AI agents that automatically prevent, detect and fix software issues are here as NeuBird AI launches Falcon, FalconClaw

The phrase “move fast and break things,” often associated with Facebook (before its rebranding to Meta), encapsulates a long-standing tech industry ethos. However, with the evolution of enterprise infrastructure into complex networks of hybrid clouds, microservices, and temporary compute clusters, the disruptive side of this mantra has become too costly for many organizations. In response, the startup NeuBird AI, now two years old, is tackling this “chaos tax” by securing $19.3 million in funding and unveiling its Falcon autonomous production operations agent.

This release represents more than just a product update; it signals a shift in ideology. Traditionally, the industry has prioritized “Incident Response,” enhancing speed and capacity for addressing problems. NeuBird AI argues that the future lies in “Incident Avoidance.”

Venkat Ramakrishnan, President and COO of NeuBird AI, emphasized this perspective during a recent interview: “Incident management is so old school. Incident resolution is so old school. Incident avoidance is what is going to be enabled by AI”.

The company is leveraging AI to provide real-time enterprise context, moving site reliability engineering and DevOps teams from a reactive approach to a predictive one.

The AI divide: a reality check on automation

Alongside the launch, NeuBird AI has released its 2026 State of Production Reliability and AI Adoption Report. This survey of over 1,000 professionals highlights a significant disconnect between executives and engineers.

While 74% of C-suite executives believe their organizations are using AI to manage incidents, only 39% of the engineers on-call actually agree.

This 35-point “AI Divide” reveals that while leadership invests in AI platforms, the technology often fails to reach those on the front lines.

Engineers face a manual and demanding environment, as the study indicates that engineering teams dedicate an average of 40% of their time to incident management instead of developing new products.

Gou Rao, co-founder CEO of NeuBird AI, shared with VentureBeat that this is a consistent operational issue: “Over the past 18 months that we have been in production, this is not a marketing slide. We have concretely been able to demonstrate a massive reduction in time to incident response and resolution”.

The impact of this “toil” extends beyond lost productivity, as alert fatigue has evolved from a morale issue to a direct reliability risk.

The report indicates that 83% of organizations experience teams occasionally ignoring or dismissing alerts, and 44% of companies encountered an outage in the past year linked to a disregarded alert. Often, the systems are so inundated with noise that customers identify failures before the monitoring tools do.

Introducing NeuBird AI Falcon

NeuBird AI’s response to these systemic challenges is the Falcon engine. While the earlier version, Hawkeye, focused on autonomous resolution, Falcon advances this by incorporating predictive intelligence. “When we launched NeuBird AI in 2023, our first version of the agent was called Hawkeye,” Rao explains. “What we’re announcing next week at HumanX is our next-generation version of the agent, codenamed Falcon. Falcon is easily three times faster than Hawkeye and is averaging around 92% in confidence scores”.

This high level of accuracy allows engineers to confidently rely on the agent’s results. Falcon marks a notable improvement in generative AI applications, especially in its ability to predict failures. “Falcon is really good at preventive prediction, so it can tell you what can go wrong,” Rao says. “It’s pretty accurate on a 72-hour window, even better at 48 hours, and by 24 hours it gets really, really accurate”.

A key feature of the new release is the Advanced Context Map, which provides a real-time view of infrastructure dependencies and service health, unlike static dashboards. This tool helps teams visualize the “blast radius” of an issue as it spreads through an environment, enabling engineers to understand not only what is broken but also why it is failing in relation to its surroundings.

‘Minority Report’ for incident management

While many AI tools emphasize flashy web interfaces, NeuBird AI caters to developers’ natural preferences with NeuBird AI Desktop. This feature enables engineers to access the production ops agent directly from a command-line interface to investigate root causes and system dependencies.

“Falcon has a desktop mode which allows it to interact with a developer’s local tools,” Rao noted. “We’re getting a lot more traction from a hands-on developer audience, especially as people go to Claude Desktop and Cursor. They’re completing the loop by using production agents talking to their coding agents”.

This integration allows for a “multi-agent” workflow where engineers can use NeuBird AI’s agent to identify root causes in production and then pass that information to a coding agent like Claude Code to implement the fix.

In a live demonstration, Rao showcased how the agent can operate in “Sentinel Mode,” continuously scanning a cluster for risks. If it identifies an anomaly, such as a projected 5% increase in AWS costs or a misconfigured Kubernetes pod, it can alert the specific engineer on-call with the expertise to resolve it.

“This is like ‘Minority Report for Incident Management’,” one financial services executive reportedly commented after a demo.

