As artificial intelligence (AI) continues to evolve, one of the most exciting advancements is agentic AI. This type of AI can autonomously set and achieve its own goals, much like a self-driving car navigates roads and makes decisions without human intervention.
In learning and development (L&D), agentic AI has the potential to revolutionize the way organizations train, develop, and support their workforce.
Challenges with Generic AI Tools
Many organizations have experimented with generic AI tools for creating content, making learning recommendations, and providing performance support. However, these tools often fall short for several reasons.
- Mediocre output: Generic AI models usually lack the expertise needed for effective instructional design. Think of it like a recipe without a chef’s touch—it might have all the ingredients, but it won’t taste as good. This often results in learning content that is uninspiring or ineffective.
- Poor contextual understanding: AI models trained on broad datasets may not fully understand an organization’s unique learning needs. It’s like using a one-size-fits-all approach for clothing; it rarely fits anyone perfectly. This can lead to recommendations that are inaccurate or irrelevant.
- Data overload issues: When organizations input large amounts of unstructured data into AI tools, the results can be confusing, contradictory, or even incorrect. Imagine trying to find a specific book in a library where all the books are scattered randomly. Properly organizing and curating data is essential to avoid these problems.
The Data AI Needs
AI functions best when it processes well-organized, curated data that reflects accurate and up-to-date knowledge. The challenge arises when AI encounters complex formats such as SCORM packages (a standard for eLearning content) or heavily tabulated data (like spreadsheets with lots of rows and columns), which can be challenging to interpret.
Transforming learning materials into AI-readable formats is crucial for achieving meaningful results. Think of it like translating a book into a language the AI can understand.
Additionally, since AI models rely heavily on the data used to train them, they can produce misleading outputs if they process outdated or conflicting information. For example, suppose you provided a large language model (LLM) with all your organization’s training courses. Some of the old courses may contain information that conflicts with newer course data. Other times, you may also have intentionally false data in assessment questions. This could cause issues because AI cannot distinguish between true and false information. If the AI uses incorrect data to generate learning materials or recommendations, it could lead to misinformation and confusion among learners.
This is where humans play a key role.
Because an AI cannot understand the context or the truth of the data it processes, all of your data needs to go through a validation process. Validating your data ensures that the information the AI uses is accurate and reliable. It’s like proofreading a document to catch and correct errors before publishing it—this step helps maintain the quality and trustworthiness of the AI-generated outputs.
What Is Agentic AI?
Agentic AI (also called AI agents) goes beyond simple automation by following a structured process to achieve complex outcomes. Imagine you have a video transcript from a webinar and want to create a quiz based on it. A single prompt won’t be enough. This is because creating a meaningful quiz involves multiple steps such as cleaning up the transcript to remove unnecessary data, identifying key learning points, and then generating relevant questions by following specific rules.
This logical breakdown can be embedded into an AI agent so that users don’t have to manually prompt it each time—it will understand the goal and can autonomously manage subtasks on its own.
Key Components of Agentic AI
- Orchestrator: Think of this as a project manager in a business setting. The project manager oversees the entire process, breaking down the main task into smaller tasks and assigning them to the appropriate team members.
- LLM Call: At various stages, the system uses an LLM to process and refine data, ensuring accurate and meaningful outputs. The LLM is like a business analyst who gathers and processes information to create detailed reports.
- Synthesizer: This final component is like a quality assurance specialist who compiles all the processed information, ensuring that the generated content meets the intended objectives and is cohesive and well-structured.
Benefits of Agentic AI
Unlike traditional AI tools that require constant human prompting, agentic AI operates more independently. Agentic AI can:
- Receive defined goals and objectives and processes them accordingly.
- Break those tasks into sub-steps.
- Evaluate its own outputs.
- Iterate and improve its performance over time.
- Interact with other AI agents.
Agentic AI streamlines complex workflows. It’s like having a capable assistant who understands your goals, gets things done efficiently, and checks their own work.
How AI Agents Can Evaluate Themselves and Work with Other Agents
AI agents don’t work in isolation—they can evaluate their own performance and collaborate with other AI systems. AI agents are like members of a well-coordinated business team, each with a specific role to play. This team works together efficiently to achieve the desired outcome without needing constant supervision. Here’s how they can work together:
- Learning Content Agent: Imagine this agent as a researcher. It extracts topics from a video transcript, much like a researcher gathers information from various sources. It uses natural language processing—a technology that helps computers understand and process human language—to identify key points and themes.
