If you lead a software engineering team, you already know the core problem: your engineers have less bandwidth than your alert stream demands.
It usually begins on a Wednesday. A product engineer pushes a promising feature to staging and leans back only to be yanked forward by the first PagerDuty alert. The payment API is throwing 503s again. Slack buzzes with frantic threads. A performance degradation alert appears, demanding immediate investigation. By lunchtime, half of that feature work lies abandoned in a maze of broken tests and half-read runbooks. Every engineer who’s built customer facing software has lived this scene, because it’s the modern “normal” for product engineers in 2025.
A decade ago, writing business logic and shipping new features felt like a full day’s work. Today, the same engineer is expected to also be an on-call firefighter, a security analyst, a cost-optimization specialist, and a release coordinator. Minor alerts steal twenty minutes of focus, but real incidents like the ones requiring postmortems can consume entire days. Every monitoring dashboard adds another tab to the endless browser session. Deep work becomes a rare luxury and innovation grinds nearly to a halt.
Platform engineering teams try their best. They craft shared libraries for logging, golden paths for safe CI/CD, and unified dashboards that hide complexity. Those efforts ease some pain, but they don’t erase it. Each service has its own quirks, from legacy dependencies, bespoke configurations, to unpredictable load patterns, so when production falters, the engineer who wrote the code still bears the pager.
That relentless cycle takes a toll. In many teams, product engineers end up spending a significant portion of their time on production support. Context switches fragment the day into micro-tasks. Complexity compounds. Technical debt accumulates. Burnout looms. Products suffer because the people who know the code best are too busy chasing alerts to build the next great feature.
From that common frustration came Cleric. As veteran SREs and platform engineers, we'd witnessed countless application teams drowning in operational overhead. We'd spent years building monitoring systems, crafting runbooks, and optimizing alerts across Splunk, Grafana, and other tools. But we recognized that even perfect platform tooling wasn't enough. What teams needed, and need today more than ever, is an intelligent layer that can reason through incidents the way a senior SRE would.
The rise of observability tooling promised to democratize production monitoring, but instead created an unexpected paradox. More dashboards, better telemetry, and sophisticated alerting didn't liberate engineers; instead, they created new operational burdens. The painful truth became clear: product engineers don't actually want "easier on-call"; they want to minimize operational distractions and focus on building products. Platform engineers don't want to constantly field production issues; they want to build reliable infrastructure and effective abstractions.
This misalignment persisted because we've been missing a crucial component in our operational stack: an intelligent layer to make sense of tools like observability platforms and Kubernetes. We've built extensive instrumentation to detect problems, but left the cognitive work of investigation to already overburdened humans.
Cleric fills this gap as an AI SRE that activates when alerts fire. Behind simple interfaces (Slack messages that accelerate resolution and a UI built for evidence-based verification), it investigates, correlates, and reasons across noisy, distributed environments. Rather than forcing engineers to dig through logs or dashboards, it delivers clear findings backed by evidence, performing the systematic investigation that neither application nor platform teams want to handle:
It plugs into the tools your team already uses like Datadog, Prometheus, and Slack, without adding deployment complexity. Unlike rigid rule-based systems, it gets better with each investigation, learning from feedback and only interrupting when it's found something actionable. It doesn't mindlessly follow playbooks. Instead, it mirrors how experienced engineers think. Pragmatic, evidence-based, and focused on root causes.
When issues occur, engineers receive concise, actionable intelligence like: “Memory spike on node-17 due to an unbounded query in the reporting service.” The fix remains in their hands, but the detective work disappears.
The result rebalances responsibilities across the engineering organization. Application and product teams reclaim focus time for complex business logic and innovation. Platform teams can concentrate on architectural improvements instead of alert triage. Both groups benefit from an AI teammate that thinks like a senior SRE, handling the cognitive burden of incident response without requiring either team to compromise their core responsibilities.
We built Cleric because this imbalance is everywhere. Engineers are constantly yanked between building and firefighting, a cycle that doesn’t just waste time, it destroys the deep focus great software demands. After years of watching talented teams burn hours chasing logs instead of writing code, the rise of AI gave us a chance to fix that. Cleric automates the grunt work of incident investigation, giving engineers back what matters most: uninterrupted time to build, refactor, and think through complex problems without an alert derailing their flow every twenty minutes.
If this sounds familiar, the best way to see if Cleric helps is to run it on a few real alerts and see what it catches. Get in touch if you want to give it a try.