Deploys real-time, AI agents to detect and eradicate threats, fix misconfigurations, and enforce policy before humans ever log in
Governence
“Agents must act autonomously—but only within clear, auditable boundaries.”
Operational and Cost Optimization
“Help me reduce toil and optimize resource usage automatically at scale.”
Observe and Act
“I want agents to continuously observe, decide, and act, across my cloud stack.”
Autonomous CloudOps, No Scripts, No Toil
Cloud Canaries deploys always-on AI agents that monitor, decide, and act across your AWS stack. Slash incident response time and eliminate manual tickets with 24/7 automated remediation and governance no custom code required.
AI-Powered Security That Remediates Instantly
Automatically detect misconfigurations, policy violations, and threats using native integrations with AWS CloudTrail, GuardDuty, and Security Hub. Agents act fast—enforcing IAM best practices and remediating risks in real time.
Slash Cloud Spend by 40% with AI Governance
Continuously optimize your cloud environment by right-sizing instances, terminating idle assets, and enforcing tagging compliance. Agents save customers up to 40% on Cloud costs—without disrupting performance.
Cloud Canaries Open Agents
Cloud Canaries provides a growing library of open-source, AI-powered agents designed for enterprise cloud operations. Each agent is independently deployable, LLM-aware, and capable of monitoring, reporting, alerting, recommending, and automating remediation — with full user control.
K8s Observability | Node & Pod Health | Cluster Intelligence A Service Agent built to assess Kubernetes health using native probes. Continuously collects and reports on the most critical metrics affecting pod readiness, infrastructure stress, and workload stability. Key Features:
App Readiness | Request Latency | Backend Connectivity A smart Service Agent that monitors Django application health and backend dependencies. Designed for modern Python-based services. Key Features:
DAG Health | Scheduler Monitoring | Task Queue Tracking A Service Agent for monitoring Apache Airflow operational health. Ideal for data engineering, orchestration SLOs, and pipeline reliability tracking. Key Features:
AWS Telemetry | Infrastructure Stress | API Behavior A Service Agent that pulls key service-level metrics from AWS CloudWatch to surface system pressure, risk indicators, and failure trends. Key Features:
CPU and memory usage across EC2, ECS, Lambda
Instance status checks (host & guest)
5xx error rate and latency across ALB, API Gateway
Disk IOPS saturation
Throttling metrics for Lambda, DynamoDB, API Gateways
FinOps Automation | Idle Spend | Commitment Gaps A Service Agent that analyzes AWS usage and spend data to surface inefficiencies, untagged resources, and RI/SP savings opportunities. Key Features:
SOC Readiness | Compliance Monitoring | Enterprise Cloud Auditing A lightweight but powerful Service Agent for tracking critical SOC-2 compliance indicators across cloud infrastructure. Built to support audit prep and continuous monitoring. Key Features:
Security & Governance Metrics | Continuous Compliance Designed to automate detection of misconfigurations and policy violations related to AWS cloud compliance. Aligned with NIST, SOC 2, and ISO readiness. Key Features:
IAM violations (admin roles, wildcard permissions)
The Vulnerability Scanning Service Agent is an open-source, LLM-integrated security assistant designed to help SREs, DevOps, and security teams proactively detect, prioritize, and remediate web application vulnerabilities. Built on the trusted OWASP ZAP library, this agent continuously scans for common and critical application weaknesses, then summarizes, recommends, and optionally automates responses — reducing mean time to detect (MTTD) and mean time to remediate (MTTR).
Latency Monitoring | Endpoint Reachability | Geo-Aware Alerting An autonomous Worker Agent that can monitor up to 250 endpoints across environments. It tracks reachability, latency, geographic impact, and response errors. Key Features:
Live health checks across multiple endpoints
Geo-failure clustering and region-aware alerts
p95/p99 latency visibility
Canary test tracking post-deploy
JSON output for integrations with dashboards or ML models
End-to-End Service Probing | User Experience Assurance | Multi-Framework Support
A Service Agent that combines HTTP response monitoring with external Digital Experience Monitoring (DEM) frameworks to provide real-world service availability and performance insights. It proactively probes services from multiple locations, collects response and latency data, and detects degradations before users notice.
Key Features
Multi-Point Probing
External DEM Framework Integration
Autonomous Alerting
Customizable Test Profiles
LLM-Powered Recommendations
How it Works
Meet the Aviary all-in-one AI platform for your Agents that manage your cloud.
Forecasts, predictions and mitigations
Agents can observe, simulate, mitigate and resolve based on actual or forecast data.
Event Identification
Data on every incident is saved and used to identify new or matching events.
Alarms, channels, APIs, metrics and notifications
Users define alarms, notifications and incidents covering any single or set of workloads.
Agent Management
Manage Agents. User define alarms, notifications and incidents covering any single or set of workloads.
Service Control Plane
Provides current health of endpoints, services, APIs for any targeted Worker Agent workload.
Compliance And Goverance
Measuring compliance and SLA metrics over a period using actual and forecasted workloads.
Multi-cloud support
Agents that understand and operate across AWS, GCP, Azure, OCI and IBM
Conversational AI
Natural interaction with agents through enterprise chat tools.
Customizability
The ability to extend or fine-tune agent logic to match org-specific policies.
Snowflake & Databricks Integration
Aviary users can store Agent data on Snowflake then use the data to generate AI models or build models with Databricks. It is easy, update your wallet with account information, enable the selected organization and pick the Agents to be used in model generation and tuning.
Store Agent data on Snowflake
Use Snowflake Cortext or Databricks for forecasting
