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.
Topic
#reasoning
6 articles — updated daily
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.
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.
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.
Reasoning in AI Agents: How Agents Decide What to Do Next
Learn how AI agents interpret goals, break down tasks, handle uncertainty, choose tools, reflect on results, and decide what to do next.
How AI Agents Work: The Complete Execution Loop
Follow the seven-stage execution loop that lets an AI agent reason, choose actions, use tools, learn from results, and keep working toward a goal.