Kronveil watches your infrastructure, detects anomalies in real time, and auto-remediates incidents before you even wake up.
As platform engineers, we've all been there: 3 AM pages, scrambling through dashboards, correlating logs across 15 different tools, and trying to figure out why the system broke — not just what broke.
I built Kronveil to solve this. It's an open-source, AI-powered observability agent that combines deep telemetry collection, real-time anomaly detection, LLM-powered root cause analysis, and autonomous remediation — all in a single Go binary.
In this post, I'll walk you through the architecture, the intelligence pipeline, and show you real test results of the system detecting anomalies and auto-remediating incidents in milliseconds.
GitHub: github.com/kronveil/kronveil
The Problem
Modern infrastructure is complex. A typical production environment has:
- Hundreds of Kubernetes pods scaling up and down
- Apache Kafka clusters processing millions of events per second
- Multi-cloud workloads across AWS, Azure, and GCP
- CI/CD pipelines deploying dozens of times per day
Traditional monitoring tools tell you what happened. But by the time you get the alert, correlate the signals, and figure out the root cause — you've already burned 30 minutes of MTTR.
What if your observability platform could think?
Architecture Overview
Kronveil is designed as a layered system with four main tiers:
LAYER 1: DATA COLLECTION
========================
+----------+ +-------+ +-------+
|Kubernetes| | Kafka | | Cloud |
|Collector | |Collect| |Collect|
+----+-----+ +---+---+ +---+---+
| | |
+----+-----+ +--+---+ |
|CI/CD | | Logs | |
|Collector | |Tailer| |
+----+-----+ +--+---+ |
| | |
v v v
================================
LAYER 2: KAFKA EVENT BUS
================================
telemetry.raw -> telemetry.enriched
anomalies.detected -> incidents.new
remediation.actions -> policy.audit
(10M+ events/sec | 3x replication)
================================
|
v
LAYER 3: INTELLIGENCE
========================
+---------+ +----------+
| Anomaly | | Root |
| Detect | | Cause |
| Z-Score | | Analyzer |
| EWMA | | DFS+LLM |
+---------+ +----------+
| |
v v
+---------------------+
| INCIDENT RESPONDER |
| Detect -> Triage |
| -> Respond -> Resolve|
+---------------------+
|
v
LAYER 4: ACTION
========================
+-------+ +------+ +------+
| Slack | |Pager | |Prom |
| Alert | |Duty | |Metric|
+-------+ +------+ +------+
The diagram above shows the full platform. Let's break down each layer.
Layer 1: Data Collection
Five specialized collectors continuously gather telemetry from your infrastructure. Each collector is a Go interface implementation that runs in its own goroutine and pushes TelemetryEvent structs into the event bus.
+--------------+---------------------------+
| Collector | What It Watches |
+--------------+---------------------------+
| Kubernetes | Pods, Nodes, Events, HPA |
| | Metrics API, Deployments |
+--------------+---------------------------+
| Kafka | Consumer lag, Topics |
| | Throughput, Partitions |
+--------------+---------------------------+
| Cloud | EC2, RDS, ELB, Lambda |
| | S3, CloudWatch metrics |
+--------------+---------------------------+
| CI/CD | GitHub Actions, Jenkins |
| | GitLab CI pipelines |
+--------------+---------------------------+
| Logs | File tailing, Syslog |
| | Structured log parsing |
+--------------+---------------------------+
Each collector implements a simple interface:
type Collector interface {
Name() string
Start(ctx context.Context) error
Stop() error
Health() ComponentHealth
}
This means adding a new data source (e.g., Datadog, New Relic) is just implementing this interface — no changes to the core engine needed.
Layer 2: Apache Kafka Event Bus
All telemetry flows through a unified Kafka event bus. This decouples collectors from intelligence modules — they don't know about each other. The bus handles 10M+ events/sec with 3x replication.
