Inside the Race to Build Self-Healing Software: Why Causely Thinks Engineers Shouldn’t Be on Call Forever

Every time an application slows down or crashes, businesses run the risk of losing money and damaging customer trust. It’s estimated that enterprises lose $23,750 per minute or $1.4Million per hour of downtime, so reducing the time it takes to identify and solve the issue is critical. 

Traditional troubleshooting requires engineering teams to comb through dashboards, sift through alerts, and make educated guesses under pressure. But a growing wave of companies are rethinking that model, aiming to take humans out of the loop altogether when things go wrong. 

Causely, a New York-based software company, is helping to lead that shift. Their founder Shmuel Kliger has been developing systems for IT Operations for over two decades. He was also the founder of Turbonomic (acquired by IBM) and the CTO of SMARTS (acquired by EMC), bringing together technical experience with a track record of successfully scaling companies. 

Shifting from Observability to Causality 

“For years, the IT industry has struggled to make sense of the overwhelming amounts of data coming from dozens of observability platforms and monitoring tools,” said Yotam Yemini, CEO of Causely. The explosion of cloud computing and artificial intelligence has flooded engineering teams with more data than they can meaningfully act on. While observability platforms have made it easier to monitor system health, they often leave it to humans to connect the dots. 

“There’s no shortage of visibility anymore. The problem is no longer blindness, its paralysis,” said Yemini. “Knowing that something is wrong doesn’t mean knowing what to do about it.” 

A New Kind of Intelligence for Complex Systems 

Causely’s approach is based on a form of AI known as causal reasoning. Rather than relying on language models to interpret logs or alerts, the platform uses a structured framework (an ontology) to map out how systems relate to one another. That foundation allows the system to determine the most likely root cause of an issue, not just highlight symptoms. 

The company has positioned itself as an engine rather than a platform, emphasizing speed, precision, and its ability to work behind the scenes. It integrates with standard telemetry pipelines, allowing it to function as a drop-in enhancement to existing observability setups. 

When Speed Is Survival 

The need for automation is especially urgent in business-critical environments where seconds of downtime can translate into significant revenue loss. In areas like digital finance, high-frequency trading, or real-time commerce, lagging performance is often as damaging as a total outage. In these scenarios, waiting for a human to intervene is risky. 

Causely’s team argues that autonomous service reliability (the ability of systems to identify and respond to problems without manual intervention) is the next frontier in enterprise software. While the idea of self-healing systems dates back decades, actual implementation has lagged. One reason, according to Yemini, is the overreliance on hype-driven solutions. 

“There's a lot of fool’s gold in the market right now,” he said. “Many companies are taking unstructured data and throwing it at general-purpose AI tools, expecting those tools to make sense of it. That rarely works in complex systems.” 

Letting Machines Do What They Do Best 

The company is clear about its ambitions. Rather than cautiously framing its tools as assistive technology for engineers, Causely embraces the idea that machines can outperform humans in specific domains, especially in high-pressure diagnostics. 

This isn’t about eliminating engineering roles, Yemini says, but about elevating them. The more routine and reactive aspects of site reliability can be handed off to automated systems, freeing human talent to focus on higher-level strategic decisions. In that sense, the future of engineering may look more like product leadership or management (setting goals and constraints) than traditional debugging. 

Toward a Multi-Agent Future 

Causely’s vision reflects a broader shift in how software is built and maintained. As engineering organizations increasingly rely on AI agents to assist with coding, deployment, and performance monitoring, the ecosystem is moving toward a world of specialized, cooperative agents.  

The journey to that vision is still underway. But for a growing number of companies betting on speed, reliability, and autonomy, tools like Causely represent more than a technical upgrade. They’re a glimpse at a future where the software behind the scenes is just as intelligent as the product in users’ hands.