I’ve been tracking AI tools on Educators Technology since 2011, and nothing I’ve covered in that time has moved as fast as what’s happening with agentic AI right now. A few months ago, most teachers were still getting comfortable with ChatGPT prompts. Now we’re looking at AI systems that can log into your learning management system, complete assignments, and submit work on behalf of students, all without a single human keystroke after the initial setup.
I put this guide together after delivering a recent presentation on the topic and spending weeks reading everything I could find from researchers, practitioners, and the developers building these systems. If you teach in any capacity, this is something you need to understand. Not because agentic AI is coming. It’s already here.
What Is Agentic AI?
Agentic AI refers to AI systems that can pursue complex, multi-step goals with minimal human intervention. Traditional chatbots generate text and wait for your next prompt. Agentic AI is different. These systems reason through problems, make plans, browse the web, execute code, manage files, and adjust their approach based on what they observe. You set the objective and the AI handles everything else.
The core architecture behind most of these systems is called the ReAct framework (Reason + Act). The agent receives a goal, reasons about what to do, takes an action, observes the result, and reasons again about the next step. This loop continues until the task is complete. Think of it as the difference between asking someone a question and hiring someone to do the entire project.
The Einstein Story
In February 2026, a 22-year-old developer named Advait Paliwal launched Einstein, an agentic AI tool built on the open-source framework OpenClaw. Einstein could autonomously log into Canvas, watch recorded lectures, complete assignments, and submit homework. Paliwal deliberately branded it as a cheating tool to provoke outrage and force a conversation about how unprepared higher education was for this moment.
Within 48 hours, over 124,000 people visited the site. Within a week, cease-and-desist letters from Hebrew University of Jerusalem and Instructure (the company behind Canvas) took it down. But here’s the uncomfortable part: the underlying technology is open-source and has over 302,000 stars on GitHub. You can’t send a cease-and-desist to a code repository. Anyone with basic technical skills can rebuild what Paliwal built. The genie is out.
Agentic AI Tools Educators Should Know
The commercial tool space has exploded in early 2026. Claude Cowork from Anthropic can produce actual files, read and write to local folders, and with its Chrome extension can navigate websites, click buttons, and fill forms autonomously. Over 50 universities have already adopted Claude for Education with campus-wide access.
ChatGPT Atlas from OpenAI comes with a full Chromium-based browser and agent mode, free for all users. It can navigate websites, make purchases, and complete multi-step tasks on its own. Perplexity Computer, launched in February 2026, orchestrates 19 AI models working in parallel. You give it one instruction and it breaks the task into subtasks, assigns each to a specialist model, and runs them simultaneously for hours.
On the open-source side, OpenClaw is the fastest-growing AI agent framework on GitHub. It can run shell commands, control browsers, manage calendars, and send emails. Its Canvas skill set allows it to navigate any LMS interface. It runs on a cloud server for about $6 a month. This is what powered Einstein.
Why Technical Safeguards Are Failing
If your institution relies on lockdown browsers, multi-factor authentication, or API restrictions to block AI misuse, those defenses are already broken. Agentic AI runs on remote cloud servers, so the student’s device is never involved. Lockdown browsers are irrelevant. Multi-factor authentication only requires one human action to approve the initial login; after that, the agent maintains the session. And agents never touch the API. They navigate Canvas through the graphical interface exactly the way a human would. Detection tools can’t distinguish agent traffic from a real student because these browsers use dynamic IP proxy rotation and hardware spoofing.
What Actually Works: Assessment Redesign
If the technical walls have crumbled, the response has to be pedagogical. Multiple researchers and practitioners are converging on the same set of strategies, and they all share a common thread: they put the human back at the center of the assessment.
Oral defenses and Socratic questioning require students to verbally explain and defend their work in real time. You can’t outsource that to an agent. Process-based evaluation shifts the grade from the final product to iterative drafts, peer reviews, reflections, and version histories. Break assignments into stages: proposal, outline, draft, final. Video logs, where students record themselves working through problems, preserve the embodied reality of thinking in ways text simply cannot.
In-class demonstrations and structured debates demand spontaneous, situated cognition that no agent can replicate. And assignments that require students to connect content to their own lived experience, field observations, or local data create a personal anchor that makes outsourcing nearly impossible.
Tips for Getting Started
- Audit your current assignments. Ask yourself: could an AI agent complete this without the student being involved? If yes, it’s time to redesign.
- Start with one assessment. You don’t need to overhaul everything at once. Pick one major assignment and add a process component, a draft stage, a reflection, or an oral defense.
- Try the tools yourself. Log into Claude Cowork or ChatGPT Atlas and give them a task you’d assign your students. See what they produce. That firsthand experience is the best teacher.
- Talk to your students about it. They already know these tools exist. An open classroom conversation about AI, learning, and intellectual growth goes a long way.
- Connect with colleagues. Join online communities where educators are sharing strategies for teaching in the age of agentic AI. You’ll find practical ideas and reassurance that you’re not navigating this alone.
- Focus on what AI can’t do. Oral explanation, personal reflection, real-time debate, and connecting ideas to lived experience are all things that remain deeply human.
Universities like Michigan, Northeastern, Ithaca College, and Ohio State are already running pilots with agentic AI in various capacities, from virtual teaching assistants to student advising systems. The institutional response is uneven, but the direction is clear: agentic AI is becoming part of the infrastructure of higher education whether we’re ready or not.
The conversation about agentic AI in education is just getting started, and the pace of development shows no signs of slowing down. I’ll keep covering the tools, the research, and the strategies here on Educators Technology. If you’ve already started adapting your courses, I’d love to hear what’s working for you.
The full guide with references can be found here in PDF format.
