Context that persists
Relevant history can travel between tasks. The research question is what to retain, what to retrieve and when to let a memory expire.
Quantum Intelligence Protocol
Learning that
carries forward.
QIP explores how autonomous agents can act with boundaries, remember what matters, and bring useful lessons into the next task.
Read the full thesisA loop, with people in it.
Choose a point in the loop to explore how a task moves from intention to a lesson. This is an illustrative architecture, not a live agent running in your browser.
QIPlearning carries forward
Explore the Compound Autonomy Loop
A task begins
An agent turns an intention into a specific task, with a human-defined boundary.
Illustrative example
Prepare a launch brief. Ask a person before contacting a customer.
1 / 4
- A task begins. An agent turns an intention into a specific task, with a human-defined boundary.
- A heartbeat picks it up. A recurring check finds ready work and brings the next action into focus.
- Memory informs the work. Relevant context and earlier lessons guide the next attempt.
- The lesson carries forward. A reviewed lesson is stored so another task can benefit from what happened.
More useful autonomy.
More deliberate boundaries.
Human authority
People define objectives and permissions. An agent should ask when a decision falls outside the authority it has been given.
Inspectable decisions
A useful system makes it possible to understand the context, action and outcome behind a lesson.
Learning we can test
Improvement is a hypothesis to evaluate. Repeated tasks, recorded failures and human review help distinguish progress from repetition.
The next question is practical.
What happens when shared memory meets your existing tools?
Explore QIP Adapter