Signal Capture Engine
Collects corrections, approvals, rejections, tool outcomes, and missing-context moments across every channel agents and people use.
Continuous Learning System
From action to better.
Live work (corrections, rejections, missing context, tool failures, approvals) becomes evidence and reviewable improvements. Never silent self-modification.
Your expertise becomes stale the moment work begins.
New exceptions appear. Approval paths shift. Tool fields change. Teams find better ways to work. Without continuous learning, you are managing a fixed system inside a moving company, and the gap between platform and reality compounds every week.
The Continuous Learning System keeps the platform alive. It turns real production signals into durable improvements, but only through human approval. Material agent behaviour, tool permissions, and integrations never change silently.
Inside the Continuous Learning System
How Continuous Learning works
Six steps turn live signals into governed improvements.
Capture signals from work
Collect corrections, approvals, rejections, tool failures, missing-context moments, and admin notes, across Company GPT, agent teams, and operational channels.
Classify and diagnose
Determine the root cause (missing expertise, stale context, wrong process, routing issue, tool gap) before suggesting any fix.
Update low-risk memory
Automatically capture personal continuity, lightweight context, and operating observations when policy allows, without bothering an admin.
Create review proposals
Route expertise, context, process, and guardrail changes to a human approval queue with evidence, diff, expected effect, and rollback plan.
Material change requires approval
Agent behaviour, tool permissions, integrations, and guardrails need owner approval and a measurement plan before release.
Measure and iterate
Track before/after improvement, detect regressions, and roll back if needed. Every change has a measurable outcome.
What Continuous Learning produces
Every improvement is observable, reviewable, and reversible.
- Improvement proposal queue
- Trust decay & re-verification queue
- Nudges: the advisory inbox
- Low-risk memory captures
- Reviewable knowledge updates
- Material change proposals (change requests)
- Before/after measurements
- Post-apply drift detection
- Targeted expertise refresh triggers
- Brain Metrics: the loop's scoreboard
Capabilities
Six engines turn live work into governed learning.
Root Cause Diagnosis
Distinguishes missing expertise from stale context from process drift from tool gaps, and routes each to the right kind of fix.
Trust Decay & Review
Verified knowledge does not stay verified by inertia: trust decays as reviews lapse or evidence shifts. The platform only lowers trust on its own; only a human restores it, through Trust Review and the Review hub.
Knowledge Synthesis & Curation
Projected wiki pages re-synthesize as knowledge changes, the Gardener proposes splits, merges, and missing pages, and the contradiction detector flags pages that disagree. Humans approve every proposal.
Builder Agent & Autonomy Ladder
The Builder Agent reads the accumulated knowledge and proposes configuration improvements as change requests. The autonomy ladder has no auto-apply rung, and a kill switch halts everything unattended.
Measurement & Feedback Loop
Tracks before/after metrics, replay drift, and Brain Metrics like brain-to-config latency, detects regressions, enables rollback, and feeds validated learning back into expertise packages.
The governed learning loop
The feedback cycle that keeps expertise packages and agent behaviour current as your company changes: observe, diagnose, propose, approve, release, measure.
- ObserveCollect signals from Company GPT, agent actions, approvals, corrections, and operational system activity.
- DiagnoseDetermine whether the issue is missing expertise, process drift, stale context, or a tool gap, not just a prompt problem.
- ProposeCreate an improvement proposal with evidence, affected objects, expected effect, and rollback plan.
- ApproveRoute to the relevant owner (expertise admin, process owner, agent manager) for review and decision.
- ReleaseDeploy the approved change with versioning, audit trail, and automatic rollback capability.
- Measure & feed backTrack impact, detect regressions, and feed validated learning back into expertise packages.
Signal sources
- Company GPT questions & corrections
- Agent conversations & tool calls
- Human edits & rejections
- Tool failures & integration gaps
- Guardrail triggers
- Meetings, emails, new documents
- Admin flags & CS observations
- Operational system changes
Loop governance
- Anything can propose a change; only a human applies it.
- The platform only ever lowers trust on its own; only a human restores it.
- Diagnosis-first: fix root causes, not symptoms.
- Versioning and measurement: every change is reversible and measured.
Why this is different
Diagnosis before fix
Distinguishes missing expertise from stale context from prompt problems, and routes each to the right solution layer.
Human-in-the-loop, not blocker-in-the-loop
Lightweight signals capture automatically; only material production behaviour changes need owner approval and measurement.
Measurement built in
Improvements are not complete until effect is measured and rollback plans are ready.
Routes to expertise, not just prompts
Missing judgment triggers expertise refresh, not a hidden prompt patch nobody can audit later.
See Verti run on your operations.
Verti connects expert judgment, live context, governed action, and learning loops into one production system.