MOMENTS RESEARCH · 01
Self-Evolving Agents
How agents can learn through human interaction, carrying useful feedback into better memory, decisions and workflows.
IN RESEARCH
Active research agenda
Questions guiding our workROLE IN THE MOMENTS LOOP
The evolution layer of the Moments Loop: experience becomes governed changes to memory, strategy, tools and workflow.
CONTEXT
Text · Image · Audio · Video · Behavior
- 01Perceive
- 02Model
- 03Plan & Act
- 04Assess
- 05Evolve
EVIDENCE
Outcomes · Capability · Uncertainty · Safety
OPEN QUESTIONS
What we are trying to understand.
- 01
How can useful experience persist without losing consent, provenance or context?
- 02
Which changes produce measurable capability gains rather than short-term task fitting?
- 03
How should an agent detect regression and return to a known-good state?
RESEARCH BOUNDARIES
How the work stays grounded.
- Improvement is evaluated against explicit objectives.
- Changes remain observable, testable and reversible.
- Human agency and permission boundaries remain part of the system design.
PUBLIC RECORD
Methods, results and lessons from the work.
Our research notes will explain what we explored, what the evidence shows and what remains open.
No publications yet
Explore the research questions above while we prepare work to share.