Autonomous behavioral intelligence for early childhood classrooms
Monty is an AI agent that watches a Montessori classroom through teacher observation notes and builds a living, growing knowledge base of what it learns. Every note that arrives triggers a full reassessment: the agent reads the child's history, extracts behavioral patterns, updates a shared knowledge graph, and decides whether it needs to go find research papers to fill gaps in its understanding.
The knowledge graph is the core. Behavioral patterns like "frustration after error correction" or "peer-recruiting during sensory play" emerge as nodes that grow stronger with each supporting observation. Edges form between them as the agent discovers relationships. When enough children exhibit a pattern, or when something surprising appears, a curiosity gate fires and the agent autonomously searches OpenAlex for peer-reviewed literature to ground its observations in science.
Everything the agent knows lives in markdown files on disk. The database is a derived index that can be rebuilt at any time from those files. This means the agent's knowledge is human-readable, version-controlled, and auditable down to a single observation.
This dashboard makes the agent's work visible in real time. You can watch behavioral patterns emerge as the force-directed knowledge graph grows on the Live page, read the actual markdown the agent writes on the Wiki page, and trace every processing step on the Console.
Five synthetic children attend this classroom, each with a distinct temperament and stress response profile. A persona engine generates observation notes through an LLM, producing realistic Montessori-style documentation that varies with each child's current regulatory state.
Operators can adjust each child's regulatory slider from calm to dysregulated, change their stress response type, inject specific scenarios (a calm morning, an escalation, an emergency), and force peer interactions between children. The agent processes whatever comes through the pipeline and responds accordingly.
Early childhood teachers spend an estimated 8-12 hours per week on documentation and pattern recognition across their classroom. Monty eliminates the synthesis layer: reviewing notes, updating developmental records, and connecting observations to research. That returns 4-6 hours per teacher per week to direct instruction.
This scales to organizations. A single classroom has 25 children. A school district has thousands. The same architecture maintains a unified, queryable knowledge base across all of them. Any authorized educator can ask "which children showed regression this month?" and get an answer grounded in aggregated observations, linked behavioral patterns, and cited literature.
The compound effect is the hardest to quantify and the most valuable. After a year of operation, the knowledge graph contains thousands of observations, hundreds of behavioral patterns, and dozens of research links. That institutional knowledge asset has no equivalent in a paper-based system.