context across conversations.
Master AI cost optimization in .NET. Learn token management, prompt engineering, caching, and model selection for sustainable AI.
Discover MCP Servers for secure AI agent tool integrations. Standardize, simplify, and enhance security for dynamic external system access.
Master AI agent observability with practical .NET logging, tracing, and monitoring. Debug, optimize, and secure your AI systems effectively.
Build an AI Agent Marketplace with ASP.NET Core & Semantic Kernel. Discover, manage, and monetize AI agents efficiently for your organization.
Learn practical strategies for testing LLMs and AI agents for accuracy, relevance, safety, hallucinations, tool use, and production reliability.
Build your first Agentic AI app in .NET 10! Learn to create intelligent agents that plan, use tools, and act autonomously. Step-by-step guide.
Explore AI-Native Architecture: designing apps around agents, memory, and intent for an agent-first world. Learn key principles.
Unlock AI potential with essential components & architecture of AI Engineering Platforms for scalable, secure, and efficient AI development.
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