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Evidence brief

AI Coding Workflows teaches cloud-to-local coding.

DeepLearningAI launched the free short course AI Coding Workflows: From Cloud to Local, in collaboration with @JetBrains and taught by @paulweveritt, covering cloud, hybrid, and local AI coding setups.

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Editorial
Frontline Lab
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X
Source author
@DeepLearningAI
Related topics
1
Collected
2026-08-13

Frontline Lab summary and source

Editorial summary

DeepLearningAI launched the free short course AI Coding Workflows: From Cloud to Local, in collaboration with @JetBrains and taught by @paulweveritt, covering cloud, hybrid, and local AI coding setups.

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Related published evidence

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LangChain:Blog(RSS)

Agent workflows: LangChain explains what an AI agent is.

AI agents are systems that run autonomously in a large language model loop, completing complex tasks by repeatedly calling the model, observing results, and adjusting the next action. A workflow is a prearranged set of fixed steps. The two complement each other: workflows provide determinism, while agents provide flexibility. Understanding the difference is key to building reliable, production-ready autonomous systems.

Original source
GitHub Blog

OpenAI: how AutoGPT uses AGENTS.md and skill gating to manage AI-generated pull requests

AutoGPT maintainers found that AI agents do not proactively read documentation, so they put instructions in AGENTS.md and skill files next to the code directories. Through gating mechanisms such as mandatory PR templates, test plans, CI coverage thresholds, and CLA signatures, they turned agent-submitted PRs from “unusable” into “usable but not aligned with the roadmap.” Because CLA signing requires a browser and OAuth flow, it is used as a “human detector” to distinguish humans from agents.

Original source