Artificial intelligence is rapidly advancing from simple automation to systems that can reason, plan, and act on their own. Building Autonomous AI Agents is a hands-on, practice-driven course that walks you through the end-to-end process of designing, developing, and deploying intelligent agents capable of independent decision-making.
Building Autonomous AI Agents
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Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
7 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Understand the core architecture and components of autonomous AI agents.
Build and orchestrate multi-agent systems using frameworks.
Integrate memory, reasoning, and tool-use capabilities into AI workflows.
Apply ethical, safety, and evaluation frameworks for trustworthy agent deployment.
Skills you'll gain
- Agentic systems
- JSON
- Scalability
- LLM Application
- Artificial Intelligence
- Artificial Intelligence and Machine Learning (AI/ML)
- Data Processing
- Prompt Engineering
- Tool Calling
- LangChain
- Generative AI Agents
- OpenAI
- Data Ethics
- Restful API
- Large Language Modeling
- Systems Integration
- Responsible AI
- LangGraph
- Data Validation
Details to know
Shareable certificate
Add to your LinkedIn profile
Taught in English
See how employees at top companies are mastering in-demand skills
There are 4 modules in this course
This module introduces learners to the foundations of single AI agents using the ReAct framework. Learners will explore the core concepts of agentic reasoning, tool usage, and memory integration. Through hands-on exercises, they will set up a development environment, define and use tools with structured inputs, and implement the ReAct loop for reasoning and decision-making. By the end of this module, learners will be able to deploy a functional agent capable of performing tasks with structured reasoning and short-term memory.
What's included
15 videos6 readings4 assignments
This module focuses on enabling a single agent to access, process, and act on external knowledge. Learners will work with retrieval-augmented generation (RAG) pipelines, including data ingestion, text embedding, and vector database indexing. They will integrate tools and actuators to enable decision-making and apply grounding techniques to ensure the agent produces contextually accurate outputs. By the end of this module, learners will have built a “strategy-grounded” agent that can reason over knowledge sources and generate validated outputs.
What's included
10 videos4 readings4 assignments
This module introduces learners to orchestrating, validating, and deploying single AI agents using LangGraph. Learners will design execution graphs, implement validation nodes, and integrate reflection loops for self-correction. They will also explore human-in-the-loop techniques and conditional logic for decision-making. Finally, learners will package their agent as a RESTful API, monitor its performance, and scale workflows for robust operation. By the end of this module, learners will have a fully operational, production-ready agent capable of autonomous task execution.
What's included
10 videos4 readings4 assignments
This module provides learners with an opportunity to synthesize their knowledge and demonstrate mastery of single-agent AI workflows. Learners will review key concepts from agentic foundations, RAG pipelines, and LangGraph orchestration. They will complete graded assessments, including scenario-based exercises and end-of-course knowledge checks, to apply their understanding in practical contexts. By the end of this module, learners will be able to confidently design, implement, and evaluate a fully functional single AI agent capable of reasoning, tool use, and executing grounded tasks.
What's included
1 video1 reading2 assignments
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Frequently asked questions
AI agents can not only chat but also take actions, use tools, and remember things to complete tasks. Regular AI models mainly generate text without acting or remembering.
You only need basic Python knowledge. Step-by-step instructions are provided for all coding exercises, so beginners can follow along easily.
ReAct stands for Reasoning + Acting. It teaches agents to think, take action, observe results, and improve, making them smarter than standard AI models.
More questions
Financial aid available,
¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.