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An AI-powered reliability engine that acts as a system intelligence layer for your agents and automation workflows. Understand why your systems behave the way they do, resolve incidents faster, and enable safe, automated decisions.

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Quick Start

Deploy the mediator, connect your telemetry, and give your agents access to Causely's causal model.

We support 20+ telemetry sources and integrate with leading AI agents and coding tools via MCP.

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Built for Reliability Agents

Agents fail not because they lack telemetry, they fail because they lack a causal model. Causely provides that model via MCP. Drop Causely into any agent. Get structured root cause analysis, blast radius, and remediation guidance, computed from your system's actual causal graph.

Incident Triage

See all active symptoms, identify root causes, and map the blast radius.

Quick Service Health

Resolve a service name and get a full health snapshot in two calls.

Post-Deploy Validation

Compare resource consumption, error rates and latency before and after a deployment for one service or an entire fleet.

Post-Incident Reporting

Generate structured postmortems and engineering tickets from resolved incident data.

What's your goal?

Causely builds a comprehensive causal model of your system, continuously learning from your telemetry to understand relationships, dependencies, and failure patterns. This enables precise root cause analysis and proactive risk identification. Discover how Causely helps you achieve your reliability goals.

Accelerate resolution

Get precise root cause insights during incidents to resolve issues faster.

Proactively prevent incidents

Identify emerging risks before they impact your services and SLOs.

Understand system behavior

Gain deep insights into why your systems behave the way they do.

Automate reliability workflows

Enable agents and automation systems to detect, diagnose, and resolve issues safely.

Optimize performance

Continuously improve system performance with actionable insights.

Your agents are ready. Give them the context to act.

Causely is the missing layer between your observability data and autonomous operations.

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