The enterprise AI knowledge hub.
36 resources in one place — 20 plain-English concept explainers on the AI agent stack, plus 16 in-depth pillar guides across 6 tracks. From "what is an AI agent?" to architecture, governance, cost, and industry playbooks — everything you need to deploy enterprise AI you actually control.
New to on-premise AI? Begin with the essentials.
The three category-defining guides that anchor everything else in the library.
Why regulated enterprises need governed AI infrastructure they control.
Read guide 02 AI Agent OrchestrationHow multi-agent networks coordinate work across models, tools, and knowledge.
Read guide 03 AI Agent GovernanceControls, auditability, and policy enforcement for enterprise agents.
Read guideUnderstand the AI agent stack, one concept at a time.
Plain-English explainers on the building blocks of enterprise AI agents — from memory and retrieval to protocols, runtime, evaluation, and governance.
Agent Fundamentals
An AI agent is a software system that uses a large language model (LLM) as its reasoning engine to pursue a goal —…
7 min read What Is Agentic AI?Agentic AI refers to AI systems that act with agency — autonomously planning, making decisions, and executing…
7 min read What Is a Multi-Agent System?A multi-agent system (MAS) is an architecture in which several specialized AI agents collaborate — each handling part…
8 min read What Is an AI Agent Framework?An AI agent framework is a software library or toolkit that provides the building blocks for creating agents — the…
7 min readMemory & Retrieval
Agent memory is the set of mechanisms that let an AI agent retain and recall information across steps, tasks, and…
8 min read What Is Retrieval-Augmented Generation (RAG)?Retrieval-augmented generation (RAG) is a technique that improves LLM responses by retrieving relevant documents from a…
8 min read What Is a Vector Database?A vector database stores data as high-dimensional vectors (embeddings) and retrieves items by similarity of meaning…
7 min read What Are Embeddings?Embeddings are numerical representations — lists of numbers, or vectors — that capture the meaning of text, images, or…
6 min read What Is Semantic Search?Semantic search finds results based on meaning and intent rather than exact keyword matches. It converts queries and…
6 min read What Is Context Engineering?Context engineering is the discipline of deciding exactly what information enters a model's context window at each step…
7 min readProtocols & Interop
The Model Context Protocol (MCP) is an open standard that defines how AI applications connect to external tools, data…
7 min read What Is Tool Use / Function Calling?Tool use — often called function calling — is the capability that lets a language model invoke external functions…
6 min read What Is Agent-to-Agent (A2A) Communication?Agent-to-agent (A2A) communication is how independent AI agents discover, delegate tasks to, and collaborate with one…
6 min readReasoning & Runtime
Agent runtime is the execution environment that actually runs an AI agent — driving the reason-act loop, managing state…
7 min read What Is the ReAct Pattern (Reasoning + Acting)?ReAct (Reasoning + Acting) is a foundational agent pattern in which a model interleaves thinking and doing: it reasons…
6 min read What Is LLM Inference?LLM inference is the process of running a trained large language model to generate output from an input — turning a…
6 min readReliability & Governance
Agent evaluation is the practice of systematically measuring how well an AI agent performs — not just whether its final…
7 min read What Is Agent Observability?Agent observability is the ability to see and understand what an AI agent did at every step — its reasoning, tool…
6 min read What Are AI Guardrails?AI guardrails are the controls and constraints that keep an AI agent operating safely and within policy — validating…
6 min read What Is Human-in-the-Loop (HITL)?Human-in-the-loop (HITL) is a design approach that keeps people involved at key points in an AI system — approving…
6 min readNo concepts match your search. Try a different term, or browse the tracks below.
Foundations
Category-defining pillars: what an enterprise AI agent platform is, and how its core layers fit together.
5 guidesWhy regulated enterprises need governed AI infrastructure they control.
Read guide AI Agent OrchestrationHow multi-agent networks coordinate work across models, tools, and knowledge.
Read guide AI Agent GovernanceControls, auditability, and policy enforcement for enterprise agents.
Read guide Private RAGSecure retrieval-augmented generation over enterprise-controlled knowledge.
Read guide LLM RoutingUse the right model for each task based on quality, cost, latency, and policy.
Read guideArchitecture & Patterns
Advanced retrieval and orchestration patterns for enterprise stacks past the pilot stage.
3 guidesWhen multi-agent retrieval wins, and how to bound its cost and quality.
Read guide Knowledge Graph RAGGraphRAG for relational and multi-hop questions, and where hybrid retrieval beats either alone.
Read guide Secure Multi-Agent NetworksZero-trust patterns, prompt-injection defense, and anti-hallucination strategies.
Read guideSecurity & Compliance
Mapping legal and supervisory requirements to runtime controls and sovereign deployment.
2 guidesCost & Economics
Practical frameworks for the cost, energy, and optimization decisions behind enterprise AI.
2 guidesEvaluation & Comparisons
Choosing between platforms, frameworks, and Microsoft-native alternatives.
3 guidesWhen enterprises need more control than a cloud-first Copilot-style platform provides.
Read guide Microsoft Copilot Studio ComparisonSide-by-side on data residency, customization, governance, orchestration, and total cost.
Read guide Enterprise AI Platform EvaluationRFP checklist, POC guide, and vendor scorecard for AI platform selection.
Read guideIndustry
Vertical playbooks for finance, healthcare, legal, and manufacturing on one horizontal platform.
1 guideTurn reading into a decision.
Move from the knowledge cluster into comparisons, products, and a conversation with our team.
Evaluate VDF AI against LangGraph, CrewAI, AutoGen, Dify, Agentforce, and Microsoft Copilot Studio.
ProductsSee how VDF AI Agents, Networks, and Chat map to the platform, orchestration, and retrieval layers.
PricingReview the commercial model once you understand the platform architecture and deployment tradeoffs.
Book a demoUse the resource cluster as preparation for a deeper conversation about your deployment model.
See VDF AI running in your environment.
Bring the questions this library raised. We will map them to a governed, on-premise deployment built around your data, your models, and your compliance requirements.