Beyond Content at Rest: Unlocking Agentic Workflows, Metadata, and Real-Time Content Generation

Introduction

Traditionally, L&D content has been designed, packaged and stored in repositories such as SCORM packages, PDFs or pre-recorded videos. While this model suits certain evergreen topics, it struggles to keep pace with evolving skill demands and shifting organisational priorities. Today, AI technologies offer new possibilities through agentic workflows and agentic metadata creation, real-time generation of content from primary data sources and retrieval-augmented generation (RAG) approaches that deliver learning exactly when and where employees need it. This article explores how to move beyond static content and embrace dynamic, context-aware learning solutions.

The Limitations of “Content at Rest”

“Content at rest” refers to pre-existing and typically unchanging learning materials stored in an LMS or intranet. Such content may quickly become outdated and often lacks immediate relevance to learners’ in-the-moment challenges (Bersin, 2020). In a world where time poverty is ubiquitous, employees crave just-in-time contextually modern and relevant guidance, not modules designed months or even years ago, that they have to click through to find the one piece of relevant information that they need.

Agentic Workflows and Metadata

1. Agentic Workflows

Agentic workflows allow an AI “agent” to orchestrate tasks, gather data and deliver appropriate resources. For instance, if an employee is using a software application and encounters an unfamiliar function, an AI agent could generate a quick tip or micro-lesson on-the-fly based on the user’s context (Microsoft, 2021).

2. Agentic Metadata Creation

AI can also analyse and generate metadata in real time, continuously tagging and indexing learning resources based on changes in business needs or new regulatory requirements. This helps ensure that when learners search for “current compliance guidelines”, the AI surfaces the most up-to-date materials, rather than merely the oldest SCORM package that matches the keywords.

Retrieval-Augmented Generation (RAG) and Just-in-Time Content

1. What is RAG?

Retrieval-Augmented Generation combines large language models with curated knowledge bases. Rather than returning a static PDF link, the AI can synthesise information from multiple documents and present a concise, contextually relevant explanation. This process merges the strengths of dynamic AI generation with the reliability of verified data sources (IBM, 2022).

2. Primary vs. Secondary Data

L&D content often recycles secondary sources, including industry best practices, summarised case studies and third-party training materials. By contrast, AI-driven real-time generation can pull from an organisation’s own primary data, including internal process documents, project wikis and user-generated feedback. This ensures employees receive guidance that is highly specific to their context and problem, rather than generic courseware.

3. Case Example

Consider a global manufacturing firm releasing updated safety procedures. Instead of waiting to finalise a new e-learning module, an AI system could integrate the new procedure text with real-time user responses and chat, extract key changes and explain what has changed since the last document update. Information could be generated in any format chosen by the user. The speed of content generation and the direct link to original sources reduce the time-to-competency dramatically (Gartner, 2022).

Critical Success Factors

Data Quality: RAG and agentic workflows depend on accurate, well-maintained repositories. If documents are poorly formatted or contradictory, AI-generated content may be confusing or erroneous.
Governance of Real-Time Updates: Giving AI the ability to create or update content on the fly requires robust approval processes. In regulated industries, any changes to compliance information bust be supported by adequate testing and governance.
User Adoption and Trust: Employees need to trust that AI-provided information is both valid and current. Clear indicators showing the source of the content (such as “This guidance was generated from document XYZ, last updated 2 days ago”) can build confidence.

The Path Forward for L&D

Moving beyond static “content at rest” represents a shift in mindset. Instead of developing monolithic courses and then “uploading” them, L&D teams become orchestrators of agile, data-driven learning ecosystems. This includes:

Investing in AI platforms (build or buy) that support RAG and real-time content generation.
Establishing metadata standards and a consistent taxonomy to ensure AI systems can easily parse, tag and retrieve relevant information.
Collaborating with subject matter experts to quickly vet or refine AI-generated updates.
Curating the repository of approved information, along with any guidelines for virtual agent response.

Conclusion

Agentic workflows, agentic metadata creation and real-time content generation address many of the limitations of static L&D assets. By leveraging retrieval-augmented generation and drawing on primary data rather than outdated secondary sources, learning departments can deliver timely, context-specific guidance. Although the transformation requires new governance models and platform investments, it promises more agile, meaningful and genuinely helpful learning experiences, enabling employees to solve problems as they arise rather than waiting for the next scheduled content refresh.

References

Bersin, J. (2020). The Big Reset: Making Sense of the Learning Technology Market.
Gartner (2022). Hype Cycle for Digital Workplace Technologies.
IBM (2022). Augmenting Organisational Knowledge with AI.
Microsoft (2021). AI-Powered Productivity: Agentic Workflows in the Workplace.