Outcomes, corrections, successful runs, failures, and changing constraints become evidence.
The autonomous learning system for deployed agents.
Your best agent should be the one that has worked with you the longest.
The missing loop
Work teaches people.
It should teach their agents too.
Your standards, tools, constraints, and decisions evolve through use. Deployed agents do not automatically convert that experience into durable improvement.
Corrections disappear into transcripts. Successful strategies remain isolated. The next run starts without enough of what the last one learned.
qova ledger
day 34 on this repo
- aug 22 rule migrations are append-only verified
- aug 23 skill seed-data generation verified
- aug 24 hook guard shipped migration files testing verified
- aug 25 memory stripe test-mode in local dev verified
- aug 26 rule grep before adding helpers verified
214 improvements shipped · 71 collecting evidence · 3 reverted
The learning system
Your agents should evolve every day.
Qova learns from the environment in which work actually happens. It builds and continually refines an operational model of what good means there—then uses that model to improve the agent system around the foundation model.
Evaluation adapts to the person and the environment the agent serves.
Qova directs the improvement to the appropriate layer of the deployed system.
The result informs both the next agent state and the next learning cycle.
What makes Qova different
The learning function should not remain static while your world changes.
Most systems improve an agent against a given objective. Qova is building the system that learns the changing structure of success.
The operating loop
The agent changes.
Context, memory, instructions, skills, tools, workflows, and harness logic can adapt from deployed experience.
The learning loop
The way it changes changes.
Evaluation, reward logic, and the policy deciding how future improvements are found can adapt as the environment moves.
The technical position
No single technique is the product.
Qova uses reinforcement learning as one technique inside a broader autonomous learning system.
Our work draws on continual reinforcement learning, adaptive evaluation, non-stationary objectives, and meta-optimization. Each is applied to the layer where it can create durable improvement. We do not treat model-weight updates as the only legitimate form of learning.
- Continual adaptation
- Learning does not end when deployment begins.
- Adaptive evaluation
- The definition of good stays fitted to the environment.
- Meta-optimization
- The process that finds improvements can itself improve.
Intelligence and ownership
Your work should make your agents better—not everyone else’s.
Customer-specific work, context, and proprietary intelligence remain isolated. Qova can learn from transferable, non-content metadata signals without pooling the intelligence that belongs to a person or organization.
Your world
work
judgement
methods
context
Private learning boundary
Non-content learning signals
From one agent to an organization
Learning changes scale.
Available now
Personal learning
A private learning loop around the deployed agents of an individual.
Available now
Collective learning
Useful patterns improve how agents work across a team while remaining inside organizational boundaries.
Long-term direction
Parametric consolidation
Organization-specific datasets, evaluations, and learning curves become the substrate for organization-owned model adaptation.
Qova Labs research
The agent improves.
Then the way it improves improves.
Repetition alone is not recursion. Recursion begins when the machinery responsible for future improvement can itself change.
Read the case for personal RSI