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 Workflows and Orchestration Explained
Learn how agent workflows and orchestration coordinate steps, dependencies, branches, parallel work, retries, checkpoints, tools, agents, and humans.
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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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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.
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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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RAG vs AI Agent: What’s the Difference?
A decision-focused comparison of RAG knowledge retrieval and AI-agent execution, including when a simple RAG pipeline is enough and when an agent is justified.
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Build Your First AI Agent
Build a genuine AI task agent in plain Python with tool calling, observations, state, guardrails, logging, error handling, and tests.
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Single-Agent vs Multi-Agent Systems
Learn how single-agent and multi-agent systems differ, what extra coordination costs, and how to choose the simplest architecture that works.
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Planning in AI Agents: From Goals to Adaptive Action
Learn how AI agents turn goals into ordered tasks, account for dependencies and constraints, use tools, track progress, and replan when reality changes.
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Tool Use in AI Agents: How Agents Act Beyond the Model
Learn how AI agents select tools, prepare arguments, execute functions and APIs, observe results, recover from errors, and stay within safe permission boundaries.
