AI Agents
Develop the skills to build a permission-scoped tool-using assistant through a learning path centred on AI Agents. Explore concepts, guided practice and an applied project.
Build practical AI Agents skills.
Skills and knowledge developed in this course.
- Agent execution
- Tool contracts
- Control and memory
- Agent evaluation
- Applied project: Build a permission-scoped tool-using assistant
- AI Agents — validation and review
About this course
This course develops practical capability in AI Agents. Its course-specific content covers Agent execution, Tool contracts, Control and memory, Agent evaluation, followed by an applied project focused on build a permission-scoped tool-using assistant. The lessons include concrete configuration, design, testing or operational decisions appropriate to the subject.
Who this is for
Administrators, developers and practitioners who work with AI Agents or plan to take on responsibilities in this area.
Before you start
Basic computer literacy and a foundational understanding of artificial intelligence & generative ai. Prior experience with the named product helps with the practical exercises.
Build a permission-scoped tool-using assistant
Deliver build a permission-scoped tool-using assistant. Use the module outcomes to state requirements, demonstrate a working result, test an expected failure or boundary case, and document assumptions and follow-up work.
Course content
Course-specific syllabus and practical exercises. Lab release, access arrangements and final delivery scope are confirmed before enrolment.
01Agent execution
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- Model decisions, tools and state
- Bounded workflows versus open-ended loops
Practical exerciseApply agent execution to the build a permission-scoped tool-using assistant and capture the validation evidence.
02Tool contracts
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- Typed inputs and validated outputs
- Authentication and least-privilege permissions
Practical exerciseApply tool contracts to the build a permission-scoped tool-using assistant and capture the validation evidence.
03Control and memory
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- State persistence and context selection
- Approval steps, retries and termination
Practical exerciseApply control and memory to the build a permission-scoped tool-using assistant and capture the validation evidence.
04Agent evaluation
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- Task success and tool-call traces
- Injection tests, budgets and human escalation
Practical exerciseApply agent evaluation to the build a permission-scoped tool-using assistant and capture the validation evidence.
05Applied project: Build a permission-scoped tool-using assistant
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- Define inputs, assumptions and acceptance criteria for: Build a permission-scoped tool-using assistant.
- Implement the project using agent execution, tool contracts, control and memory, agent evaluation.
Practical exerciseBuild a permission-scoped tool-using assistant.
06AI Agents — validation and review
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- Demonstrate the completed project against its acceptance criteria.
- Exercise an invalid-input or failure scenario and document the observed result.
Practical exercisepresent the project evidence, limitations and a repeatable handover.
Technical references
These primary vendor, project or standards sources inform this independent IT Purview outline. Reference pages checked 25 October 2026; current provider documentation and exam objectives take precedence when versions change.
From understanding to application.
Progress by learning milestone. The schedule is agreed around your starting level and learning goals.
- 01Learning milestone 1 In progress
Set the context
Agent execution · Tool contracts
Establish the AI Agents scope, baseline concepts and operating assumptions. - 02Learning milestone 2 In progress
Build the working skills
Control and memory · Agent evaluation
Apply the core AI Agents techniques in the course lab and record the result. - 03Learning milestone 3 In progress
Validate and handle exceptions
Check expected behavior, investigate a failure case and capture evidence for build a permission-scoped tool-using assistant. - 04Learning milestone 4 In progress
Deliver the applied project
Complete build a permission-scoped tool-using assistant, explain key decisions and identify a practical next step.
Career outlook after this learning path
Typical roles this roadmap can support, with broad salary ranges by relevant work experience.
| Role | 0–3 years | 3–7 years | 7–12+ years |
|---|---|---|---|
| AI / ML Engineer | ₹10–17 LPA | ₹20–35 LPA | ₹38–62 LPA |
Ranges are indicative India-market compensation estimates in ₹ lakh per annum, not a salary promise. They reflect broad market guides and vary by location, employer, skills and role scope; the experience bands refer to relevant work experience, not time since training. Completing this course does not guarantee employment, a role, compensation or career progression. View market reference ↗