KAFKA TOPICS (10 total):
========================
Telemetry Flow:
telemetry.raw
-> telemetry.enriched
-> anomalies.detected
Incident Flow:
incidents.new
-> incidents.updated
-> remediation.actions
Governance Flow:
policy.violations
-> policy.audit
-> capacity.forecasts
Config: capacity.changes
Why Kafka? Three reasons:
Durability — events survive crashes, enabling replay and audit trails
Fan-out — multiple intelligence modules can consume the same event stream independently
Backpressure — if anomaly detection falls behind, events queue up instead of being dropped
Layer 3: Intelligence Engine
This is the brain of Kronveil. Three modules analyze telemetry in parallel, each specializing in a different aspect:
+----------------------------------+
| ANOMALY DETECTOR |
| |
| Input: telemetry.enriched |
| |
| Algorithms: |
| - Z-Score (deviation from mean)|
| - EWMA (trend smoothing) |
| - Linear Trend (prediction) |
| |
| Output: anomalies.detected |
+----------------------------------+
|
v
+----------------------------------+
| ROOT CAUSE ANALYZER |
| |
| Input: anomalies.detected |
| |
| Process: |
| 1. Build dependency graph |
| 2. DFS traversal for causality |
| 3. Collect evidence |
| 4. LLM analysis (AWS Bedrock) |
| |
| Output: root cause + fix |
+----------------------------------+
|
v
+----------------------------------+
| CAPACITY PLANNER |
| |
| Input: telemetry.enriched |
| |
| Algorithms: |
| - Linear regression forecast |
| - Confidence intervals |
| - Resource right-sizing |
| |
| Output: capacity.forecasts |
+----------------------------------+
All three modules feed into the Incident Responder, which orchestrates the full incident lifecycle:
INCIDENT LIFECYCLE:
===================
Anomaly Root Cause Capacity
Detected Found Alert
\ | /
v v v
+-------------------------+
| INCIDENT RESPONDER |
| |
| 1. Create Incident |
| 2. Score Severity |
| 3. Correlate Events |
| 4. Auto-Remediate |
| 5. Notify (Slack/PD) |
| 6. Track Resolution |
+-------------------------+
|
v
+-------------------------+
| AUTO-REMEDIATION |
| |
| - scale_deployment |
| - restart_pods |
| - rollback_deploy |
| - drain_node |
| - failover_db |
| - toggle_feature |
| |
| Safety: |
| - Circuit breaker |
| - Dry run mode |
| - Human approval gate |
+-------------------------+
Layer 4: Action & Integrations
The final layer delivers results to humans and systems:
+----------+ +-----------+ +---------+
| AWS | | Slack | | Pager |
| Bedrock | | Block Kit | | Duty |
| (LLM) | | Alerts | | Events |
+----------+ +-----------+ +---------+
+----------+ +-----------+ +---------+
| REST API | | gRPC API | | Prom |
| :8080 | | :9091 | | :9090 |
+----------+ +-----------+ +---------+
REST API (:8080) — Dashboard, incident management, test injection
gRPC API (:9091) — High-performance inter-service communication
Prometheus (:9090) — Metrics export for Grafana dashboards
Slack — Real-time alerts with Block Kit rich formatting
PagerDuty — On-call escalation via Events API v2
AWS Bedrock — LLM backbone for root cause analysis
The Complete Event Flow
Here's how a single CPU spike travels through the entire system:
CPU spike on pod-xyz (95% usage)
|
v
[K8s Collector] picks up metric
|
v
[Kafka] telemetry.raw topic
|
v
[Anomaly Detector] Z-score = 5.8 sigma
|
v
[Kafka] anomalies.detected topic
|
v
[Incident Responder] creates INC-0001
|
+---> [Root Cause Analyzer]
| |
| v
| DFS on dependency graph
| |
| v
| AWS Bedrock LLM analysis
| |
| v
| "OOM in pod-xyz caused by
| memory leak in v2.3.1"
|
+---> [Auto-Remediation]
| |
| v
| scale_deployment (replicas: 5)
|
+---> [Slack] Alert with root cause
+---> [PagerDuty] Page on-call
+---> [Prometheus] Metric exported
|
v
INC-0001 resolved (MTTR: 1.7ms)
Deep Dive: The Intelligence Pipeline
This is where Kronveil gets interesting. Let me walk through how a single CPU spike turns into an auto-remediated incident.
Step 1: Anomaly Detection
Kronveil uses a combination of statistical methods:
Z-Score Analysis: Measures how many standard deviations a value is from the mean
EWMA: Smooths out noise to detect real trends
Linear Trend Prediction: Identifies directional trends to predict upcoming anomalies
The detector maintains a sliding time window for each signal and requires a minimum of 30 data points before it starts detecting. This prevents false positives during cold starts.
Sensitivity levels:
| Level | Z-Score Threshold | Use Case |
|---|---|---|
| High | 2.0 sigma | Critical systems, catch everything |
| Medium | 3.0 sigma | Default, balanced |
| Low | 4.0 sigma | Noisy environments, reduce alerts |
Step 2: Incident Creation & Severity Scoring
When an anomaly is detected, it gets scored on a 0.0 to 1.0 scale:
Score >= 0.9 --> CRITICAL --> Page On-Call
Score >= 0.7 --> HIGH --> Slack Alert
Score >= 0.5 --> MEDIUM --> Dashboard
Score < 0.5 --> LOW --> Log Only
The incident responder also correlates events — grouping related anomalies within the same time window to avoid alert storms.
