Optimize Python for Agentic AI

This course is part of Agentic AI Development & Security Specialization

Instructor: LearningMate

Access provided by Upwardly Global

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Refactor Python code for AI agents to improve readability and performance. Apply coding standards and use profiling to optimize bottlenecks.

Skills you'll gain

Tools you'll learn

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments¹

AI Graded see disclaimer

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Agentic AI Development & Security Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 2 modules in this course

"Optimize Python for Agentic AI" is an intermediate course for developers who want to elevate their Python code from functional to professional-grade. In the world of AI, inefficient or unreadable code can cripple an agent's performance and slow down team collaboration. This course equips you with the essential software engineering practices to write Python that is both highly efficient and exceptionally clear.

You will learn to apply clean-code conventions, including PEP 8 standards, type hints, and descriptive docstrings, to produce readable and maintainable modules that your teammates can easily understand and build upon. Through hands-on labs, you will master the art of performance tuning by systematically using profiling tools like cProfile to analyze runtime behavior, pinpoint hidden bottlenecks, and refactor code for significant speed improvements. By the end of this course, you will be able to confidently balance readability with runtime efficiency, ensuring the AI systems you build are not only intelligent but also robust, scalable, and production-ready.

You start by understanding how professional AI systems depend not only on smart logic but also on clean, readable Python. When deadlines hit, messy code slows teams down. This module shows you why clarity and consistency matter for maintainability and collaboration. You will refactor a memory manager from an AI agent, practice adding type hints and docstrings, and run style checks with flake8. You will see how these conventions keep complex AI projects manageable—and you will apply them to produce production-quality Python.

What's included

2 videos1 reading1 assignment1 ungraded lab

Now that your code reads cleanly, it is time to make it run fast. Performance problems often hide in unexpected places. You will learn when to profile, how to interpret profiler data, and how to refactor Python for efficiency. By the end of this module, you will replace inefficient regex calls and confirm improvements through benchmarks. You will close the course by combining maintainability with performance in a final strategy report.