- Curriculum Agent: This agent acts like a project planner. It takes the topics identified by the researcher (the learning content agent) and structures them into a learning module, organizing the content in a way that makes sense for learning.
- Assessment Agent: Think of this agent as a quality tester. It generates quizzes based on the learning module created by the project planner (the curriculum agent). The quality tester ensures that the quizzes align with the learning objectives and content.
- Feedback Agent: This agent is like a performance analyst. It reviews learner interactions with the quizzes and learning modules, analyzing how well learners are doing. It then refines the content over time, providing personalized feedback to improve the learning materials.
Benefits of Collaborative AI Agents
AI agents operate like a well-coordinated business team and provide many benefits.
- Efficiency: By dividing tasks among specialized agents, the overall process becomes more efficient, just like a business team where each member focuses on their area of expertise.
- Collaboration: AI agents can work together to achieve complex tasks. This collaborative approach allows different specialized agents to handle specific parts of a workflow.
- Accuracy: Each agent focuses on its specific role, ensuring higher accuracy in its outputs, similar to how a specialist in a business team ensures high-quality work in their domain.
- Self-Evaluation: AI agents can evaluate their own performance. This means they can monitor their outputs, check for accuracy, and adjust as needed.
- Continuous Improvement: Feedback loops allow agents to learn from their performance and improve over time.
Agentic AI in L&D Examples
In L&D, AI agents can play diverse roles that enhance both content delivery and personalized learning experiences:
- Learning Content Analyzers: AI processes large volumes of training materials, extracting key concepts and summarizing essential information. This helps in creating concise and relevant learning content.
- Instructional Design Assistants: AI helps create learning objectives, course outlines, and assessment questions aligned with best practices. This role is crucial in ensuring that instructional content is effective and engaging.
- Personalized Learning Coaches: AI recommends tailored learning paths based on an employee’s role, experience level, and career goals. This ensures that development is relevant and targeted, enhancing the learning experience.
- AI-Driven Course Recommendations and Real-Time Coaching: AI agents can suggest relevant training tailored to job roles and learning history. Real-time coaching agents, such as chatbots, provide on-demand knowledge and answer task-related queries, supporting employees in the moment.
- Automated Content Creation: Tools like Learning Content AIQ analyze training materials and generate video scripts, assessments, and even AI-generated video instructors. This streamlines content production and ensures consistency and quality.
How Agentic AI Can Work with the Wider Learning Technologies Environment
Agentic AI doesn’t replace existing L&D tools; it enhances them by integrating seamlessly into existing learning ecosystems.
- Integration with Learning Management Systems (LMS): Agentic AI can work within an LMS to automate course recommendations and track learner progress. This integration provides personalized learning experiences and improves overall learning outcomes.
- Complementing Content Creation Tools: AI agents can complement content creation tools by enabling the generation of adaptive learning materials tailored to organizational needs. This enhances the efficiency and effectiveness of content production.
- Enhancing Performance Support Systems: AI agents can deliver contextual insights within digital workflows, ensuring that learners receive timely and relevant guidance while performing their tasks. This capability supports employees in real-time and enhances their performance.
The Future of Agentic AI in L&D
As organizations increasingly embrace AI-driven learning solutions, agentic AI will play a key role in making training more adaptive, personalized, and efficient.
By thoughtfully implementing AI agents, L&D teams can shift their focus from content generation to higher-value strategic learning initiatives. This approach emphasizes a human-plus-AI collaboration, where AI handles repetitive and data-intensive tasks, freeing up humans to focus on the more creative, strategic, and interpersonal aspects of their work.
Are you ready to explore the possibilities of agentic AI in your L&D strategy? Reach out today to learn how AI-powered agents like Learning Content AIQ can enhance your learning ecosystem.
For More Resources on AI in Learning and Development
Human+AI Quick Start Guide for L&D
AI is a game-changer, but its true power lies in how you use it. The Human+AI Quick Start Guide is your resource for maximizing AI’s full potential, from identifying key problems to mastering the art of prompt engineering for optimal results.
AI in Learning and Development: A Comprehensive Guide
How organizations are using AI to create superior learning experiences that empower employees to achieve organizational goals.
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