Building AI Agents for Complex Tasks is an intermediate-level course designed to equip learners with the skills to design, build, and evaluate intelligent agents that operate autonomously across dynamic, multi-step environments. Moving beyond simple chatbot flows, this course introduces learners to agent architectures that perceive context, make decisions, integrate tools, and recover from failure.
Building AI Agents for Complex Tasks
Building AI Agents for Complex Tasks
Instructor: Hurix Digital
Access provided by Masterflex LLC, Part of Avantor
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
4 hours to complete
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
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Taught in English
Recently updated!
December 2025
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There are 3 modules in this course
This foundational lesson introduces what AI agents are and how they differ from traditional software. Learners will explore agent-environment interactions, the concept of perception, and how various types of agents—reactive, deliberative, and hybrid—handle decision-making. Through real-world examples like smart assistants and warehouse robots, learners will classify agent types and determine where each model excels or breaks down.
What's included
4 videos1 reading1 assignment
This lesson moves from theory to implementation. Learners will construct intelligent agents that integrate inputs (perception), structured reasoning (decision loops), and output (action). They'll explore core modules such as memory, planning chains, and tool execution in LangChain and Rasa. Real-world examples like Alexa’s task-based updates and LangChain agents with tools will help frame the technical walkthroughs.
What's included
3 videos1 reading2 assignments
In the final lesson, learners will focus on evaluating how agents perform in realistic, changing environments. They'll explore testing strategies, interpret edge-case behaviors, and fine-tune agents using logs, performance feedback, and outcome tracking. Examples such as AlphaCode’s reasoning iterations and BabyAGI’s task queue refinement will help frame the concepts. This lesson culminates in the Capstone project, where learners will apply everything they've learned to design and deliver an intelligent, goal-driven agent.
What's included
4 videos1 reading4 assignments
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