Building your own AI SRE gives you control. Getting it to work in production is the hard part.

The agent is straightforward. Building the layer that makes it accurate is the hard part. Causely provides a live causal model of your system, always current.

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The Hard Part

The agent isn't the problem. It's what the agent doesn't have.

You can build an ops agent in a week. Claude Code, HolmesGPT, your own framework, the tooling is there. What takes months to build, and breaks the moment your topology changes, is the layer underneath: a semantic model of how your system behaves, what depends on what, and how failures propagate.

Without that layer, your agent scans broadly, forms hypotheses from ambiguous telemetry, and sometimes gets it wrong in ways that matter. It hallucinates root causes on complex incidents. It burns tokens doing work that should already be done. Every time a service is added or topology changes, someone has to update the context the agent depends on to stay accurate.

The Foundation

Causely is the causal model your agent runs on.

Causely continuously builds and maintains a live model of your system: services, dependencies, failure paths, and health signals. It's delivered via MCP, so your agent queries it directly, with no scanning, no topology prompt engineering, and no manual updates when your system changes.

Your team builds the agent. Causely handles the foundation it depends on.

Accurate from day one

Your agent gets deterministic root cause from causal graph traversal, not correlation over raw telemetry. 100% fault accuracy across all agent configurations in our benchmark.

Stays current automatically

The causal model updates continuously as your system changes. No prompt maintenance when services are added or topology evolves.

Works with whatever you build

MCP-native. Compatible with Claude Code, Cursor, Codex, and any agent framework. Plug in and your agent immediately has system understanding it would have taken months to build.

What You Get

Build with Causely vs. without.

Build without Causely	Build with Causely
Agent scans broadly to form a hypothesis	Agent queries the causal model directly
Topology encoded in prompts, goes stale	Live causal model, always current
Hallucinated root causes on complex incidents	Deterministic root cause from causal graph traversal
False positives erode trust in the agent	100% fault accuracy across all benchmark configurations
Token costs grow with system complexity	48% fewer tokens per investigation on average

The Data

What agents without causal context actually do in production.

67%

false positive rate on a healthy baseline (agents inventing incidents that don't exist)

75%

of no-context configurations produced at least one missed diagnosis

433K

avg tokens per investigation without Causely

Based on 72 experiments across Claude Code (Sonnet), Codex (GPT-5.4-mini), HolmesGPT (Gemini Flash Lite), and HolmesGPT (Sonnet).

These aren't edge cases. They're what agents do when they lack a deterministic understanding of your system. They scan broadly, form hypotheses from ambiguous telemetry, and sometimes get it wrong in ways that matter. False pages that erode trust, missed diagnoses that become P0 incidents.

The Bottom Line

Your team builds the agent. Causely is the foundation it runs on.

Plug in via MCP. Your agent immediately gets a live causal model of your environment: topology, dependencies, failure paths, always current. The work you would have spent building and maintaining that layer goes back to building the product.

“Every second counts when you're running critical production systems, and a GenAI hallucination at 3 AM can be catastrophic. ‘Plausible answers’ won't cut it. You need a deterministic, causal foundation for automating ops, and that's what Causely provides.”