Building AI Agents & Agentic AI System via Microsoft Autogen

Install Anaconda and VS Code to create and manage Python environments, then set the path and use VS Code to run .ipynb and .py files.

Learn to create and manage virtual environments with conda, activate them, and install packages via a requirements.txt in your project workspace.

Explore variables in Python by declaring, assigning, and naming them, and understand variable types. Master type checking, dynamic typing, and conversion with practical examples like input and a calculator.

Learn Python lists as ordered, mutable data structures; create, access, modify, slice, and iterate, and master list methods and list comprehension, including nested forms.

Explore real-world uses of Python lists with to-do lists, grades, and inventory, using append, remove, in checks, and built-in functions like max, min, sum to compute averages.

Explore how Python sets store unique items, enable membership tests, and perform union, intersection, and difference operations, with practical examples for removing duplicates and counting unique words.

Master dictionaries in Python by creating, accessing, and modifying key-value pairs; explore nested dictionaries, comprehension, and common methods like keys, values, and items, with practical coding examples.

Explore tuples in Python: creating, accessing, and manipulating immutable ordered collections, including packing and unpacking, nested tuples, and common methods like count and index, with practical examples.

Explore how to define and call Python functions, use parameters including default and variable-length arguments, distinguish positional and keyword arguments, and implement return statements and docstrings to organize code.

Explore practical Python function examples, including temperature conversion, password strength checks, shopping cart total cost, palindrome detection, recursive factorial, file word frequency counting, and email validation with regular expressions.

Explore Python lambda functions, an anonymous one-expression tool for quick operations and higher-order use. Learn syntax, examples, and how map and filter leverage lambda for list processing.

Explore the Python standard library overview, covering modules such as array, math, random, os, json, csv, datetime, time, and re for file operations, data serialization, and pattern matching.

Explore Python file operations by reading and writing text and binary files using with open. Learn text line reading, appending, writing vs overwriting, and cursor seek to reset reading position.

Create directories, list files and folders, join paths, and check for file or directory existence using the OS module, while distinguishing absolute and relative paths.

learn exception handling in Python using try, except, else, and finally blocks to gracefully handle errors, from name and zero division errors to file operations that close resources reliably.

Explore classes and objects in Python, define attributes and methods, and initialize with a constructor using self, shown through a bank account example with deposit, withdraw, and balance.

Explore polymorphism in Python through method overriding and interfaces, use abstract base classes, and see shape and vehicle examples including rectangle and circle area calculations and start engine.

Explore encapsulation and abstraction in Python object-oriented programming by bundling data and methods into a single unit, restricting access with private and protected variables and using getters and setters.

Explore how magic methods, or dunder methods, define object behavior for built-in operations in Python. Override __init__, __str__, and __repr__ to customize initialization and string representations of objects.

Explore custom exception handling in python by creating a generic error class and a date of birth exception, then validate age between 20 and 30 with a try-catch block.

Master Python generators by using yield to lazily produce values and save memory, compare with iterators, and apply to processing large files efficiently.

Explore advanced python decorators, including function copy and closures, to modify function behavior without changing code, create reusable wrappers, and apply decorators with arguments using @ syntax.

Learn to log with multiple loggers in Python's logging framework by creating module-specific loggers, setting their levels, and emitting debug, warning, and error messages.

Demonstrate Python logging by configuring level, format, and file and stream handlers, writing to app_one.log; define arithmetic app logger with add, subtract, multiply, divide and debug and error messages.

Explore what agentic AI is, how autonomous AI agents decide and act, and how multi-agent workflows use tools to automate tasks across real-world scenarios.

Learn how microsoft autogen serves as a framework for building AI agents and applications, enabling single and multi-agent systems to collaborate on end-to-end solutions. Explore its architecture, components, and open-source ecosystem within microsoft ai, azure, and semantic kernels.

Explore autogen v0.4, a complete rewrite with asynchronous foundation and layered design, clarifying the AG2 split, 0.2 to 0.4 migration, and observability for production with Azure Semantic Kernel.

Explore large language model providers to power your first autogen agent, including Gemini and OpenAI. Get keys, configure a model client, and use free or local options.

Compare Autogen with Langchain and other frameworks, focusing on architecture, multi-agent capabilities, and observability; Autogen’s asynchronous design enables collaborative AI, whereas Langchain favors sequential, single-agent workflows.

Compare Autogen with other AI frameworks, highlighting its core and agent chat APIs, template-based customization, maturity, and use cases from team tasks to complex enterprise workflows.

Compare Microsoft Autogen and Microsoft Semantic Kernel, highlighting Autogen's multi-agent collaboration and standalone agent network against Semantic Kernel’s integration, function composition, and skill memory for AI in existing apps.

Compare Autogen and Lama index to show Autogen's strength in dynamic multi-agent interactions and task automation, versus Lama index's knowledge retrieval and document integration for enterprise data.

Master async functionality in Python to build real-time agent interactions with Autogen. Learn synchronous vs asynchronous, subroutine versus coroutine, and concurrency and parallelism, with practical code demonstrations.

Build your first agent with the Autogen Agent Chat API in a Jupyter notebook, wiring a GPT-4 model client and running asynchronous conversations to answer real tasks.

Configure the agent's brain by selecting OpenAI Gemini or other locally hosted models with Autogen, and learn to connect via the OpenAI chat completion client using a Gemini API key.

Explore how tools in Autogen version 0.4 let an agent act by calling external services and APIs, using a weather tool as a practical example.

Explore messages in Autogen version 0.4 and how agents communicate, using text and image messages for multimodal tasks, with a hands-on coding demo and future tool messages.

Learn how to run and observe a single agent in Microsoft Autogen, using agent run and on message stream to inspect tool calls, inner messages, and streaming output.

Explore multi-agent collaboration in Autogen, connecting specialized agents to tackle complex tasks through shared messages and tasks. Understand how specialization, asynchronous execution, and resiliency boost performance over a single agent.

Observe how a multi-agent team built with round robin group chat collaborates in real time using Autogen, streaming live messages and task results to reveal stop reasons and collaboration dynamics.

Explore team operation in Autogen by observing agents, running them in a round-robin group chat, and applying reset, stop, resume, and abort to manage stateful workflows.

Explore how termination conditions control Autogen teams, including max message termination and text mention termination, and how to combine them for production-ready workflows.

Learn how external termination controls stopping a team from outside the run in Microsoft Autogen, using a set method to stop after the current turn completes.

Discover how aborting a team immediately stops execution and raises a canceled error, using a cancellation token to cancel pending async calls in Autogen core.

Discover single agent teams in Microsoft Autogen, enabling one agent to function as a controllable, observable, scalable team with termination conditions; learn setup and testing via a short poem task.

Learn to manage the state of autonomous teams in Microsoft Autogen by saving and loading team and agent state, resetting contexts, and persisting state to enable reliable reloading.

Explore human in the loop concepts in Autogen, interact with a team of agents using a user proxy and run-time feedback, and learn how feedback shapes agent outputs.

Learn to implement handoff termination in Autogen, pausing runs to capture user feedback, using handoff messages, termination conditions, and text mention termination for interactive AI agents.

Explains Autogen studio components—agents, models, tools, and terminations—how teams use round-robin or selector workflows to manage AI assistants.

Showcases integrating a local LLM with Autogen Studio via LM Studio, configuring a Mistral 7B model, and testing locally. It also covers addressing setup errors to ensure reliable local inference.