What Is a Causal Model in Observability?
A causal model isn’t an LLM. Here’s what causal models are, how they differ from rules engines and correlation, and why agents need them.
Your agents trust your telemetry. Should they?
Your agents read telemetry fast and literally. OllyGarden shows how missing, contradictory, and split data quietly produce wrong answers, and Causely shows why even clean data needs causal context before an agent opens the wrong PR.
Causely Feature Demo: Diagnosing Failures in an Agentic App
Causely maps an agentic app's agent sessions, MCP tools and services, then traces failed runs to the cause: a broken tool contract or an MCP server exception.
Causely Upgrades Its MCP Server So Background Agents Are Faster to Deploy, Easier to Trust, and Cheaper to Run
Causely’s MCP server upgrades help background agents deploy faster, produce traceable decisions, and use fewer tokens, with new open source examples.
How OpenTelemetry observes AI agents without code changes
OpenTelemetry eBPF Instrumentation captures LLM calls, MCP tool calls, embeddings, and reranks at the network layer, giving multi-agent apps full traces with no code.
Causely Feature Demo: Faster Investigations with AWS DevOps Agent
With the Causely MCP server, AWS DevOps Agent halved its investigation time and cut tool calls from 36 to 10. With an external fault, 3 minutes instead of 15.
How to make Claude background agents 3-5x faster and cheaper
In three regression scenarios, Claude Managed Agents with Causely produced correct fixes 3.6–5.7x faster and at 3.5–5x lower cost than the baseline agent.
What should trigger an autonomous agent in production?
A control band breach tells an autonomous agent only that a metric moved. A trigger carrying a causal chain tells it what broke and how far the problem reaches.
How to Check an Agent's Diagnosis Before It Touches Production
New Causely MCP tools show which diagnoses an agent ruled out and how far a failure would spread, so teams can verify the agent's reasoning before acting in production.
How to Operate AI Agents in Production: The Three-Layer Stack
Operating AI agents in production takes three layers: instrumentation, evaluation, and causal reasoning, including the root-cause analysis behind agent reliability.
Why A2A Joining the AAIF Matters
Causely joins the AAIF along with A2A from Google. Agents can now talk over an open standard, but what they say is only as good as the context they start from.
How Open-Inspect and Causely close the loop on autonomous remediation
Open-Inspect gives background agents an execution environment. Causely gives them a causal diagnosis. Together, they close the loop from finding to fixing.