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
What Makes a System an Agent
Downloadable Assets and Resources
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 certifications are built around demonstrated competency and real-world capability beyond traditional course completion or attendance-based certificates.
Having a verified SkillEnsure credential made a real difference during interviews. It gave employers confidence in my abilities.
The assessment criteria were clear, and the entire certification felt fair and transparent. It’s a system I can confidently showcase.
SkillEnsure helped me turn years of experience into a recognized credential. The certification process was straightforward, credible, and focused on demonstrated knowledge.
Many certifications focus on course completion. SkillEnsure focuses on demonstrating understanding against defined competency criteria, which made the credential feel more valuable to me.
