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 to a dizzying maze of hybrid clouds, microservices, and ephemeral compute clusters, "Breaking" Part of this has become a structural tax that many organizations are no longer able to pay. Today, two-year-old startup Newbird AI is launching a full-scale offensive against "create chaos," Announced a $19.3 million funding round alongside the release of its Falcon Autonomous Production Operations Agent.
The launch isn’t just a product update; This is a philosophical pivot. Over the years, the industry has focused on "incident response"-Making fire engines faster and making pipes bigger. Newbird AI is arguing that this is the only sustainable way forward "avoid incident".
As Venkat Ramakrishnan, President and COO of Newbird AI, said in a recent interview: "Event management is very old school. The solution to the incident is very old school. Incident prevention will be enabled by AI".
By grounding AI in a real-time enterprise context rather than just big language model logic, the company aims to shift site reliability engineering and bring teams from a reactive posture to a predictive one.
The AI Divide: A Reality Check on Automation
The launch is also accompanied by Newbird AI’s 2026 State of Production Reliability and AI Adoption Report, a survey of more than 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 practitioners—the engineers actually on call at 2:00 a.m.—agree.
This 35-point "a divide" Suggests that while leadership is writing checks for AI platforms, the technology is often failing to reach the front lines.
For engineers, the reality remains manual and difficult: The study found that engineering teams spend an average of 40% of their time on incident management rather than building new products.
CEO Gau Rao, co-founder of Newbird AI, told VentureBeat that this is an ongoing operational reality: “Over the last 18 months that we’ve been in production, this is not a marketing slide. We’ve been able to concretely demonstrate a huge reduction in incident response and resolution times”.
its side effects "Hard Labour" There are more than just lost productivity. Warning fatigue has turned from a morale issue to a direct credibility risk.
According to the report, 83% of organizations have teams that sometimes ignore or dismiss alerts, and 44% of companies have experienced an outage in the past year that is directly linked to suppressed or ignored alerts. In many cases, there is so much noise in the system that customers become aware of the failure before monitoring tools can detect it.
IIntroducing Newbird AI Falcon
Newbird AI’s answer to this systemic failure is the Falcon Engine. While the company’s previous version, Hawkeye, focused on autonomous resolution, the Falcon extends that capability into predictive intelligence. "When we launched Newbird AI in 2023, the first version of our agent was called HawkEye," Rao explains. "What we’re announcing at HumanX next week is a next-generation version of our agent, codenamed Falcon. Falcon is easily three times faster than Hawkeye and averages about 92% in confidence score".
This level of accuracy allows engineers to trust the agent’s output at face value. Falcon represents a significant leap forward compared to previous generative AI applications in the space, particularly in its ability to predict failure. "Falcon is really good at preventive prediction, so it can tell you what could go wrong," Rao says. "“It is quite accurate at 72 hours, even better at 48 hours, and at 24 hours it becomes very accurate indeed.”
One of the standout features of the new release is the advanced reference map. Unlike static dashboards, this is a real-time view of infrastructure dependencies and service health. It allows teams to visualize "blast radius" Learning about an issue as it spreads throughout the environment helps engineers understand not only 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 toward the developer’s native habitat with NeuBird AI Desktop. This allows engineers to invoke the Production Ops Agent directly from the command-line interface to explore root causes and system dependencies.
"Falcon has a desktop mode that allows it to interact with the developer’s native tools," Rao noted. "We’re getting a lot of traction from the hands-on developer audience, especially as people move to cloud desktops and cursors. They are completing the loop by talking to their coding agents using production agents.
This enables integration "multi agent" Workflow where an engineer can use Newbird AI’s agent to diagnose the root cause in production and then hand that diagnosis off to a coding agent like Cloud Code to implement the fix.
During a live demo, Rao showed how to set up the agent "sentinel mode," Constantly inspecting the cluster for risks. If it detects an anomaly – such as an estimated 5% increase in AWS costs or a misconfigured Kubernetes pod – it can flag a specific engineer on-call who has the domain expertise to fix it.
"It’s like ‘Minority Report’ for incident management," A financial services executive reportedly told the team after the demo.
Reference Engineering: A Gateway to Security
The primary concern for enterprises deploying AI is security – ensuring that large language models do not run "crazy" Or exclude sensitive data. Newbird AI addresses this through a proprietary approach "reference engineering".
"The way we implemented our agent is that the big language models never actually touch the data directly," Rao explains. "We become the gateway to how the context can be accessed.” This means that the model is the reasoning engine, but Newbird 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 limits and restricts what the agent can do," Rao says. "If it comes up with something unusual, or something we don’t know, it won’t work. We will not do this”.
This architectural choice allows Newbird AI to remain model-agnostic. If a new model from Anthropic or Google outperforms the current reasoning engine, Newbird AI can easily switch to it without requiring the customer to change their platform. "Customers do not want to be tied to any specific type of logic," