Overview

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. You’ll work with leading agent frameworks—including LangChain, Autogen, and AgentOps—and learn how to integrate models, tools, and APIs to build dynamic multi-agent ecosystems. Through guided demonstrations and structured labs, you’ll implement core agent components such as memory, tool use, planning modules, and goal-driven workflows, ultimately building a fully functional autonomous agent. The course also emphasizes safety, evaluation, and alignment practices to ensure that agents operate transparently, ethically, and reliably in real-world settings. By the end of this course, you will be able to: • Understand core agent architectures, memory types, and planning strategies. • Build agents using frameworks like LangChain, CrewAI, Autogen, and AgentOps. • Connect models, APIs, and external tools into cohesive multi-agent systems. • Implement goal-driven workflows with monitoring, evaluation, and safety controls. • Deploy production-ready autonomous agents capable of operating reliably in real environments. This course is ideal for AI developers, data scientists, software engineers, and technology professionals transitioning from basic prompt engineering to building fully autonomous systems. A foundational understanding of Python, APIs, and basic AI concepts is recommended, though all frameworks are introduced from scratch. Join us to master the tools and techniques that power the next generation of AI systems that can think, act, and collaborate with minimal human intervention.

Syllabus

The Agentic Foundation (ReAct & Tool Use)

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.

Context, Knowledge, and Grounding (RAG)

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.

Orchestration, Validation, and Deployment (LangGraph)

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.

Course Wrap-Up and Assessment

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.