Advanced AI-Assisted & Agentic Development

An advanced course on using agentic coding tools and AI across the whole SDLC at scale. Learners apply agentic coding for multi-file changes, integrate with CI/CD, review AI-generated code, and follow secure and reliable practices. The outcome is dependable AI-assisted engineering on real systems.

What this course covers

The course runs across 12 topics, each one a short adaptive session rather than a recorded lecture. The tutor explains the idea, works an example, checks understanding, and adjusts the next step to the answer given.

Foundations of Agentic Coding & AI Agents in the SDLC — Understand the transition from autocomplete to autonomous agents, exploring how AI agents plan, execute, and iterate on complex software tasks.
Configuring Agentic Workspaces and Context Providers — Set up your agentic coding environment, configure project indexing, and establish context boundary rules to optimize agent performance.
Multi-File Context Maps and Architecture Comprehension — Learn how agents construct context maps across large codebases and practice prompting agents to analyze system-wide patterns.
Executing Complex Multi-File Refactoring Tasks — Direct an agent to execute a structural change spanning multiple modules, managing imports, and adjusting APIs simultaneously.
Resolving Dependency and Build Errors Autonomously — Configure feedback loops where the agent runs builds, parses error logs, and iteratively fixes compilation issues across files.
Integrating AI Code Generation into CI/CD Pipelines — Set up automated pipelines that trigger AI agents to review pull requests, suggest fixes, or generate documentation on commit.
Automated Post-Commit Code Repair and Verification — Build a CI/CD workflow where failing tests trigger an AI agent to isolate the bug, write a patch, and verify the fix.
Critical Review and Human-in-the-Loop Validation — Develop rigorous review protocols for AI-generated pull requests, focusing on architectural alignment and subtle logical flaws.
Writing Test Suites for Agentic Output Validation — Author robust unit, integration, and property-based tests specifically designed to validate the correctness of agent-generated code.
Secure Coding Practices & Vulnerability Scanning for AI Outputs — Identify security risks (like prompt injection or insecure dependencies) in agent outputs and implement automated SAST checks.
Mitigating Hallucinations and Managing Context Limits — Apply advanced prompting strategies and context pruning techniques to keep AI agents grounded and prevent logical drift.
Scaling Agentic Workflows: Governance and Team Best Practices — Establish team guidelines, monitor API token usage, and define collaboration models between human developers and AI agents.

Skills you build

Results are measured against named skills in the OneRange taxonomy of more than 10,000 skills, so a manager sees proficiency per skill rather than a completion tick. This course maps to AI Engineering, Secure Coding Practices, CI/CD Implementation, Working with AI Tools, AI Agent Development.

AI Engineering
Secure Coding Practices
CI/CD Implementation
Working with AI Tools
AI Agent Development

Who it is for

Senior engineers, platform teams. The material is pitched at expert level, and takes roughly 180 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

It sits in the Function: Engineering track of the Vero AI Catalog, and can be assigned to one person, a team, or the whole company.

How it is delivered and assessed

Delivery is conversational and interactive, including guided lab, code lab, branching scenario exercises. There is no video to sit through and no slide deck to click past.

Understanding is checked with a code assessment of 5 items, with a pass mark of 70%.

Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

See how this training works for the whole function on AI training for Engineering.

Also in this track

AI for Security Engineering — A course for security engineers on AI in defensive work: log analysis, detection, incident response, and secure code review - plus the AI-specific threats they now defend against.
AI for Engineering & Software Development — This course helps software engineers accelerate development with AI coding assistants while keeping code secure and reviewed. Learners use AI for coding, review and debugging, tests and documentation, and apply secure-use practices. They leave shipping higher-quality code faster.
AI-Augmented Software Engineering — Ship better code faster with AI assistance

Frequently asked questions

How long does this course take?

It runs roughly 180 minutes at a typical pace. Because every session adapts, someone who already knows a topic moves through it quickly instead of sitting through an explanation they do not need.

How is it assigned to a team?

An administrator assigns it from the Vero dashboard to one person, a team, a department or the whole company, and sees progress and results per person and per skill.

Can we customise it with our own documents and terminology?

Yes. Administrators can copy this course into their own library and adapt it — edit the outline, change the duration, swap the assessment format, or ground it in internal documentation so answers cite the company's own source material.

Course code FUN-ENG-201.