Context engineering: a gateway for security

A primary concern for enterprises adopting AI is ensuring security, specifically that large language models do not malfunction or compromise sensitive data. NeuBird AI addresses this through its proprietary “context engineering” approach.

“The way we implemented our agent is that the large language models themselves are never actually touching the data directly,” Rao explains. “We become the gateway for how the context can be accessed”. This design means the model serves as the reasoning engine, while NeuBird AI acts as the intermediary that manages the data.

The company has also established strict controls on what the agent can execute. “We’ve created a language that confines and restricts the agent from what it can do,” says Rao. “If it comes up with something anomalous, or something we don’t know, it won’t run. We won’t do it”.

This architectural strategy allows NeuBird AI to remain model-agnostic. If a newer model from Anthropic or Google surpasses the current reasoning engine, NeuBird AI can seamlessly switch it out without requiring customers to alter their platform. “Customers don’t want to be tied to a specific way of reasoning,” Rao asserts. “They want to be tied to a platform from which they can get the value of an agentic system”.

Displacing the “army”: displacing expensive observability

NeuBird AI claims that agentic systems can significantly reduce the data storage needs of enterprises. Currently, teams depend on extensive storage platforms with intricate query languages.

“People use very complex observability tools like Datadog, Dynatrace, and Sysdig,” Rao says. “This is the norm today, which is why it takes an army of people to solve a problem. What we’ve been able to demonstrate with agentic systems is that you don’t need to store all that data in the first place”. By reasoning across raw data sources, the agent can discern which signals are irrelevant and which are crucial. This transition, Rao argues, “reduces human toil and effort while simultaneously reducing your reliance on these insanely expensive observability tools”.

The effectiveness of this “incident avoidance” strategy was recently demonstrated at Deep Health. Rao explains how their agent identified a systemic issue that traditional tools missed: “Our agent was able to go in and prevent an issue from happening which would have caused this company, Deep Health, a major production outage. The customer is completely beside themselves and happy about what it could do”.

FalconClaw: operationalizing ‘tribal knowledge’

A major challenge in IT operations is the retention of “tribal knowledge,” the valuable expertise held by senior engineers. NeuBird AI aims to address this with FalconClaw, a curated, enterprise-grade skills hub compatible with the OpenClaw ecosystem.

FalconClaw allows teams to document best practices and resolution steps as “validated and compliant skills”. The tech preview was launched today with 15 initial skills that integrate seamlessly with NeuBird AI’s toolchain.

According to Francois Martel, Field CTO at NeuBird AI, this approach transforms invaluable expertise into a reusable asset that the AI can automatically utilize.

This initiative seeks to standardize agent interactions with infrastructure, moving away from proprietary “black box” systems toward a multi-agent environment where various AI tools can share a common set of operational capabilities.

Scaling the moat: funding and leadership

The $19.3 million funding round was led by Xora Innovation, a firm backed by Temasek, with contributions from Mayfield, M12, StepStone Group, and Prosperity7 Ventures. This brings NeuBird AI’s total funding to roughly $64 million.

The interest from investors is largely driven by the founding team’s strong background. Gou Rao and Vinod Jayaraman previously co-founded Portworx, acquired by Pure Storage, and Ocarina Networks, acquired by Dell. They have recently strengthened their leadership team with Venkat Ramakrishnan, another Pure Storage veteran, as President and COO.

For investors like Phil Inagaki of Xora, the value lies in NeuBird AI’s “best-in-class results across accuracy, speed and token consumption”. As cloud costs continue to rise, the ability of an AI agent to not only resolve issues but also optimize infrastructure capacity is becoming essential. NeuBird AI claims its agent can save enterprise teams over 200 engineering hours per month.

The path to ‘self-healing’ infrastructure

According to the State of Production Reliability report, existing incident management practices are “no longer sustainable”. With 61% of organizations estimating that a single hour of downtime costs $50,000 or more, the financial consequences of remaining in a reactive cycle are substantial.

NeuBird AI’s introduction of Falcon and FalconClaw represents a determined effort to disrupt that cycle. By prioritizing prevention and the “context engineering” needed to make AI reliable for enterprise production, the company is positioning itself as the vital intelligence layer for the modern tech stack.

While the “AI Divide” between executives and practitioners poses a significant challenge for the industry, NeuBird AI is confident that as engineers recognize the benefits of a CLI-driven, 92%-accurate agent capable of predictive insights, skepticism will diminish. For site reliability engineers overwhelmed by a barrage of non-actionable alerts, the arrival of a dependable AI collaborator is highly anticipated.

NeuBird AI Falcon is now available, with organizations invited to register for a free trial at neubird.ai.