Self-Healing · Self-Learning

The Platform That
Fixes Itself.

While your competitors wake up to broken pipelines, Swashi detects the incident, proposes the fix, and has it ready for one-click approval — before you even notice.

15
Incident patterns detected
24
Agents monitored 24/7
90d
Self-learning window
0
Auto-deploys without approval

The Self-Healing Loop

Five stages from incident detection to human-approved patch — fully automated except the final approval.

Stage 1 of 5

DETECT

Watchdog monitors 24 agents via heartbeat every 60s

DETECT
CLASSIFY
RETRIEVE
PROPOSE
APPROVE
Incident Intelligence

15 Patterns. Zero Surprises.

Every pattern was extracted from real production incidents. The system recognises them instantly — because it has seen them before.

HIGH
deployment-killed-batch
Update during a live job interrupts work in progress
HIGH
missing-dependency
A required component fails to load after an update
HIGH
broken-data-write
An agent writes data in the wrong format
HIGH
auth-handshake-order
A scheduled task gets incorrectly blocked by security checks
HIGH
cold-start-timeout
A slow restart causes a task to exceed its time limit
HIGH
memory-limit-exceeded
An agent runs out of memory mid-task, leaving partial data
MEDIUM
kill-switch-stub
A pause toggle shows "paused" but the change didn't save
MEDIUM
wrong-data-reference
Stale data reference after a structure update
MEDIUM
schema-version-mismatch
Newer data read by an older agent, fields don't line up
MEDIUM
message-retry-failure
A message fails repeatedly and gets set aside for review
MEDIUM
cross-origin-block
A request gets blocked by a missing security header
MEDIUM
auth-token-expired
A security token expires mid-way through a long task
MEDIUM
rate-limit-cascade
A usage limit causes cascading retries across agents
MEDIUM
search-index-missing
A complex search fails because an index wasn't ready yet
MEDIUM
config-secret-missing
A required setting is missing after expanding to a new region
Self-Learning Fix Engine

Every Fix Becomes
Future Knowledge.

Every issue ever fixed is turned into searchable knowledge. When a new incident arrives, the engine retrieves the most relevant past fixes — ranked by relevance, recency, and confidence.

1
Service Scope Filter
Only retrieves fixes for the affected service
2
Architecture Version Filter
v3.x fixes ranked 1.0×, v2.x ranked 0.5×, v1.x excluded
3
Confidence Decay
Fixes older than 180 days get 0.75× score, older than 365 days get 0.5×
4
Top-3 Ranked Results
Final ranking by composite score with calibrated confidence 0–100%
fixes_knowledge · chunk FIX-S26-3
id: "FIX-S26-3"
title: "Experiment Agent DEGRADED on x-command"
patternType: "wrong-collection"
riskLevel: "MEDIUM"
date: "2026-05-29"
architectureVersion: "v3.0"
confidenceScore: 87
isRepeatedFailure: false
keywords: ["agent_heartbeats", "lastPing", "DEGRADED"]
Retrieved in 12ms · Score: 87/100
Self-Learning Engine

Every Outcome.
Compounding Intelligence.

The system tracks outcomes for 90 days, extracts winning patterns into Campaign DNA, and blacklists failing patterns into Anti-DNA — so every campaign is smarter than the last.

90-Day Outcome Window

Every lead, email, call, meeting, and revenue event is tracked. The system learns what actually converts over the full sales cycle.

Campaign DNA

Winning opener, offer, objection counter, tone, and call window are distilled from measured outcomes — not guesses.

Anti-DNA

Patterns that failed are equally preserved. The system learns what NOT to do, preventing costly regressions.

Wilson Score Bandit

Experiments use Wilson score confidence intervals — dynamically shifting traffic toward winners, not naive 50/50 splits.

Global Learning Boundary

Your data never trains other tenants. Only de-identified aggregate signals flow upward — enterprise trust protected.

No Rogue Optimization

The system cannot self-edit its own frameworks, rewrite agent prompts, or deploy logic changes without human approval.

The Learning Loop
Lead
Email
Reply
Call
Meeting
Revenue
intelligence_events
Growth Intelligence
Campaign DNA
Applied Everywhere
Human Governance

Not a Black Box.
You Remain in Control.

Higher impact always requires higher approval. Swashi never autonomously deploys, merges, or mutates anything without a human gate.

Action TypeAuthorityWhy
Content generationAutoLow stakes, reversible
Outreach sendOptional approvalVolume-gated
Voice sales actionApproval requiredHigh-intent interaction
Deploy / patchApproval requiredIrreversible in production
Pricing / offer changeApproval requiredRevenue-impacting

The platform that fixes itself
while you sleep.

Self-Healing and Self-Learning are included in every Swashi plan.