Compound Learning Loop
VALP is not tied to a user’s local agents. The protocol is a control loop for
autonomous work.
The first-principles question is:
When an intelligent system claims a task is done, what evidence makes that claim trustworthy?
VALP answers with visible intent, routing, execution, evidence, correction,
approval, synthesis, audit, and learning.
Operating Principles
- First-principles evidence:
- A completion claim must point to receipts, files, logs, screenshots, reviews,
approval ledgers, or other concrete evidence. Natural-language confidence is
not enough.
- Control-system loop:
- The task has a target, sensors, actuators, feedback, error correction, and
stop conditions. Dispatches are actuators. Receipts and evidence are sensors.
valp audit is the controller check.
- Accounting ledger:
- Critical states are append-only or task-local records. Dispatch sent,
dispatch submitted, expected evidence, review, approval, synthesis, and
learning all need auditable refs.
- Anti-hallucination boundary:
- LLM output is useful reasoning, but it is not completion proof until it
touches external evidence.
- Compound engineering:
- Every non-trivial task should improve future tasks. The improvement must be
stored as evidence-backed feedback, not hidden memory.
Core Artifacts
automation-policy.json- Records what can proceed automatically, what must stop, the risk
classification, approval behavior, stop conditions, and audit grade.
context-pack.json- Gives workers compact, evidence-backed task context without copying private
transcripts or stale memory.
routing-feedback.json- Records the validated assignment outcome: Leader-declared Agents, expected evidence, actual
evidence, blockers, result, lessons, and next routing hints.
learning-feedback.json- Records what the system learned and which protocol, schema, audit, docs,
local overlay, skill, runtime adapter, or memory updates are proposed.
Learning Rule
Learning feedback is a prior, not authority.
Workspace feedback indexes are also not authority. A positive history entry
must resolve to matching task-local feedback, passed completion gates, and
existing evidence before it can influence routing.
old success + missing current tool -> do not route
old success + approval risk -> stop for approval
old failure + repaired current capability -> route with lower confidence
old context gap + similar task -> include the missing context in context-pack
This keeps VALP intelligent without letting stale memory override current
evidence.
Automation Rule
Full automation means:
continue automatically while evidence proves the loop is healthy
stop automatically when evidence, approval, context, runtime, or scope gates fail
It does not mean silent high-risk execution. The safer automation is the one
that knows exactly when to stop.