AI Agents for Training Developers: What They Are and How to Start Using Them

Odin Training
Apr 16
4 min read

Most training developers are familiar with AI as a prompt-and-response tool: you ask, it generates, you edit. That model is not going away, but it is no longer the only one worth knowing. AI agents represent the next step, and in 2026 they are moving from early experimentation into production workflows across L&D teams.

According to Google Cloud's AI Agent Trends 2026 report, 65 percent of organizations are now actively experimenting with AI agents, and 84 percent of enterprises plan to increase their spending on agentic AI this year. For training developers, this shift matters not because you need to become an AI engineer, but because understanding what agents can do changes how you think about your own workflow.

What Is an AI Agent, Actually?

A standard AI interaction is a single exchange: you provide a prompt, the AI returns a response, and the session is complete. An AI agent is different. It is designed to pursue a goal across multiple steps, using tools and making decisions along the way, often without you prompting each step individually.

In practical terms, an AI agent might take a policy PDF, extract the key compliance requirements, draft a scenario-based quiz, generate a script for a short video module, and organize all of it into a folder, starting from one instruction. What would take a training developer a half-day of manual work becomes a review-and-refine task. The language you will hear around this includes agentic workflows, multi-step automation, and autonomous task completion. These all describe the same shift: AI that acts rather than just responds.

What AI Agents Can Actually Do for Training Developers

Here is where it gets concrete. Current AI agents are being used in L&D contexts to:

Convert source documents such as policy manuals, job task analyses, and SOPs into structured course outlines
Generate full microlearning packages including scripts, slide outlines, and quiz questions from a single source file
Summarize learner feedback and cluster it by theme for post-training analysis
Create multiple versions of the same content tailored to different audiences or delivery formats
Automate administrative tasks such as tracking completions, sending reminders, and generating progress reports

The time savings are significant. AI video tools combined with agentic workflows are reducing course production from 80-plus hours to under five hours for comparable output, with 67 percent of users reporting saving 10 or more hours per week on content development tasks alone.

How Agents Fit Into Your Workflow (And Where You Still Lead)

The commander-agent model is a useful frame for understanding how this works in practice. You provide the strategic intent: the audience, the learning objective, the performance context, the quality standard. The agent handles the tactical execution: drafting, structuring, formatting, generating variations. You direct. It executes.

What this is not is a fire-and-forget system. AI agents in 2026 still require human oversight at every stage that matters. They will hallucinate facts. They will miss nuance. They will produce content that is technically correct and instructionally flat. Your judgment, your knowledge of the learner, and your standards for what gets published are what make the output worth using. The agent does the volume work. You do the design work.

Where to Start if You Have Never Used an Agent

You do not need a dedicated agentic AI platform to start experimenting. The most accessible entry point is a multi-step prompt chain you build yourself using a tool you already have.

Start with a document-to-outline workflow. Give an AI tool like Claude or ChatGPT a policy document or job task analysis and ask it to complete four steps in sequence: identify the key performance tasks, write a learning objective for each, suggest a module sequence, and draft three scenario-based quiz questions per module. That four-step chain is an agentic workflow you can run today.

Once you are comfortable directing multi-step tasks, move to multi-file workflows. Connect your source materials: SME interview transcripts, competency frameworks, existing course content. An agent can cross-reference these and surface gaps or inconsistencies that you would otherwise catch only through close manual review.

The Risk You Cannot Ignore

The most important risk to manage is quality drift. When agents produce large volumes of content quickly, there is a real pressure to reduce review time. That is exactly backwards. The faster the production, the more disciplined your review process needs to be.

The second significant risk is factual accuracy. AI agents draw on training data that may be outdated, incomplete, or wrong for your specific context. In regulated training environments, including law enforcement, healthcare, and compliance training, this is not a minor concern. Every AI-generated claim that will be trained on as fact needs to be verified by a human with subject matter authority. Build your quality gates before you build your agent workflows, not after.

Want to Build These Skills With Your Team?

If you want your training team to start building practical agentic workflows for course development, I offer private 4-hour virtual workshops designed specifically for training developers. We work through your department's actual projects and source materials so participants leave with workflows they can use immediately, not just a theoretical understanding of what AI agents are.

Format: Private virtual sessions for up to 20 participants

Investment: $2,000 USD / $2,500 CDN per workshop

Reach out at kerry.avery@shaw.ca to learn more, or visit the workshops page on this site.