IncidentFox: The AI SRE That Fixes Incidents Fast

In an era when software systems underpin nearly every industry, production reliability has become a board-level concern. Downtime can cost millions, damage reputation, and erode user trust in minutes. Yet the responsibility for preventing and resolving incidents still falls largely on Site Reliability Engineering (SRE) teams — professionals who must diagnose complex failures across sprawling, interconnected systems, often under extreme time pressure.

IncidentFox emerged in 2025 to address a persistent bottleneck in this domain: the gap between artificial intelligence capabilities and the deep, organization-specific context required to resolve real production incidents. While many AI tools promise automated incident response, most fail when confronted with the messy reality of custom infrastructure, proprietary tooling, and siloed team knowledge.

Founded by Jimmy Wei and Long Yi, both former engineers at Roblox who had firsthand experience operating at massive scale, IncidentFox set out to create an AI SRE agent that behaves less like a generic chatbot and more like an embedded engineer who truly understands a company’s systems. Their insight was simple but powerful: AI cannot fix what it cannot see, and in most organizations, the necessary visibility is locked behind integrations that are costly and time-consuming to build.

By positioning integrations — not models — as the real obstacle, IncidentFox reframed the entire problem space of AI-driven reliability engineering.

Why Do Traditional AI SRE Tools Fall Short?

Most existing AI operations tools follow a predictable pattern. They connect to common platforms such as Slack, incident trackers, monitoring dashboards, and documentation systems. These integrations create the illusion of situational awareness, but when a real incident occurs, the AI often lacks access to the specialized tools needed to investigate deeply.

Modern engineering organizations rarely operate on standardized stacks. One team may run a custom Kafka pipeline with internal dashboards, another may rely on a proprietary deployment system, and a machine learning group might use a unique model-serving architecture. These systems are invisible to generic AI assistants.

The conventional solution has been to build custom integrations — sometimes called MCP servers — for each internal system. However, this approach introduces new challenges. It requires engineering time, ongoing maintenance, and cross-team coordination. Worse, it assumes that teams know in advance which integrations will be necessary during an incident, which is rarely the case.

When incidents occur at inconvenient hours, engineers often find themselves debugging unfamiliar services owned by other teams. Without the right tools, even highly skilled responders waste precious time gathering context instead of resolving the issue.

IncidentFox identified this integration gap as the true limiting factor in automated incident response. The founders recognized that until integrations could be created dynamically and intelligently, AI SRE tools would remain only partially effective.

How Does IncidentFox Automatically Learn Each Customer’s System?

IncidentFox’s defining capability is its ability to auto-discover a company’s technical environment and generate the integrations needed to operate within it. Instead of requiring engineers to manually configure access to every internal tool, the system analyzes codebases, infrastructure patterns, and historical incident data to identify missing capabilities.

Once it detects gaps — for example, the inability to query a particular service’s health endpoint — IncidentFox generates the necessary integration automatically. Human approval remains part of the process, ensuring that security and governance requirements are respected, but the heavy lifting is handled by the AI agent itself.

This approach transforms onboarding from a months-long engineering project into a process that can be completed in less than a day. Teams adopting IncidentFox do not start from scratch, either. The platform ships with more than 300 prebuilt integrations covering widely used technologies such as Kubernetes, AWS, Grafana, Prometheus, Datadog, Elasticsearch, PagerDuty, and GitHub.

By combining auto-generation with a substantial built-in library, IncidentFox positions itself as both adaptive and immediately useful.

Why Is Per-Team Configuration Essential for Large Organizations?

One of the most overlooked realities of enterprise engineering is that different teams operate almost like separate companies. Their tools, workflows, terminology, and priorities vary dramatically. A payments team concerned with transaction integrity has little overlap with a machine learning group optimizing model performance.

IncidentFox addresses this fragmentation through per-team configuration. Each team receives a tailored AI SRE instance with its own set of tools, prompts, and knowledge base. Engineers can inject domain-specific knowledge directly into the system, ensuring that the agent understands the nuances of their services.

This design acknowledges that reliability engineering is not a one-size-fits-all discipline. What constitutes “config drift” for one team might represent intentional experimentation for another. By respecting these differences, IncidentFox avoids the common pitfall of centralized tools that impose rigid workflows on diverse teams.

The result is an AI agent that feels local rather than external — an assistant that speaks each team’s language and understands their unique operational context.

