Centralized vs Decentralized Multi-Agent Architectures
Compare centralized, decentralized, and hybrid multi-agent architectures across control, state, coordination, scale, observability, governance, and resilience.
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Agent Routing Patterns: How Agents Choose the Next Worker
Learn seven agent-routing patterns, from deterministic rules and classifiers to semantic, capability-aware, hierarchical, and fallback routing.
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AI Agent Architecture: Components and Data Flow
Understand modern AI-agent architecture from goals and instructions through reasoning, tools, observations, state updates, guardrails, and stopping.
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Agent Handoffs, Delegation, and Sub-Agents
Learn how delegation, handoffs, and sub-agents divide work while preserving task ownership, context, state, permissions, and reliable result contracts.
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Vector Databases Explained for AI Agents
Learn how vector databases store embeddings, power semantic search, and support RAG and memory without replacing a knowledge base or relational database.
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Reranking in RAG: Why Retrieval Quality Matters
Learn why RAG pipelines rerank retrieved candidates, how cross-encoders and other methods improve ordering, and what reranking cannot fix.
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Hybrid Search vs Dense vs Sparse Retrieval
Compare sparse, dense, and hybrid retrieval by matching signal, strengths, failure modes, fusion methods, and the evidence needed to choose a RAG baseline.
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Chunking Strategies for RAG
Compare fixed-size, recursive, semantic, and document-aware chunking for RAG, with practical guidance on chunk size, overlap, metadata, and evaluation.
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Build Your First RAG Agent
Build a framework-neutral RAG agent with a controlled retrieval tool, attributable evidence, bounded loops, citation checks, traces, and layered evaluation.
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Embeddings Explained for AI Agents
A beginner-friendly mental model of embeddings, vectors, similarity, and how AI agents use them for retrieval and memory without confusing similarity with truth.
