Sold by: Causely
Give your AI ops agents a continuously updated causal model of your system, delivered via MCP, so they stop guessing, consume fewer tokens, and remediate faster.
Overview
AI ops agents are only as reliable as the context they receive. Raw telemetry tells an agent what is happening. It cannot tell the agent what caused it, which services are at risk, whether a planned change is safe, or when the system is approaching its limits. Agents reasoning from telemetry alone scan broadly, accumulate context, and produce inconsistent answers.
Causely solves this by building and maintaining a continuously updated causal model of your applications including what normal looks like, what caused what, how changes ripple through your system, and what safe looks like. This model is exposed to your agents via MCP as structured, actionable context, delivered the moment the agent needs it, specific to your environment.
The results are measurable. In a benchmark across 72 experiments covering Claude Code, Gemini, and Codex configurations, agents with Causely reached 100% accuracy across every configuration, reduced average token consumption by 48%, cut mean query time by 63%, and eliminated the 67% false positive rate.
Causely connects to your existing observability sources including CloudWatch, Prometheus, Datadog, and OTel via native integrations and exposes causal context through a standard remote MCP server. Any MCP-compatible agent framework connects immediately. Causely deploys with either a SaaS or BYOC architecture, so your data stays within your environment. No new instrumentation. No model training. No rip and replace.
Highlights
- Agents that diagnose correctly, every time - AI ops agents without a causal model construct narratives from ambiguous telemetry. In our benchmark, 75% of configurations missed at least one fault diagnosis, and two of four produced a 67% false-positive rate, generating incidents that did not exist. With Causely, every configuration achieved 100% fault accuracy, and false-positive rates dropped to zero. Agents receive a structured causal model, so diagnosis is deterministic, not probabilistic.
- Fewer tokens, lower cost per investigation - Open-ended environment scanning is how agents run up inference costs. Causely replaces scanning with targeted causal queries: agents request the context they need and receive it in a compact, structured MCP response. In our benchmark, this reduced average token consumption by 48% and worst-case token exposure by 81% for the most expensive configuration. Fewer tool calls, less context accumulation, lower cost per correct diagnosis.
- From alert to remediation in a fraction of the time - Agents reasoning from raw telemetry are slow by design. They scan broadly before they can act. Causely delivers pre-computed causal context the moment an agent needs it, cutting the scan phase to zero. In our benchmark, mean query time dropped 63% on average and up to 83%. Faster triage during active incidents. Earlier detection before they escalate.
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Dimension | Description | Cost/12 months |
|---|---|---|
Service | A service represents one deployable application component, such as a microservice, API, or external dependency. Each service that Causely analyzes as part of your topology for causal inference counts toward your plan. This plan includes up to 500 services; additional services can be added by upgrading your plan. | $24,000.00 |
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