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Exploring AI Coding Agent Workflows (Summary)

Agentic coding is already reshaping how developers write, review, and ship code. Not all agents work the same way, though. The interaction mode matters as much as the tool itself.

In this video course, you’ve learned how to:

  • Identify the four coding agent workflow types: IDE, terminal, PR, and cloud
  • Understand what makes each workflow distinct, from real-time inline editing to highly autonomous cloud tasks
  • Recognize which mode fits common development scenarios
  • Weigh the risks and tradeoffs associated with each workflow

The boundaries between these categories will keep blurring as tools evolve. Rather than chasing the “best” tool, focus on the interaction mode that matches your current task. Use IDE agents for interactive editing, terminal agents for complex multi-file work, PR agents for automated review, and cloud agents for well-scoped tasks that can run in the background and be reviewed later.

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00:00 Agentic coding is already reshaping how developers write, review, and ship code. You’ve seen two tools in action, GitHub Copilot and Claude Code. Not all agents work the same way, and now you know why the interaction mode matters as much as the tool itself.

00:16 This chart gives you a quick reference for comparing the four types of AI coding agent workflows you’ve learned about in this course. In this course, you’ve learned how to identify the four coding agent workflow types, IDE, terminal, PR, and cloud.

00:32 You’ve seen what makes each workflow distinct, from real-time inline editing to highly autonomous cloud tasks. And you can now weigh the risks and trade-offs of each workflow.

00:45 Here’s a quick reference. Use IDE agents for interactive editing, terminal agents for complex, multi-file work, PR agents for automated review, and cloud agents for well-scoped tasks that can run in the background and be reviewed later.

01:02 The boundaries between these categories will keep blurring as tools evolve, but the interaction mode will always matter. To keep building your skills with AI-assisted development, check out these Real Python tutorials.

01:15 In LLM Application Development With Python, you’ll learn how to call LLM APIs from OpenAI, Ollama, and OpenRouter in your Python code. In Build an LLM RAG Chatbot With LangChain, you’ll learn how to use LangChain to build custom chatbots.

01:33 And in LangGraph Tutorial: Build Stateful AI Agents in Python, you’ll learn to expand LangChain’s capabilities with LangGraph by providing tools to build complex LLM workflows with state, conditional edges, and cycles.

01:48 Thanks for watching! Nos vemos en el próximo curso de Real Python. See you in the next Real Python course!

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