Sonarly: AI Engineer for Production Incidents

In an era where software systems operate at enormous scale and speed, reliability has become as critical as innovation. Sonarly, a San Francisco–based startup founded in 2025 and backed by Y Combinator’s Winter 2026 batch, emerged to tackle one of the most persistent pain points in modern engineering: production incidents. With a compact team of two founders, Dimittri Choudhury and Alexandre Klobb, Sonarly positions itself as an “AI engineer” dedicated to triaging and fixing production alerts automatically.

The company was born from a simple observation: while AI coding tools have dramatically accelerated software development at build time, the run-time phase — where software actually operates in production — remains chaotic. Engineering teams still grapple with dashboards flooded with alerts, fragmented logs, and incomplete context when things break. Sonarly aims to bring the same automation revolution to production environments that coding assistants brought to development.

At its core, Sonarly is a control layer for monitoring and incident response. It connects to existing observability tools such as Sentry and Datadog, as well as communication channels like Slack and Discord, acting as an intelligent intermediary that understands which alerts matter and what should happen next. By doing so, the startup seeks to transform production from a reactive firefighting exercise into a proactive, largely autonomous system.

Why Is Production Still a Mess in the Age of AI Coding Tools?

Modern software engineering has experienced a productivity explosion thanks to AI-assisted development. Developers can now generate code, tests, and documentation at unprecedented speed. However, once that code is deployed, organizations still rely on traditional monitoring practices that were designed for a pre-AI era.

Production environments generate massive volumes of telemetry: logs, metrics, traces, user feedback, and alerts. The problem is not the lack of data but the lack of clarity. Engineers frequently encounter alert fatigue — a state where the sheer number of notifications makes it difficult to distinguish real emergencies from noise. Critical incidents can hide among false positives, duplicates, or low-priority warnings.

Furthermore, incident resolution depends heavily on human availability. When an alert fires at night or during a busy release cycle, teams must wait for the on-call engineer to investigate. Even once someone is available, they must manually gather context from multiple tools, correlate signals, and diagnose the root cause. This process inflates Mean Time to Repair (MTTR), a key metric that directly impacts user satisfaction and business outcomes.

Sonarly’s founders argue that the fundamental issue is contextual blindness. Coding agents excel during development because they have access to the codebase and clear instructions. In production, however, they lack a unified understanding of the system’s real-time state. Without that context, automation stalls precisely where it is needed most.

How Does Sonarly Act as an Autonomous On-Call Engineer?

Sonarly’s primary innovation lies in positioning itself as an autonomous on-call engineer that operates continuously in the background. Rather than replacing human engineers, the platform augments them by handling the most time-consuming aspects of incident response.

When an alert occurs, Sonarly ingests signals from across the monitoring stack. It deduplicates repeated alerts, filters out false positives, and prioritizes issues based on severity and potential user impact. Only actionable alerts are forwarded to the engineering team, significantly reducing noise.

But triage is only the beginning. Sonarly then launches an AI coding agent equipped with comprehensive production context. This agent investigates the incident by analyzing logs, metrics, traces, user reports, and the relevant sections of code. By synthesizing these data sources, it identifies likely root causes and proposes — or even deploys — fixes.

The system continuously updates its understanding of the production environment after each incident. This learning loop ensures that future alerts are handled faster and more accurately. Over time, the AI becomes increasingly familiar with the system’s architecture, dependencies, and historical failure patterns.

The result is a shift from reactive firefighting to proactive reliability engineering. Instead of waking engineers in the middle of the night, Sonarly begins the investigation immediately, often resolving issues before users notice them.

What Problem Does Sonarly Solve for Engineering Teams?

The startup focuses on reducing MTTR, which is widely recognized as one of the most important reliability metrics. Every minute a bug remains unresolved can translate into lost revenue, frustrated users, and reputational damage. Conversely, rapid resolution can strengthen user trust by demonstrating responsiveness.

Sonarly identifies two major bottlenecks that inflate repair times. The first is signal discovery. Noisy alerts obscure the true cause of incidents, delaying diagnosis. The second is execution delay. Even once the problem is understood, teams must wait for the appropriate engineer to become available and then spend hours investigating.

By automating both phases, Sonarly compresses the entire incident lifecycle. It surfaces the right signal instantly and initiates the investigation without human intervention. This approach allows organizations to maintain high reliability even with lean engineering teams — a particularly valuable advantage for startups and fast-moving product companies.

