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

The mantra of the modern tech industry was arguably coined by Facebook (before it became Meta): "move fast and break things."

But as enterprise infrastructure has shifted into a dizzying maze of hybrid clouds, microservices, and ephemeral compute clusters, the "breaking" part has become a structural tax that many organizations can no longer afford to pay. Today, two-year-old startup NeuBird AI is launching a full-scale offensive against this "chaos tax," announcing a $19.3 million funding round alongside the release of its Falcon autonomous production operations agent.

The launch isn't just a product update; it is a philosophical pivot. For years, the industry has focused on "Incident Response"—making the fire trucks faster and the hoses bigger. NeuBird AI is arguing that the only sustainable path forward is "Incident Avoidance".

As Venkat Ramakrishnan, President and COO of NeuBird AI, put it in 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".

By grounding AI in real-time enterprise context rather than just large language model reasoning, the company aims to move site reliability engineering and devops teams from a reactive posture to a predictive one.

The AI divide: a reality check on automation

Accompanying the launch is NeuBird AI’s 2026 State of Production Reliability and AI Adoption Report, a survey of over 1,000 professionals that reveals a massive disconnect between the boardroom and the server room.

While 74% of C-suite executives believe their organizations are actively using AI to manage incidents, only 39% of the practitioners—the engineers actually on-call at 2:00 AM—agree.

This 35-point "AI Divide" suggests that while leadership is writing checks for AI platforms, the technology is often failing to reach the frontline.

For engineers, the reality remains manual and grueling: the study found that engineering teams spend an average of 40% of their time on incident management rather than building new products.

Gou Rao, co-founder CEO of NeuBird AI, told VentureBeat that this is a persistent operational reality: “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 consequences of this "toil" are more than just lost productivity. Alert fatigue has transitioned from a morale issue to a direct reliability risk.

According to the report, 83% of organizations have teams that ignore or dismiss alerts occasionally, and 44% of companies experienced an outage in the past year tied directly to a suppressed or ignored alert. In many cases, the systems are so noisy that customers discover failures before the monitoring tools do.

Introducing NeuBird AI Falcon

NeuBird AI’s answer to this systemic failure is the Falcon engine. While the company’s previous iteration, Hawkeye, focused on autonomous resolution, Falcon extends that capability into 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 level of accuracy allows engineers to trust the agent's output at face value. Falcon represents a significant leap over previous generative AI applications in the space, particularly in its ability to forecast failure. "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”.

One of the standout features of the new release is the Advanced Context Map. Unlike static dashboards, this is a real-time view of infrastructure dependencies and service health. It allows teams to visualize the "blast radius" of an issue as it propagates across an environment, helping engineers understand not just what is broken, but why it is failing in the context of its neighbors.

'Minority Report' for incident management

While many AI tools favor flashy web interfaces, NeuBird AI is leaning into the developer's native habitat with NeuBird AI Desktop. This allows engineers to invoke the production ops agent directly from a command-line interface to explore 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 enables a "multi-agent" workflow where an engineer can use NeuBird AI’s agent to diagnose a root cause in production and then hand off that diagnosis to a coding agent like Claude Code to implement the fix.

During a live demo, Rao showcased how the agent could be set to "Sentinel Mode," constantly sweeping a cluster for risks. If it detects an anomaly—such as a projected 5% spike in AWS costs or a misconfigured Kubernetes pod—it can flag the specific engineer on-call who has the domain expertise to fix it.

"This is like 'Minority Report for Incident Management'," one financial services executive reportedly told the team after a demo.

Context engineering: a gateway for security

A primary concern for enterprises deploying AI is security—ensuring large language models don't go "crazy" or exfiltrate sensitive data. NeuBird AI addresses this through a proprietary approach to "context engineering".

"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 means the model is the reasoning engine, but NeuBird AI is the middleman that wraps the data.

Furthermore, the company has implemented strict guardrails on what the agent can actually 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 choice allows NeuBird AI to remain model-agnostic. If a newer model from Anthropic or Google outperforms the current reasoning engine, NeuBird AI can simply switch it out without requiring the customer to change 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

One of the most radical claims NeuBird AI makes is that agentic systems can actually reduce the amount of data enterprises need to store in the first place. Currently, teams rely on massive storage platforms with complex 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”. Because the agent can reason across raw data sources, it can identify which signals are junk and which are critical. This shift, Rao argues, “reduces human toil and effort while simultaneously reducing your reliance on these insanely expensive observability tools”.

The practical impact of this "incident avoidance" was recently demonstrated at Deep Health. Rao recounts how their agent detected a systemic issue that was invisible to traditional tools: “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'

One of the most persistent problems in IT operations is the loss of "tribal knowledge"—the hard-won expertise of senior engineers that exists only in their heads. NeuBird AI is attempting to solve this with FalconClaw, a curated, enterprise-grade skills hub compatible with the OpenClaw ecosystem.

FalconClaw allows teams to capture best practices and resolution steps as "validated and compliant skills". The tech preview launched today with 15 initial skills that work natively with NeuBird AI’s toolchain.

According to Francois Martel, Field CTO at NeuBird AI, this turns hard-won expertise into a reusable asset that the AI can use automatically.

It’s an attempt to standardize how agents interact with infrastructure, moving away from proprietary "black box" systems toward a multi-agent world where different AI tools can share a common set of operational abilities.

Scaling the moat: funding and leadership

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

The investor interest is fueled largely by the pedigree of the founding team. Gou Rao and Vinod Jayaraman previously co-founded Portworx, which was acquired by Pure Storage, and Ocarina Networks, acquired by Dell. They have recently bolstered their leadership 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 spiral, the ability of an AI agent to not only fix bugs but also optimize infrastructure capacity is becoming a "must-have" rather than a "nice-to-have". NeuBird AI claims its agent can save enterprise teams more than 200 engineering hours per month.

The path to 'self-healing' infrastructure

As the State of Production Reliability report notes, current 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 stakes of staying in a reactive loop are enormous.

NeuBird AI's launch of Falcon and FalconClaw marks a definitive attempt to break that loop. By focusing on prevention and the "context engineering" required to make AI trustworthy for enterprise production, the company is positioning itself as the critical intelligence layer for the modern stack.

While the "AI Divide" between executives and practitioners remains a significant hurdle for the industry, NeuBird AI is betting that as engineers see the value of a cli-driven, 92%-accurate agent that can "see around corners," the skepticism will fade. For the site reliability engineers currently drowning in a flood of non-actionable alerts, the arrival of a reliable ai teammate couldn't come soon enough.

NeuBird AI Falcon is available starting today, with organizations able to sign up for a free trial at neubird.ai.