Learn it from developers who built it.
Tutorials, how-tos and deep dives on AI, .NET, C#, cloud and developer tools, written by the community.
Learn how to design a hybrid Copilot Studio tool layer using MCP servers and custom connectors, with practical guidance on API ownership, tool discovery, authentication, governance, ALM, and enterprise architecture.
For decades, enterprise capacity has been constrained by a simple equation: More work requires more people. If a business needs twice the output, it usually needs more employees, more contractors, more management overhead, more onboarding, more coordination, and more operating cost. Artificial intelligence is beginning to change that equation. But the real transformation is not simply about giving employees AI assistants.
Why Gate2ASI AI’s AgentFactory (Formerly AlpineGate AI's AgentFactory) Challenges Microsoft, Salesforce, ServiceNow, Google and AWS at the Architecture Level Gate2ASI AI’s AgentFactory — formerly AlpineGate AI’s AgentFactory — is not trying to win the enterprise agent race by building another chatbot, another workflow canvas, or another wrapper around a frontier model. It is pursuing a more ambitious architectural objective: creating the governed operating environment in which Digital Intelligence can function as an accountable enterprise workforce. That distinction is becoming more important by the month.
Benchmark Microsoft Foundry Hosted Agents for .NET by measuring deployment time, startup overhead, resource usage, and the operational cost of hosted AI agents.
Understand the difference between LLMs and AI agents with practical C# and .NET examples covering tool calling, memory, state, agent loops, RAG, guardrails, and enterprise AI architecture.
Human-in-the-Loop is not a bottleneck; it is the ultimate safety valve for enterprise AI. By leveraging LangGraph’s native interrupt() and Checkpointer mechanisms, you can build systems that combine the speed and analytical power of LLM-driven RAG with the judgment, accountability, and regulatory compliance of human experts.
Learn how to implement the Blackboard Architecture with LangGraph for multi-agent AI systems using centralized state, dynamic routing, RAG, and enterprise workflows.
Learn what Model Context Protocol (MCP) is, how hosts, clients, servers, tools, resources, and prompts work, and how MCP connects AI agents with enterprise systems.
By carefully choosing between appending, merging, and overwriting, you ensure that your RAG application doesn't just "work" it remembers, it learns, and it maintains a consistent view of the world across multiple agents. In the high-stakes environment of SOC, that consistency is the difference between a resolved incident and a security breach.
Benchmark AI agent tool selection with 10, 100, and 500 available tools to measure selection accuracy, latency, token usage, and wrong-tool calls.
Handling state divergence in a distributed LangGraph setup isn't about avoiding conflicts—it's about managing them through strict versioning and persistent storage.
In the TechCorp example, the ability to explicitly track retrieved_precedents and retry_count allowed us to build a system that was not only intelligent but also resilient and observable. In the enterprise, where every decision must be traceable and every failure must be handled gracefully, the flexibility of StateGraph is not just a feature it's a requirement.
In the enterprise, AI agents are no longer isolated scripts; they are collaborative teams. When you build a Multi-Agent System using LangGraph, you are orchestrating a complex dance where a "Router" might send work to a "Researcher," who then passes it to a "Validator."
Preventing infinite loops in multi-agent systems isn't about hoping for the best; it's about designing for the worst. By combining hard step limits, state-aware routing, and semantic loop detection (checking if we're doing the same thing twice), you can build an Enterprise RAG system that is not only intelligent but also incredibly resilient.
Learn how to build production AI agents with Claude Web Fetch in Microsoft Foundry for grounded responses, real-time web content, and reliable agent workflows.
Learn how to test AI agents when web search and external sources fail, including timeout handling, fallback strategies, graceful degradation, and response validation.
In the Construction and Infrastructure domain, project delays are rarely caused by a single factor. They are the result of complex interactions between structured schedules (Gantt charts, resource allocation) and unstructured realities (weather anomalies, supply chain disruptions, regulatory changes, and on-site labor sentiment).
