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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Reflection in AI Agents: How Agents Review, Correct, and Improve Their Work
Learn how AI agents use feedback, critique, and execution review to detect mistakes, revise their approach, and improve results without endless retry loops.
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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.
