AI Agent Knowledge Library

Agent Architectures

Architectural approaches for single-agent, multi-agent, hierarchical, event-driven, and orchestrated agentic systems.

6 Published resources

Explore: Protocols & Interoperability Workflow & Orchestration Context Architecture

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Start with this guide

A host discovers multiple MCP servers, applies policy-aware routing, and executes calls through isolated client connections.

Architecture Guide Intermediate

Multi-Server MCP Architecture: Routing, Isolation, and Control

An architecture guide for coordinating multiple MCP servers without creating tool collisions, permission sprawl, or shared failure domains.

Knowledge library

Latest resources

6 published articles

Agents A, B, and C perform controlled reads and writes to a shared state and context store that an orchestrator uses to coordinate work.

Explainer Intermediate

Shared State and Context in Multi-Agent Systems

Learn how multi-agent systems separate local context from shared workflow state, exchange artifacts, synchronize updates, persist checkpoints, and avoid state conflicts.

A multi-agent system hub connected to supervisor-worker, orchestrator-specialist, router-selected-agent, parallel-agent aggregation, and peer-to-peer coordination patterns.

Explainer Intermediate

Multi-Agent Coordination Patterns Explained

Learn seven practical multi-agent coordination patterns and how they manage roles, ownership, state, handoffs, aggregation, conflicts, and stopping.

A side-by-side comparison of a centralized architecture where four agents connect through a supervisor and a decentralized architecture where four peer agents coordinate directly.

Comparison Intermediate

Centralized vs Decentralized Multi-Agent Architectures

Compare centralized, decentralized, and hybrid multi-agent architectures across control, state, coordination, scale, observability, governance, and resilience.

A complete AI agent architecture showing a user goal entering an agent core with instructions, context, state, and memory before reasoning, action selection, tool use, observation, state update, repetition, or finish.

Explainer Beginner

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.

An agent graph starting with request understanding and a router that branches to retrieval, tool use, or human review before evaluation, retry, or finish.

Explainer Intermediate

Agent Graphs and State Machines Explained

Learn how agent graphs and state machines make nodes, edges, branches, loops, checkpoints, transitions, retries, and terminal outcomes explicit.