Build agents that use tools safely: function calling, the agent loop, planning and memory, guardrails and human approval, testing with scenarios, real systems, and a guided task automation agent project.
Level
03
Modules
05
Lessons
22
Introduction
Downloadable Assets and Resources
What Makes a System an Agent
Function Calling and Tool Schemas
Designing Good Tools
The Agent Loop
Assessment
An agent is a language model that can decide what to do next and use tools to do it: query a database, call an API, write a report. That power is exactly what makes agents difficult to trust. This course shows you how to build agents that plan, remember and act, and, more importantly, how to make them reliable: with guardrails, budgets, approvals and tests.
You practice on a mock online shop: a database of customers and orders and simulated services, so an agent can do real-looking work without touching anything real. The model itself is a deterministic scripted model with flaws that you can imitate and defend against, so the failures that you must defend against actually happen in the lessons, every printed output is reproducible and no API key or payment is needed.
A realistic situation, a precise explanation with a diagram and the trade-offs, code that you run and modify, exercises with hints and a summary. You keep one running idea: an agent is code around a model, and the code is where reliability comes from.
The last module builds a Task Automation Agent for the shop: tools and the agent loop, then guardrails, approval steps and tests, and finally an evaluation and report. It comes with a template repository, a worked solution repository (zip files), the mock shop database and services, and a scenario file that defines the test cases.
Lessons on tools, the agent loop, planning and memory, reliability and real systems, a mock shop with a database and services, a scripted model that needs no API key, scenarios for testing, a guided task automation agent project with template and solution repositories, and a quiz for every module.
Agents are where AI stops answering and starts doing, and it is the fastest-moving area of the field. This course gives you the part that is missing from most demos: how to keep an agent safe, bounded and testable. You practice in a safe sandbox where mistakes cost nothing. If you want to build agents that people can actually trust with real work, enroll now and start building.
SkillEnsure certificates reflect hands-on practice and real-world skills, not just watching videos or reaching the end of a course.
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The certification framework is well designed. It’s structured enough to be trusted, but flexible enough for real-world skills across industries.
The certification framework was straightforward and well-organized. I liked having access to knowledge references while still being able to prove my understanding through assessment.
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