Causely is a causal reasoning platform for production systems: it maintains a live causal model of your environment so that AI agents and engineers can determine root causes from evidence instead of guesswork.
Most incident tools investigate by querying telemetry at the moment something breaks and generating a plausible explanation. Causely inverts that approach. It deploys locally, processes telemetry where it lives, and continuously maintains a structured causality graph built from a causal model — a library of known root causes and how they propagate — instantiated against a topology graph of your actual environment. When an alert fires, Causely answers with the diagnosis, the blast radius, and the supporting evidence, and it can also work proactively, surfacing emerging degradation before an SLO is violated. Crucially, Causely is built to be called by other agents: any AI agent or coding tool can query its causal model through an MCP server, APIs, or webhooks, which the company positions as the missing layer between observability data and autonomous operations.
In Causely’s published benchmarks, adding its causal context to existing agents made diagnosis significantly faster and cheaper, and in healthy environments it tells agents when no root cause is active, reducing the fabricated incidents that raw telemetry searches can produce.
Causely does not publish list pricing; the platform is sold through its sales team, typically starting with a deployment against your existing telemetry sources.
Causely is for SRE and platform teams, and for teams building AI operations agents, whose incidents involve tangled cross-service causality and who need diagnoses they can verify rather than plausible-sounding guesses. It also fits teams adopting third-party AI SRE agents who need those agents to be more accurate.