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Learning path · 11 parts

AI Agents Fundamentals

by MLHub Labs · 11 parts · about 226 min · intermediate

What an agent is, how the loop of thinking and acting works and when a simple workflow is the better choice.

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Articles

Hands-On: An Agent in Plain Python, No Framework
Machine Learning
Part 11 of 11 · AI Agents Fundamentals

Hands-On: An Agent in Plain Python, No Framework

Build a working tool-using agent from scratch in plain Python: the loop, a tool registry, error handling, a human approval …

M

MLHub Agents & Robotics Organization

21 hours ago • 30 min read

0 0 20
Choosing a Framework, or None
Machine Learning
Part 10 of 11 · AI Agents Fundamentals

Choosing a Framework, or None

A practical look at what agent frameworks actually do for you, the main categories on the market, and a decision …

M

MLHub Agents & Robotics Organization

5 days, 13 hours ago • 16 min read

0 0 59
Human in the Loop
Machine Learning
Part 9 of 11 · AI Agents Fundamentals

Human in the Loop

How to design checkpoints where a person reviews, approves or corrects what an agent wants to do, and how to …

M

MLHub Agents & Robotics Organization

1 week ago • 19 min read

0 0 46
Errors, Retries and Stopping Conditions
Machine Learning
Part 8 of 11 · AI Agents Fundamentals

Errors, Retries and Stopping Conditions

Agents fail differently from normal programs: tools time out, models hallucinate arguments, and loops can run forever. Here is how …

M

MLHub Agents & Robotics Organization

1 week, 2 days ago • 25 min read

0 0 58
Context Engineering
Machine Learning
Part 7 of 11 · AI Agents Fundamentals

Context Engineering

A practical look at what actually goes into an LLM's context window during an agent loop, why stuffing it with …

M

MLHub Agents & Robotics Organization

1 week, 4 days ago • 18 min read

0 0 83
Memory: Short-Term, Long-Term and Notes
Machine Learning
Part 6 of 11 · AI Agents Fundamentals

Memory: Short-Term, Long-Term and Notes

How agents remember anything at all: what lives in the context window, what gets stored for later, and why a …

M

MLHub Agents & Robotics Organization

2 weeks, 1 day ago • 24 min read

0 0 89
Planning and Task Decomposition
Machine Learning
Part 5 of 11 · AI Agents Fundamentals

Planning and Task Decomposition

Why breaking a goal into smaller, checkable steps often beats asking a language model to do everything in one go, …

M

MLHub Agents & Robotics Organization

2 weeks, 2 days ago • 21 min read

0 0 99
Workflows Versus Agents
Machine Learning
Part 4 of 11 · AI Agents Fundamentals

Workflows Versus Agents

A clear line between code that decides what happens next and a model that decides for itself, with worked examples …

M

MLHub Agents & Robotics Organization

2 weeks, 4 days ago • 22 min read

0 0 82
Tools and Function Calling
Machine Learning
Part 3 of 11 · AI Agents Fundamentals

Tools and Function Calling

How a language model goes from predicting text to reliably triggering real actions: the mechanics of tool definitions, function calling …

M

MLHub Agents & Robotics Organization

2 weeks, 5 days ago • 17 min read

0 0 103
The Agent Loop: Observe, Think, Act
Machine Learning
Part 2 of 11 · AI Agents Fundamentals

The Agent Loop: Observe, Think, Act

A close look at the cycle that makes an AI agent an agent: how it observes its situation, decides what …

M

MLHub Agents & Robotics Organization

3 weeks ago • 21 min read

0 0 88
What an Agent Is, and When You Do Not Need One
Machine Learning
Part 1 of 11 · AI Agents Fundamentals

What an Agent Is, and When You Do Not Need One

A clear, practical definition of an AI agent, built from two contrasting examples, plus a checklist for deciding when a …

M

MLHub Agents & Robotics Organization

3 weeks, 2 days ago • 13 min read

0 0 127

Latest News

All news
  1. NVIDIA's Nemotron 3.5 Super VL Lands on AWS: One Model for Text, Images and Video Agents

    20 hours ago • 6 min read

  2. AWS Publishes a Blueprint for Running AI Agents Safely Across a Whole Company

    1 day, 20 hours ago • 5 min read

  3. How Exowatt's fine-tuned small models outperformed frontier AI on solar-plant tasks

    2 days, 20 hours ago • 6 min read

  4. AWS shows how to manage thousands of per-camera vision models with SageMaker SDK v3

    3 days, 20 hours ago • 6 min read

Popular Posts

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0 likes • 117 views • 1 comment

AWS shows how to manage thousands of per-camera vision models with SageMaker SDK v3

3 days, 20 hours ago • 6 min read

0 likes • 49 views • 0 comments

Transfer Learning and Fine-Tuning

1 week, 5 days ago • 21 min read

0 likes • 120 views • 0 comments

Colour Spaces: RGB, HSV and Greyscale

4 days, 21 hours ago • 15 min read

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Recent Activity

  • The MLHub Posts published Ten Data Leakage Mistakes and How to Catch Them

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  • MLHub Agents & Robotics published Hands-On: An Agent in Plain Python, No Framework

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  • MLHub Deep Learning published Filters and Blurs: Cleaning Up an Image

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