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Multi-Hop Reasoning in RAG: Building Enterprise-Grade Intelligent Systems
Build a multi-hop RAG system for enterprise compliance investigations using LangGraph, Neo4j Graph RAG, stateful AI agents, and FastAPI.
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.
Build a multi-hop RAG system for enterprise compliance investigations using LangGraph, Neo4j Graph RAG, stateful AI agents, and FastAPI.
Build a cost-aware enterprise LLM assistant with LangGraph, model routing, Graph RAG, token limits, and usage monitoring.
Build a hybrid enterprise RAG system that uses LangGraph to route queries between chunk-level semantic search and document-level Graph RAG context.
Build a secure LangGraph-based customer support agent that detects prompt injection, routes safe requests, and protects Graph RAG data retrieval with Neo4j and FastAPI.
Build a real-time RAG financial advisory assistant with LangGraph multi-agent orchestration, Graph RAG, persistent memory, ChromaDB, Redis, FastAPI, and React.
Build a streaming multi-agent RAG system with LangGraph, FastAPI, pgvector, Azure OpenAI, and React, delivering retrieval progress, citations, and token-by-token responses.
Build a LangGraph multi-agent RAG pipeline that supports legacy and modern schemas during enterprise knowledge-base migrations using unified models, hybrid retrieval, and persistent state.
Build a version-aware RAG system with LangGraph and FastAPI that routes queries to compatible vector embeddings and supports embedding-model migrations.
Learn how semantic drift degrades RAG retrieval and how a multi-agent LangGraph workflow can detect temporal mismatches and retrieve current context.
Learn how AG-UI connects AI agents to real-time .NET interfaces using event streaming, ASP.NET Core endpoints, tool-call updates, and Blazor-compatible frontends.
Explains GitHub HydraFusion's multi-model coding approach, including agent orchestration, task routing, repository context, tool use, and validation workflows.
AI agents are becoming capable of performing tasks that previously required direct developer involvement. An agent can generate code, execute scripts, process files, query services, and combine multiple tools to complete a request. That capability creates a security problem that traditional applicat
Learn how AI security agents can analyze Android repositories, identify attack surfaces, trace data flows, validate findings, and support vulnerability discovery.
Learn how to use Visual Studio's Git Agent to review pull requests with repository context, assess code and tests, and identify security and regression risks.
AI agents are increasingly being used for tasks that require more than generating text. An agent may execute code, process files, call APIs, run data transformations, or perform multiple tasks at the same time. That creates an important production challenge: how do you let AI agents execute work wit
Build a multi-agent LangGraph RAG system that reduces retrieval latency using semantic caching, query-complexity routing, parallel retrieval, quantized embeddings, and performance profiling.
Suppose your company already has an internal support system. Employees use it to check tickets, look up customer information, and create requests. Now they want to do the same work from Microsoft 365 Copilot. You do not necessarily need to build another chatbot from scratch. Microsoft 365 Copilot ca
Learn how to design AI agent memory across sessions using persistent context, semantic retrieval, memory consolidation, expiration, privacy, and user controls.
Secure enterprise multi-agent RAG systems by encrypting prompts, protecting model endpoints with access controls and rate limits, and redacting PII before LLM inference.
Learn how to add a cross-encoder re-ranking agent to a LangGraph RAG workflow to filter retrieved documents and improve enterprise support answers.
AI voice agents are moving beyond simple voice assistants. Modern systems can listen to a caller, understand the request, retrieve information, call business tools, and respond naturally during a live conversation. That makes voice AI interesting for customer support, appointment scheduling, sales q
AI coding assistants are becoming more capable. Instead of only suggesting a line of code, an AI coding agent can inspect files, make changes, execute commands, run tests, and continue working based on the results. That additional capability is useful, but it also creates an important engineering qu
Learn how to optimize context windows in LangGraph multi-agent RAG systems using retrieval filtering, semantic compression, structured state, and a distiller agent.
AI coding tools have changed quite a bit. They are no longer limited to answering questions or suggesting a line of code. An agent can look at a task, decide what it needs to do, call tools, inspect files, and continue working based on the result. That becomes interesting when you want to put this k
AI agents are moving beyond simple question-and-answer workflows. A modern agent can search databases, call APIs, execute functions, read documents, send messages, create tickets, update records, and interact with external systems. That capability makes agents useful, but it also introduces a new se
Learn how AI agents behave without users, including identity, authorization, tool limits, retries, idempotency, human approval, security, and monitoring.
AI applications are moving beyond simple chat interfaces. Modern AI agents can call tools, retrieve information, execute workflows, ask for additional input, and perform multiple steps before producing a final response. However, many applications still present all of that activity through a basic te
AI agents become useful when they can do more than generate text. A production agent often needs to retrieve information, call business APIs, access databases, work with documents, or invoke specialized tools. This creates an architecture problem: how should an AI model connect to those capabilities
Build a production-ready FastAPI foundation for multi-agent RAG systems with Pydantic validation, JWT authentication, Redis rate limiting, and OpenTelemetry observability.
Learn when to use FastAPI async endpoints versus Celery background workers for scalable multi-agent RAG workflows, with LangGraph orchestration and persistent state.
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