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.
In recent months, agentic AI has made significant strides, with enhanced reasoning and multi-agent coordination now enabling dynamic, personalized learning experiences that respond to real-time learner needs and shifting industry trends.
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.
- Inconsistent multimedia outputs: Generic AI tools now handle text, images, and audio, but without proper tuning, they may produce inconsistent or misaligned multimedia learning content, reducing learner engagement. Curated data and specialised workflows are essential.
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.
Building Trust through Transparency and Safe AI Use
To create effective learning experiences, agentic AI needs well-organised, accurate data, but it’s equally important to ensure AI is used responsibly. As of August 2025, L&D teams are now prioritising transparency, brand safety, and proper AI use to build trust. Transparent AI processes, such as clear audit logs, show exactly how AI generates content or recommendations, giving you confidence in its outputs.
To protect your brand, human oversight ensures AI aligns with your organization’s values, avoiding errors or inappropriate content. Training your team to use AI correctly, with clear guidelines on prompting and data input, ensures it delivers as intended, supporting consistent, high-quality learning. These steps create a reliable, safe AI environment, empowering your people to use it effectively while safeguarding your reputation.
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.
Building on these foundations, recent advancements have significantly enhanced agentic AI’s capabilities. By mid-2025, agents have begun leveraging chain-of-thought reasoning to tackle more sophisticated L&D tasks, such as designing adaptive assessments that align with specific learning goals. Additionally, multimodal processing now enables agents to create rich, interactive learning experiences, combining text, visuals, and audio in formats tailored to individual learner preferences. These innovations are driving deeper engagement and more effective outcomes across digital learning environments.
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 for concise, relevant content.
- Instructional Design Assistants: AI creates learning objectives, course outlines, and assessment questions aligned with best practices, ensuring effective and engaging content.
- Personalized Learning Coaches: AI recommends tailored learning paths based on an employee’s role, experience, and career goals, enhancing development relevance.
- Virtual Teaching Assistants: Tools like DeepSeek’s R1 act as 24/7 teaching assistants in higher education, autonomously managing course development and delivery.
- Real-Time Analytics Agents: AI provides real-time analytics on learner performance, dynamically adjusting courses to optimize outcomes.
- Automated HR Training Plans: AI agents design personalized employee training plans, aligning with job roles and career goals to support workforce development.
How Agentic AI Works with Wider Learning Technologies
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.
Modern agentic AI systems are designed to be scalable and interoperable, using cloud-based architectures and standardised protocols to integrate with a wide range of LMS platforms. Their modular design allows L&D teams to upgrade capabilities without disrupting existing workflows, making it easier to adapt to evolving technologies. This flexibility supports large organizations in delivering consistent, personalized learning experiences across diverse teams and geographies.
The Future of Agentic AI in L&D
Agentic AI adoption in learning and development is rapidly maturing, with organizations moving beyond pilot projects to implement strategic, scalable programs. Cross-functional teams are increasingly integrating AI agents with enterprise systems to deliver end-to-end learning solutions that enhance personalization and operational efficiency.
Open-source models such as DeepSeek’s R1 are helping reduce costs and accelerate innovation, making agentic AI more accessible for building adaptive e-learning ecosystems. As these technologies become more embedded in organizational learning strategies, L&D teams are shifting focus toward higher-value initiatives, leveraging human-AI collaboration to drive transformative learning outcomes.
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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