How it works
Autonomous reliability infrastructure
A detailed look at how the agent watches your system, investigates anomalies, and maintains software health autonomously.
Traditional observability assumes humans are always watching. The autonomous reliability agent removes that assumption. Instead of logs → dashboards → alerts → humans, telemetry flows directly into agent reasoning, enabling continuous, proactive system maintenance.
The system operates in five stages: ingestion, summarization, investigation, action, and improvement. Each stage is designed to scale judgment, not just data.
The five stages
How telemetry becomes autonomous action
Ingests existing telemetry
Connects to ClickHouse / OpenTelemetry / logs / metrics / traces. No re-instrumentation required.
- •Reads from your existing observability stack—no new agents or instrumentation needed
- •Supports ClickHouse, Prometheus, Datadog, New Relic, Splunk, and custom OpenTelemetry collectors
- •Handles high-volume, high-cardinality telemetry streams efficiently
- •Columnar storage optimized for time-series queries and compression
Continuously summarizes system state
Produces dense, structured situation reports optimized for agent reasoning—not human reading.
- •Compresses hours of telemetry into rolling situation reports every 5-15 minutes
- •Structured format optimized for LLM reasoning, not dashboard visualization
- •Captures trends, anomalies, correlations, and context that humans would miss
- •Maintains historical context while focusing on recent changes and deviations
Agent investigates anomalies proactively
No alerts required. Changes, deviations, and weak signals trigger investigation.
- •Long-running agent maintains a live mental model of your system's normal state
- •Detects deviations, weak signals, and anomalies before they become incidents
- •Sub-agents investigate anything that looks off—even if nothing is technically 'broken'
- •No predefined alerts or thresholds—learns what normal looks like for your system
Takes action or escalates
Rollbacks, restarts, scaling, feature flags, or human escalation—based on configured autonomy.
- •Graduated autonomy levels: observe-only, recommend, auto-fix with approval, full autonomy
- •Actions include rollbacks, restarts, scaling adjustments, feature flag toggles, and more
- •Full audit trail of every decision, investigation, and action taken
- •Human escalation for high-risk decisions or when confidence is low
Improves automatically as models improve
Swap in better models → better reasoning → fewer incidents. No product migration.
- •Model-agnostic architecture—swap LLM providers or models without code changes
- •As reasoning models improve, the agent gets smarter automatically
- •No product migrations or rewrites—just better models
- •Continuous improvement without engineering effort
Core processes
Deep dive into how each component operates
Telemetry ingestion
How we handle your existing observability data
- •Read-only access to your telemetry stores
- •No data duplication—queries run against your existing infrastructure
- •Efficient columnar queries optimized for time-series analysis
- •Handles petabyte-scale data without performance degradation
Situation report generation
How we compress telemetry into actionable intelligence
- •Rolling window analysis of system state
- •Structured JSON format optimized for agent reasoning
- •Captures correlations across metrics, logs, and traces
- •Maintains context about recent deployments, config changes, and external factors
Anomaly detection
How we identify issues before they become incidents
- •Baseline learning from historical patterns
- •Statistical deviation detection across multiple dimensions
- •Weak signal amplification—catches subtle changes humans miss
- •Context-aware—understands normal vs. abnormal for your specific system
Investigation workflow
How agents investigate potential issues
- •Multi-agent architecture: coordinator + specialized investigators
- •Deep dives into logs, traces, and metrics when anomalies detected
- •Hypothesis generation and testing
- •Root cause analysis before taking action
Action execution
How we take corrective actions safely
- •Permission boundaries define what actions are allowed
- •Dry-run mode for testing before production
- •Rollback capabilities for every action
- •Human approval gates for high-risk operations
Continuous learning
How the system improves over time
- •Learns from successful interventions
- •Adapts to your system's unique patterns
- •Model updates improve reasoning without code changes
- •Feedback loops refine detection and action strategies
Architecture
Four layers working together
Data layer
Reads from your existing telemetry infrastructure
Processing layer
Compresses and analyzes telemetry streams
Agent layer
Long-running agents with live mental models
Action layer
Executes corrective actions through your infrastructure
Why this is different
No alerts required
The agent investigates anomalies proactively. You don't need to define thresholds, alerts, or monitors. The system learns what normal looks like and investigates deviations automatically.
No dashboards needed
Telemetry is optimized for agent reasoning, not human visualization. Situation reports are dense and structured—designed for LLM consumption, not dashboard display.
Scales judgment, not just data
Traditional tools scale data storage and query performance. This scales the ability to reason about system health, investigate anomalies, and take corrective action—even at petabyte scale.
Model-agnostic improvement
As LLM models improve, the agent gets smarter automatically. No product migrations or rewrites—just swap in better models for better reasoning and fewer incidents.
Ready to deploy?
Join the waitlist to get early access to the autonomous reliability agent.