Education has moved past experimenting with AI features. The next leap is agentic AI—autonomous systems that connect tools, streamline workflows, and drive measurable outcomes across learning environments.
GUEST COLUMN | by Rishi Raj Gera
MOTION BD
Since the public release of ChatGPT in November 2022, generative AI has rapidly entered mainstream education conversations. From automated grading and personalized learning to content tagging and support bots, GenAI tools have surfaced across the Pre-K through the workforce continuum. But for many institutions and learning platforms, adoption has felt fragmented and tactical. The promise is clear, but outcomes are still maturing.
‘GenAI tools have surfaced across the Pre-K through the workforce continuum. But for many institutions and learning platforms, adoption has felt fragmented…’
Stakeholders are now asking how to move beyond early use cases into scalable, outcome-aligned implementations that integrate across real workflows.
We are entering a new phase. The question is no longer whether AI can support education. It is how we shift from assistive tools to autonomous systems that operate across environments, reduce drag, and support outcomes with greater clarity and consistency.
This shift is being driven by the rise of AI agents.
From Tools to Agents
AI agents differ from traditional AI features embedded in applications. They are goal-driven systems that can observe, decide, and act across multiple environments. They work with system APIs, maintain context, and communicate with other agents to complete multi-step processes.
Instead of generating a single output, an AI agent can manage a complete workflow. For example, it may identify a struggling student in a learning platform, generate a review plan, notify the instructor, and track the student’s progress across future assessments.
Why Traditional AI Falls Short
AI has already been tried in many education contexts, but efforts often stall due to poor workflow integration, inconsistent output, and lack of system coordination.
Examples include:
- Grammarly assists students, but instructors still reconcile feedback manually with rubrics and grading platforms
- Turnitin flags plagiarism, but faculty must interpret results and take action manually
- Autograding tools struggle with unexpected formats and require human review
- BI tools like Power BI respond to report requests, rather than providing on-demand insights
These tools function in isolation, not as part of an orchestrated system. This leads to inefficiency, duplication, and mistrust in automation.
How AI Agents Help
AI agents bring continuity and reasoning. They can manage tasks involving decision-making, follow-up, and escalation. Most importantly, they operate across systems, accessing and updating data in LMS, SIS, CRM, and CMS platforms.
In academic support, an AI agent can:
- Scan LMS data to flag students at risk
- Check attendance or enrollment patterns
- Generate and send messages aligned to faculty policies
- Log interventions in student support systems
- Trigger follow-ups if students do not respond
This is not a standalone tool. It is a digital teammate with responsibilities and defined boundaries.
Enabling Coordination: MCP and A2A
For agents to work at scale, they need structured communication. This is enabled by Message Communication Protocol (MCP) and Agent-to-Agent (A2A) communication.
- MCP defines message formats and rules for intent, context, and expected responses. It provides the language and structure that supports secure, scalable communication.
- A2A describes how agents interact: passing tasks, sharing memory, and synchronizing results. MCP powers the mechanism; A2A defines the behavior.
Example:
- A content ingestion agent detects new uploads
- It passes them to a standards-alignment agent, which tags them to CASE or custom frameworks
- A third agent checks accessibility metadata and flags issues
- Each step includes validation and fallback actions if earlier agents fail
This allows agents to work in coordination, not in isolation.
Figure: AI agents as orchestrators between educational platforms and integrated tools, enabling scalable, coordinated workflows for teachers and students.
Where Agents Are Already Working
AI is embedded in many education tools, but often lacks interoperability or context. Agentic AI links them into intelligent workflows that save time and reduce bottlenecks.
Examples include:
- Grammarly paired with an agent may align feedback with rubrics, integrates it into LMS workflows, and tracks revision cycles
- Turnitin with an agent may evaluate severity, recommends actions, and logs outcomes
- Autograding tools enhanced by agents may detect patterns of misconceptions and recommend adaptive remediation
- BI dashboards with agents may auto-generate filtered views and flag anomalies
- Helpdesk platforms with agents may classify tickets, identify urgency, and coordinate resolution
More use cases include:
- In K–12, an agent may monitor pacing in Schoology, assigns leveled readers, and notifies guardians
- In Higher Ed, an agent may audit Banner data to flag missing credits and suggest alternate pathways
- For Publishers, an agent may check ADA compliance, transcript accuracy, and content readiness
- For Localization, an agent may manage string extraction, translation memory use, and re-import
- For Assessment, an agent may detect distractor flaws and readability gaps
- For Curriculum, an agent may review NGSS alignment and drafts tasks
- For Accessibility, an agent may perform WCAG 2.2 scans and prioritizes remediation
- For Workforce Learning, an agent may match roles to certifications and alerts HR
- For Career Services, an agent may optimize resumes based on job listings and tracks outcomes
- For Financial Aid, an agent may check FAFSA status, eligibility, and sends reminders
Where to Start
For organizations ready to explore agentic AI, begin with repeatable, low-risk workflows such as:
- Tagging content to frameworks like NGSS or Common Core
- Managing onboarding checklists across departments
- Auto-generating nudges for at-risk students
- Coordinating peer and instructor feedback cycles
- Escalating support tickets through agent-based triage
These can start with a single agent per role and expand as comfort and capability grow.
Looking Ahead
The education sector does not need more pilots. It needs operational models that function across systems with measurable results. AI agents provide a path toward this future.
They are not a silver bullet, but they are a step forward—one that enables secure, adaptive, and goal-aligned automation. With the right protocols and implementation support, agents can reduce friction and help educators focus on what matters most.
It is time to stop treating AI like a feature. It is time to design it as a system of work.
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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. Connect with Rishi on LinkedIn.
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