Additionally, Sonarly helps maintain alignment between deployed code and monitoring systems. As teams release updates, alert configurations and investigative workflows can quickly become outdated. The platform automatically adjusts its internal representation of the system, ensuring that its analysis reflects the current production state.

Who Are the Founders Behind Sonarly?

The story of Sonarly is closely tied to the unconventional journeys of its founders. Dimittri Choudhury moved from a small village in France to Paris to study computer science, where he launched an earlier startup that reached over 100,000 users in just six months. That experience convinced him of the power of rapid execution — and the necessity of reliable infrastructure to support growth.

Alexandre Klobb began freelancing as a teenager, initially specializing in web scraping before evolving into a full-stack developer and AI engineer. He later co-founded a career guidance platform that served tens of thousands of students navigating the French education system. Both founders share a pattern of leaving traditional academic paths to pursue entrepreneurial ventures.

Their combined background in large-scale consumer applications exposed them to the operational chaos that accompanies rapid growth. Repeated encounters with production incidents and alert fatigue shaped their conviction that monitoring itself needed reinvention.

By building Sonarly, they aim to provide coding agents with the production context necessary to operate effectively at run time — closing the gap between development automation and operational reliability.

How Does Sonarly Learn and Improve Over Time?

A defining feature of Sonarly’s approach is continuous learning. Traditional monitoring tools rely on static rules and thresholds, which often fail to adapt to evolving systems. Sonarly instead constructs a dynamic representation of the production environment that updates after every alert and resolution.

This representation incorporates architectural dependencies, historical incidents, code changes, and user feedback. When a new alert appears, the platform can immediately place it within this contextual framework, accelerating diagnosis.

The learning mechanism also helps prevent recurrence. By understanding why past incidents occurred and how they were resolved, the AI can recommend preventive measures or adjust alerting strategies. Over time, the system shifts from reactive repair to predictive maintenance.

Such capabilities hint at a future where production environments are largely self-healing. While Sonarly does not claim to eliminate human oversight, it moves toward a model where engineers focus on innovation rather than constant firefighting.

What Could Sonarly Mean for the Future of Software Reliability?

If successful, Sonarly represents a broader shift in software engineering toward autonomous operations. Just as cloud infrastructure abstracted away hardware management, AI-driven reliability tools may abstract away incident response.

For organizations, this could mean faster release cycles, reduced operational costs, and improved user experiences. For engineers, it could reduce burnout associated with on-call duties and midnight emergencies.

The startup also highlights a growing recognition that AI’s impact on software development is incomplete without corresponding advances in production management. As applications become more complex and distributed, manual monitoring approaches will struggle to keep pace.

Sonarly’s vision suggests a future where production systems are continuously monitored, understood, and repaired by intelligent agents operating alongside human teams. In that world, reliability becomes an automated property rather than a constant struggle.

Why Are Investors and Accelerators Paying Attention?

Participation in Y Combinator’s Winter 2026 batch signals strong investor interest in the operational AI space. Startups that address reliability, security, and infrastructure challenges are increasingly viewed as foundational to the next generation of software companies.

Sonarly’s narrow focus on production alerts — a problem experienced by nearly every engineering team — gives it a potentially broad market. Its integration-first approach allows organizations to adopt the platform without replacing existing monitoring tools, lowering barriers to entry.

Moreover, the concept of an AI engineer dedicated to run-time operations resonates with a trend toward specialized AI agents embedded within enterprise workflows. If coding agents transformed development, operational agents may transform maintenance.

Conclusion: Is Sonarly Building the Missing Piece of the AI Software Stack?

Sonarly positions itself as the missing counterpart to AI coding assistants — an AI engineer that operates after deployment rather than before it. By triaging alerts, investigating root causes, and fixing bugs automatically, the startup aims to bring order to the chaos of production environments.

Its founders’ backgrounds in rapid-growth startups, combined with the support of a major accelerator, provide momentum behind this vision. While the company is still in its early stages, its approach addresses a universally recognized pain point in software engineering.

If Sonarly succeeds, it could redefine how organizations think about reliability, shifting from reactive incident management to proactive, AI-driven resilience. In doing so, it may help software teams deliver on a long-standing promise: building products that simply work — even when no one is watching.