Step 3: Root Cause Analysis (LLM-Powered)
For high/critical incidents, Kronveil uses AWS Bedrock (Claude or Titan):
- Build a dependency graph of affected services
- Traverse the graph using DFS to find the causal chain
- Collect evidence (metrics, logs, events)
- Send to the LLM with a structured prompt
- Receive root cause explanation and recommended fix
Step 4: Auto-Remediation
Supported actions:
| Action | Description |
|---|---|
scale_deployment | Scale up/down pods |
restart_pods | Rolling restart |
rollback_deploy | Revert to previous version |
drain_node | Safely drain a problematic node |
failover_db | Database failover |
toggle_feature | Feature flag toggle |
Safety is built in:
Circuit Breaker: Max 5 attempts per 10 minutes
Dry Run Mode: Test remediation without executing
Approval Required: Optional human-in-the-loop
Cooldown Period: Prevent remediation storms
Testing It Live
I deployed Kronveil on a local Kubernetes cluster using kind and tested the full pipeline.
Deployment
kind create cluster --name kronveil-test
docker build -f deploy/Dockerfile.agent -t kronveil:latest .
kind load docker-image kronveil:latest --name kronveil-test
helm install kronveil ./helm/kronveil \
--namespace kronveil --create-namespace \
--set image.repository=kronveil \
--set image.tag=latest \
--set image.pullPolicy=Never
Health Check
All 6 modules running and healthy:
{
"data": {
"status": "healthy",
"components": [
{"name": "kubernetes-collector", "status": "healthy"},
{"name": "kafka-collector", "status": "healthy"},
{"name": "anomaly-detector", "status": "healthy"},
{"name": "incident-responder", "status": "healthy"},
{"name": "root-cause-analyzer", "status": "healthy"},
{"name": "capacity-planner", "status": "healthy"}
]
}
}
Triggering Anomaly Detection
Kronveil includes a test injection endpoint. The burst mode sends 35 normal baseline events followed by a single spike — triggering the full pipeline.
curl -s -X POST \
"http://localhost:8080/api/v1/test/inject?mode=burst" \
-H "Content-Type: application/json" \
-d '{"source":"production-api","signal":"cpu_usage"}'
Result:
{
"data": {
"status": "burst_complete",
"events_injected": 36,
"anomalies_found": 1,
"incidents_created": 1,
"anomalies": [{
"signal": "production-api.cpu_usage",
"score": 0.97,
"severity": "critical",
"description": "value 200.00 deviates 5.8 sigma from mean"
}],
"incidents": [{
"id": "INC-0001",
"severity": "critical",
"status": "resolved",
"timeline": [
{"action": "created", "actor": "system"},
{"action": "remediation_started", "actor": "ai"},
{"action": "resolved", "details": "MTTR: 1.7ms"}
]
}]
}
}
What happened in those 1.7 milliseconds:
- 35 baseline events established a normal CPU usage pattern (~50%)
- One spike event hit 200% — deviating 5.8 sigma from the mean
- Anomaly detector flagged it as critical (score: 0.97/1.0)
- Incident responder created INC-0001
- Auto-remediation kicked in with
scale_deployment
- Incident resolved — MTTR: 1.7ms
Testing Multiple Signal Sources
I then simulated a Kafka consumer lag spike — a second anomaly detected at 5.8 sigma, triggering INC-0002 with auto-remediation. Both incidents are independently tracked with full audit trails.
Tech Stack
| Component | Technology |
|---|---|
| Agent | Go 1.21 (single binary, ~10MB) |
| Event Bus | Apache Kafka (10 topics, 3x replication) |
| AI/LLM | AWS Bedrock (Claude, Titan) |
| Anomaly Detection | Z-Score, EWMA, Linear Regression |
| Policy Engine | OPA (Rego rules) |
| Secret Management | AWS Secrets Manager + HashiCorp Vault |
| Deployment | Kubernetes + Helm |
| Dashboard | React + TypeScript |
| API | REST + gRPC + Prometheus |
76 files. One binary. Zero external Go dependencies.
What's Next
Real Kubernetes client-go integration — watch actual pods, nodes, and events
Kafka consumer group monitoring — connect to real brokers
Multi-cloud secret management — Azure Key Vault and GCP Secret Manager support (currently AWS-focused)
Dashboard UI — React dashboard for visualizing anomalies and incidents
Prometheus metrics export — anomaly scores, incident counts, MTTR
Webhook integrations — Slack and PagerDuty notifications
Multi-cluster support — monitor multiple clusters from a single agent
Get Involved
Kronveil is open source under the Apache 2.0 license.
GitHub: github.com/kronveil/kronveil
Star the repo if you find this useful
Contributions welcome — especially around new collector integrations, LLM prompt engineering, and dashboard widgets
Developed by Ramasankar Molleti
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