AI Agents can be a complex topic to understand and differentiate from RAG, AI workflows, Agentic workflows, and more. This guide will provide a definition of AI Agents with practical examples inspired by the Building effective agents manifesto from Anthropic.

Developing AI applications leverages multiple patterns from AI workflows with static steps to fully autonomous AI Agents, each fitting specific use cases. The best way to start is to begin simple and iterate towards complexity.

This guide features a Code Assistant that will will progressively evolve from a static AI workflow to an autonomous AI Agent.

Below are the different versions of our Code Assistant, each progressively adding more autonomy and complexity:

v1 Explaining a given code file

The first version starts as a AI workflow using a tool to provide a file as context to the LLM (RAG).

v2 Performing complex code analysis

Then, we will add Agentic capabilities to our assistant to enable it more complex analysis.

v3 Autonomously reviewing a pull request

Finally, we will add more autonomy to our assistant, transforming it into a semi-autonomous AI Agent.

New Pushing our Code Assistant to production

Discover the best practices to deploy your AI Agents to production.

Depending on your experience developing AI applications, you can choose to start directly with the second part covering Agentic workflows.

Happy coding!

Deployment
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Code Assistant v1: Explaining a given code file
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