Learn how to benchmark AI tool search accuracy across large enterprise tool catalogs by measuring selection accuracy, latency, token usage, and incorrect tool calls.
In the Advertising and AdTech industry, structured data historical Click-Through Rates (CTR), Cost-Per-Click (CPC), impression volumes, and bid prices—is the foundation of media buying. But structured data is inherently backward-looking. It tells you what did happen, not what will happen when the market shifts. To build truly predictive intelligence, we must answer a critical question: What unstructured context actually improves forecasting performance in AdTech?
In the Subscription and SaaS world, churn is the silent killer. Most companies rely on standard structured metrics to predict it: Monthly Recurring Revenue (MRR), contract length, and login frequency. While these are useful, they are often lagging indicators. By the time a user’s login frequency drops by 50%, they have already mentally checked out. To build a truly predictive churn model, we must look beyond the database rows. The biggest impact on classification performance comes from behavioral and semantic feature engineering.
In the high-margin, low-tolerance world of FMCG retail, the difference between reacting to a stockout and predicting it days in advance is the difference between lost revenue and optimized profitability. Predictive RAG is the bridge to that future.
Traditional forecasting models rely heavily on structured historical data—time series, numerical metrics, and categorical variables. However, **unstructured context** such as news articles, social media sentiment, earnings call transcripts, regulatory filings, customer support tickets, and market commentary often contains leading indicators that significantly improve forecast accuracy when properly integrated.
Learn how to track and attribute AI agent usage and costs across GitHub teams using agent-level metrics to improve visibility, governance, and FinOps.
Most developers treat Retrieval-Augmented Generation (RAG) as a "lookup" tool for Large Language Models (LLMs). You ask a question, it fetches a document, and the LLM summarizes it. This is a QA (Question-Answering) Workflow.
The artificial-intelligence industry is racing toward agents. OpenAI is building increasingly capable agent runtimes and model-native execution environments. Microsoft is expanding Copilot Studio into autonomous enterprise agents with permissions, governance, audit logging and human supervision. Google is giving agents identities, security boundaries and production observability. AWS is turning AgentCore into a managed runtime for identity, memory, policy, tools, evaluation and telemetry. Salesforce is surrounding Agentforce with observability and operational controls. LangGraph has made durable execution, checkpointing and human intervention first-class runtime concepts.
Learn how to build an autonomous AI campaign planner using LangGraph, RAG, and persistent memory to analyze market trends, competitor activity, internal services, and generate enterprise marketing strategies.
In the fast-paced world of B2B SaaS and digital services, competitive intelligence is no longer a quarterly report—it’s a real-time necessity. Waiting for a monthly newsletter to learn that a competitor has dropped prices or launched a new feature is too late.
Measure AI coding agent cache efficiency by analyzing context compaction, model switching, cache reuse, latency, token usage, and performance across agent workloads.
Learn how to build an enterprise-grade digital wallet transaction forecasting platform using LangGraph, RAG, Prophet, persistent memory, and AI agents for explainable spending predictions and anomaly detection.
Learn how to build smart KPI cards with LangGraph, RAG, and persistent memory to detect anomalies, explain SaaS churn trends, identify root causes, and generate actionable business insights.
Everything on AI Agents, not only its articles: the AI Agents page →
Mahesh Chand9 videos · 24 news posts+5.5Kpoints2
Aarav Patel21 articles · 1 reaction received+2.1Kpoints3
Ananya Desai21 articles · 1 reaction received+2.1Kpoints4
Riya Patel20 articles · 1 reaction received+2Kpoints5
Bhavya Singh20 articles+2KpointsFull leaderboard →
DevSocialUpcoming · 3rd editionAI Agents ConferenceNov 23–27, 2026Live onlineMembers only16days leftRegisterMembers onlyWrite an article and earn +1,000 SHARP when it is approved.
Write an article
