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For years now, the promise of AI in education has centered around efficiency–grading faster, recommending better content, or predicting where a student might struggle.
But at a moment when learners face disconnection, systems are strained, and expectations for personalization are growing, task automation feels…insufficient.
What if we started thinking less about what AI can do and more about how it can relate?
That’s where agentic AI comes in. These systems don’t just answer questions. They recognize emotion, learn from context, and respond in ways that feel more thoughtful than transactional. Less machine, more mentor.
So, what’s the problem with what we have now?
It’s not that existing AI tools are bad. They’re just incomplete.
Here’s where traditional AI systems tend to fall short:
- NLP fine-tuning
Improves the form of communication but doesn’t understand intent or depth. - Feedback loops
Built to correct errors, not guide growth. - Static knowledge bases
Easy to search but often outdated or contextually off. - Ethics and accessibility policies
Written down but rarely embedded in daily workflows. - Multilingual expansion
Translates words, not nuance or meaning across cultures.
These systems might help learners stay afloat. They don’t help them go deeper.
What would a more intelligent system look like?
It wouldn’t just deliver facts or correct mistakes. A truly intelligent learning system would:
- Understand when a student is confused or disengaged
- Ask guiding questions instead of giving quick answers
- Retrieve current, relevant knowledge instead of relying on a static script
- Honor a learner’s pace, background, and context
- Operate with ethical boundaries and accessibility in mind–not as an add-on, but as a foundation
In short, it would feel less like a tool and more like a companion. That may sound idealistic, but maybe idealism is what we need.
The tools that might get us there
There’s no shortage of frameworks being built right now–some for developers, others for educators and designers. They’re not perfect. But they’re good places to start.
| Framework | Type | Use |
| LangChain | Code | Modular agent workflows, RAG pipelines |
| Auto-GPT | Code | Task execution with memory and recursion |
| CrewAI | Code | Multi-agent orchestration |
| Spade | Code | Agent messaging and task scheduling |
| Zapier + OpenAI | No-code | Automated workflows with language models |
| Flowise AI | No-code | Visual builder for agent chains |
| Power Automate AI | Low-code | AI in business process automation |
| Bubble + OpenAI | No-code | Build custom web apps with LLMs |
These tools are modular, experimental, and still evolving. But they open a door to building systems that learn and adjust–without needing a PhD in AI to use them.
A better system starts with a better architecture
Here’s one way to think about an intelligent system’s structure:
Learning experience layer
- Where students interact, ask questions, get feedback
- Ideally supports multilingual input, emotional cues, and accessible design
Agentic AI core
- The “thinking” layer that plans, remembers, retrieves, and reasons
- Coordinates multiple agents (e.g., retrieval, planning, feedback, sentiment)
Enterprise systems layer
- Connects with existing infrastructure: SIS, LMS, content repositories, analytics systems
This isn’t futuristic. It’s already possible to prototype parts of this model with today’s tools, especially in contained or pilot environments.
So, what would it actually do for people?
For students:
- Offer guidance in moments of uncertainty
- Help pace learning, not just accelerate it
- Present relevant content, not just more content
For teachers:
- Offer insight into where learners are emotionally and cognitively
- Surface patterns or blind spots without extra grading load
For administrators:
- Enable guardrails around AI behavior
- Support personalization at scale without losing oversight
None of this replaces people. It just gives them better support systems.
Final thoughts: Less control panel, more compass
There’s something timely about rethinking what we mean by intelligence in our learning systems.
It’s not just about logic or retrieval speed. It’s about how systems make learners feel–and whether those systems help learners grow, question, and persist.
Agentic AI is one way to design with those goals in mind. It’s not the only way. But it’s a start.
And right now, a thoughtful start might be exactly what we need.
Rishi Raj Gera, Magic EdTech
Rishi Raj Gera is Chief Solutions Officer at Magic EdTech. Rishi brings over two decades of experience in designing digital learning systems that sit at the intersection of accessibility, personalization, and emerging technology. His work is driven by a consistent focus on building educational systems that adapt to individual learner needs while maintaining ethical boundaries and equity in design. Rishi continues to advocate for learning environments that are as human-aware as they are data-smart, especially in a time when technology is shaping how students engage with knowledge and one another.
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