Cleric announced the launch of an AI site reliability engineer (SRE) agent that continuously learns from every incident and helps software engineers move more quickly to resolve issues.
When an incident occurs, Cleric’s system autonomously investigates and delivers findings directly in Slack with links to relevant evidence. For complex cases, engineers can guide its reasoning through conversation or examine detailed diagnostics through a web interface. It provides confidence scores and learns from feedback, improving its signal-to-noise ratio over time.
Customers don’t need to rebuild or adapt their environments to take advantage of Cleric. Cleric helps teams manage the complexity of modern infrastructure by learning directly from their systems and interactions. It integrates with existing observability, CI/CD, and incident tooling (including popular observability platforms like Datadog and Grafana) to analyze alerts, correlate data, and surface root causes faster over time.
“We designed Cleric to reason about systems the way experienced engineers do: by correlating context across logs, metrics, and configurations, not just reacting to individual alerts,” said Willem Pienaar, Cleric’s co-founder and CTO.
“Any system operating at scale needs to adapt continuously,” said Shahram Anver, co-founder and CEO of Cleric. “Production isn’t static. It's a living environment. Cleric learns from every incident, alert, and human decision to evolve how it supports operations.”
Reliability and software engineering teams are already using Cleric’s agent to spend less of their time putting out fires and more time shipping code. Early adopters report freeing 20–30% of engineering capacity previously lost to repetitive troubleshooting.
Cleric raised a total of $9.8 million in seed funding. This new financing was led by Vertex Ventures US, with follow-on participation from its initial seed investor, Zetta Venture Partners. Cleric has also been named a Gartner Cool Vendor in AI for SRE and Observability 2025. These milestones underscore growing validation that adaptive, learning systems are the next step in how modern teams run production.
The company will use its new funding to expand R&D in San Francisco, accelerate customer deployments, scale production support to meet growing demand, and expand partnerships with observability and infrastructure platforms.
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