Can an AI SRE Truly Improve Itself Over Time?

Perhaps the most ambitious aspect of IncidentFox is its self-improvement loop. After every incident, the system evaluates its own performance against the actual resolution. If it failed to access critical data or missed a diagnostic step, it identifies the deficiency and generates new capabilities to address it.

This continuous learning process mirrors how human engineers gain expertise through experience. Instead of relying on manual updates or periodic retraining, IncidentFox evolves organically as it participates in incident response.

The agent also updates its prompts and knowledge base, subject to human review, ensuring that improvements are both measurable and trustworthy. Over time, organizations using IncidentFox can expect the system to become increasingly aligned with their operational realities.

Such adaptability is particularly valuable in fast-moving environments where infrastructure changes frequently. Traditional tools often become outdated as systems evolve, but IncidentFox is designed to evolve alongside them.

What Tangible Results Does IncidentFox Deliver?

Early metrics reported by the company suggest substantial improvements in incident management efficiency. Alert noise — the overwhelming flood of notifications that engineers must sift through — can be reduced by as much as 85 to 95 percent through intelligent correlation of related signals.

Investigation times that once stretched across hours can shrink to minutes, enabling faster mitigation and reduced downtime. Onboarding is similarly streamlined, with deployments ranging from a five-minute Docker setup to a production Kubernetes rollout in approximately half an hour.

These gains translate into more than operational convenience. They directly impact business continuity, customer satisfaction, and engineering morale. By automating the most repetitive and stressful aspects of incident response, IncidentFox allows human engineers to focus on strategic improvements rather than constant firefighting.

How Does IncidentFox Balance Enterprise Requirements and Open-Source Flexibility?

Enterprise adoption of AI tools often hinges on trust, security, and compliance. IncidentFox addresses these concerns by offering SOC 2 compliance, role-based access control, audit logging, and support for single sign-on systems. Organizations can deploy the platform as self-hosted, on-premises, or managed SaaS, depending on their security posture.

At the same time, the company has embraced an open-source model under the Apache 2.0 license. This decision reflects a belief that transparency fosters trust and accelerates innovation. Engineers can inspect prompts, modify configurations, and integrate their preferred large language models using their own API keys.

By avoiding vendor lock-in, IncidentFox appeals to organizations wary of committing their reliability workflows to a proprietary ecosystem.

Who Are the Founders Behind IncidentFox?

The founding team’s credibility stems from their experience operating at one of the world’s largest gaming platforms. Jimmy Wei previously worked as a software engineer at Roblox, contributing to communication systems used by over 100 million daily active users. His background also includes research in conversational AI and leadership roles in earlier startups.

Long Yi, who serves as the company’s technical leader, built database infrastructure at Roblox and experienced the realities of on-call duty firsthand. His perspective as an SRE navigating constant incidents shaped the product’s design philosophy.

Together, they represent the convergence of AI expertise and operational experience. IncidentFox can be seen as the product of both perspectives: the engineer building intelligent systems and the engineer relying on them during crises.

What Does the Future Hold for AI-Driven Reliability Engineering?

IncidentFox’s vision extends beyond automating incident response. It points toward a future in which AI agents become integral members of engineering teams — learning, adapting, and collaborating alongside humans. As systems grow more complex and distributed, the need for such assistance will only intensify.

If the company succeeds, the role of SREs may shift from reactive troubleshooting to proactive system design, with AI handling routine diagnostics and coordination. Incidents could become less disruptive, more predictable, and easier to resolve.

The broader implication is a transformation in how organizations think about operational resilience. Rather than treating reliability as a constant struggle against entropy, companies may begin to see it as a continuously optimized process powered by intelligent agents.

Why Might IncidentFox Represent a Turning Point in AI Operations?

By focusing on integrations as the missing link in AI effectiveness, IncidentFox challenges prevailing assumptions about what makes automation successful. Its approach suggests that intelligence alone is insufficient without access to the right tools and context.

In doing so, the startup positions itself not merely as another monitoring solution but as a new category of operational partner — an AI SRE that never loses context, never tires, and improves with every incident.

As engineering organizations search for ways to maintain reliability at scale, IncidentFox’s experiment will be closely watched. Whether it becomes a standard component of the modern tech stack or inspires a wave of similar innovations, its core idea — that AI should adapt to systems rather than forcing systems to adapt to AI — may prove to be its most enduring contribution.