Beyond benchmarks for AI.
Beyond tests for humans.
openLesson optimizes learning efficiency for humans and agentic systems, measuring learning-to-conversion and increasing the ROI of every learning intervention across real product workflows.
As humans and agents work inside real products, openLesson turns that activity into efficiency signals: evidence that judgment converts into outcomes, not just that a step was completed or a benchmark was passed.
Our focus is
Catch thinking in the flow of work. Optimize for understanding that converts.
We drive conversion through
Make learning show up downstream: adoption, deployment, and real use.
Our method is
Strengthen how people and agents think by engineering interruptions.
Trace Interruption Model • Proof-of-Work API • Think Aloud Protocol • ILE • Agentic Learning Environment
A learning world model — not linear analytics.
openLesson optimizes learning efficiency for humans and agents by progressively building a learning world model, adapting interfaces in real time, and using an interruption prediction model that breaks the linearity problem of classic analytics.
Integrated directly into your existing workflows and tools, it turns everyday product activity into precise efficiency signals — measuring true learning-to-outcome conversion and dramatically increasing the ROI of every user learning intervention.
Products for humans and agents. One Workspace.
Start with a Workspace, powered by the Trace Interruption Model across every product. Workspaces accumulate learning signals over time. Proof-of-Work API scores humans and agents from artifacts. Think Aloud Protocol and ILE optimize human learning-to-conversion. Agentic Learning Environment evolves agent skill.md files as agents learn, because they are not born with skills.
Workspace
The container for a learning goal. Set the skill or scenario, add context (docs, recordings, tool traces), and run every product against the same live picture of the work.
- —One place for goals, proof of work, and scores
- —Humans and agents work inside the same workspace
Trace Interruption Model
The shared brain behind every product. It watches how people and agents think during real work, then steps in with a targeted question instead of waiting for the next chat reply.
- —Same model across Proof-of-Work API, TAP, ILE, and ALE
- —Grounded in your workflow, skills, and conversion goals
For humans
Proof-of-Work API
Send recordings, write-ups, or session artifacts from human workflows. Get readiness scores and a clear gap list before promotion or sign-off.
- —Scores live cognition and written proof of work
- —Fits onboarding, certification, and QA gates
Think Aloud Protocol
Send someone a link. They talk through their thinking while they work. You get a scored report on what they actually understand.
- —Captures live reasoning, not polished write-ups
- —Shareable links per workspace or practice block
Integrated Learning Environment
Where people practice after gaps show up. Guided scenarios and coaching until scores improve.
- —Practice targets the gaps that were found
- —Progress tracked in the same workspace
For agents
Proof-of-Work API
Pipe tool traces and run artifacts from agent workflows. Get readiness scores and gap lists before deploy or promotion.
- —Scores agent runs from real tool use
- —Fits CI, eval harnesses, and deploy gates
Agentic Learning Environment
Agents are not born with skills. ALE evolves skill.md files as agents learn from real workspace runs, closing gaps until Proof-of-Work API scores say the skill is ready to deploy.
- —Skill file evolution driven by proof of work, not one-shot prompt edits
- —Sandbox practice on real scenarios until the agent earns the skill
Coming soon
Optimize learning. Maximize conversion.
Interrupt, score, and improve on the same workspace.
TIM applies across every product, breaking turn-based interactions and probing a closer reasoning layer. Pipe tool traces into Proof-of-Work API for human and agentic efficiency scoring. Issue Think Aloud Protocol URLs for live human cognition. Route humans into the ILE to improve. Soon, ALE will evolve agent skill.md files as agents learn from real runs until learning efficiency clears the bar.
Optimize learning efficiency, build judgment, and tie every signal to learning-to-conversion: deploy gates, adoption metrics, promotion, and compliance at every step.
Workspace → Integrate → Convert
Create a Workspace
Define the skill, scenario, or conversion goal. Enrich it with documents, tool traces, screen shares, video, or any proof of work from humans or agents.
Integrate to your product or workflow
Augment the UIs, internal tools, and agent dev processes you already run. Wire in Proof-of-Work API, Think Aloud links, and practice flows where work happens, not in a separate training layer.
Measure learning efficiency
Proof-of-Work API and Think Aloud Protocol score humans and agents on conversion readiness and gap density, not vanity completion or benchmark pass rates.
Close gaps, raise ROI
Route humans into the ILE for targeted practice. Use ALE to evolve agent skill.md files until learning efficiency clears the deploy and adoption bar.
Stop measuring completion. Start measuring learning efficiency.
Raise the ROI of learning for agent deployments: fewer failed rollouts, faster time-to-production.
Optimize human learning-to-conversion: adoption, activation, and workflow mastery, not click-through training.
Detect reasoning gaps before they surface in client work, incidents, or bad deploys.
Separate genuine human thinking from AI-fed interview polish and take-home fluff.
Turn real product activity into learning efficiency signals tied to deploy gates, promotion, and compliance.
Close gaps with ILE practice so efficiency gains compound, not one-off completion badges.
Increase the ROI of learning for humans and agents.
Whether you are gating an agent deployment or confirming a team learned a new tool, openLesson optimizes learning-to-conversion with efficiency signals and helps humans close the gaps when they do not.