Autonomous AI and (the Possible Future of AI in Schools): Welcome MoltBot and Autonomous AI Agents

Over the last week, I have played extensively with Clawdbot, now aka Moltbot. Seeing AI that is THIS autonomous, and the work it has done has been mind-blowing. I have built numerous applications and have several tasks and projects that will be delivered to me daily. From research to applications ready to be delivered on my GitHub. Wow! In my opinion, this is where AI comes alive and goes outside the box.

For education, I believe this technology has several major ramifications for school and district leadership as well as teaching and learning. From what I can gather, this technology can cooperate not only with itself but also with multiple (many) subagents to monitor and solve problems. In only a day, I was able to build and test several applications that I could theoretically deploy to a school, send myself research, and a conduct daily updates on whatever topics I wanted. I also want to preface that there is definitely hype with this type of technology, but as you see in the video I provided below, there are many applications and ramifications of this technology.

This is where my previous posts from last year on Agentic AI comes to fruition (and this one). Now, with a tool like Moltbot, there is an opportunity to develop software extremely quickly and deploy. But, also using agents to monitor various systems and student progress within schools. This is where I believe there is a huge opportunity with this technology. While this is newer technology, privacy concerns are valid, but I believe there are ways to build environments that are safe and secure (as I will discuss at the end of this post).

Let’s quickly discuss some applications for teachers and school leaders:

Just think about the following: an autonomous AI agent can complete the following overnight. Again, let me emphasis that once the task is automated, you can have the AI working overnight for you or while you are busy teaching a class, commuting, or sleeping. Now, let’s first see this from a teachers context of how this can work.

• Teacher and planning workflow support: draft lesson-plan first drafts aligned to standards, create exit tickets, build differentiated practice sets, and summarize class misconceptions for reteaching.
• Student learning support: generate study guides, scaffold explanations, create practice with feedback loops, and help students plan assignments with step-by-step checklists (all live while students work with YOU and your AI agents (the tutors) and you will receive the reports in the morning and lessons ready to go for the day).
• Family communication support: draft newsletters, translate messages for multilingual families, and prepare FAQ responses, always with staff review before sending. And, also, this could be schedule continuously each week with injecting several prompts and documents to make these communications up to date and relevant.

Now, let’s see example of how this bot can support the workflow of school and district leaders. Again, I want to emphasis is that various SKILLs can be developed to where much of this can be done by itself through agents and subagents by providing several smaller prompts and context when leaders are asked. Again, let me emphasis, this can likely be done almost entirely by itself.

• Instant progress visibility: pull assessment results from multiple sources, flag missing data, and generate daily “who needs support” lists by grade/class/standard.
• Daily reporting: auto-generate principal digests (attendance, behavior, grades, intervention updates) and weekly board-ready snapshots with trends.
• Data entry and cleaning: import rosters, standardize spreadsheet formats, catch duplicates/errors, and create clean uploads for SIS/LMS systems (with human review).
• Instructional coaching: synthesize observation notes into themes, generate coaching questions tied to teacher goals, and curate targeted resources for the next cycle.
• Administrator operations: draft agendas and meeting summaries, generate action-item trackers, optimize schedules, and streamline routine communications.
• Student support systems: help counselors track scholarship timelines, build college search lists, and organize application tasks (with clear guidance and guardrails).
• Early warning + MTSS support: identify students crossing risk thresholds (attendance dips, missing assignments, behavior patterns), draft Tier 1/2 action lists, and prepare parent contact scripts.
• Intervention tracking: monitor progress monitoring data weekly, summarize what’s working, and prompt teams when it’s time to adjust supports.
• Staff workload relief: draft agendas, meeting minutes, action trackers, and follow-up emails automatically from notes or recordings (approved before sending).
• Budget monitoring: daily or weekly budget-to-actual updates, alerts when categories run hot, and auto-prepared explanations for variances.
• Purchasing workflows: draft PO justifications, check vendor quotes against guidelines, and create approval packets with required documentation.
• Grant compliance: track deadlines, compile evidence artifacts, draft reports, and flag missing components before submissions.
• Audit readiness: maintain organized “audit folders” with labeled receipts, meeting notes, and approvals—prompting staff when something is missing.
• cheduling optimization: propose master schedule drafts, identify conflicts, and generate alternative scenarios based on constraints.
• Sub coverage: monitor absences, recommend coverage plans, and prepare communication templates to staff.
• Facilities + operations: track work orders, summarize recurring issues, and produce weekly maintenance priorities.

Right now, I believe this type of AI can be easiest implemented for leadership within schools and districts. where time disappears into spreadsheets, emails, and meetings. Imagine waking up to a dashboard summary: which students slipped in attendance yesterday, which interventions need follow-up, which classes showed growth, and which sites have data anomalies that require a quick check. Done well, this doesn’t replace professional judgment; it removes the fog so leaders can respond faster and more fairly.

Moving Forward

Currently, with time and lots of tokens and API configurations, much of what I discussed above can be done with Moltbot. While it is incredibly expensive due to the number of tokens you’d spend, there is promise in the next few years if schools build out local AI infrastructure to support this type of technology in addition to newer safe guards that can be put in to support the implementation around this technology (especially if the AI is locally controlled).

For myself, I plan to build out my local infrastructure at home to run this type of technology locally. I do see this eventually landing in schools and universities, but not for a few years until the technology matures and becomes safer. I am excited about this technology, but also scared at its ramifications when massively adopted. I do see a world where we, ourselves, will not be doing most of the communicating. Rather, AI agents on our behalf will. Additionally, I see a world where much of the internet is a ghost town because AI agents will be doing all of the searching for us instead of human eye balls on the internet. Last, I see this technology impacting jobs in the future. With this in mind, how do we prepare our students for an AI Agent driven world? This is the question I pose to you all as this is where AI becomes real and will greatly impact our world.

AI is officially outside the box and actually decent at the tasks you ask it to do.

What will be next?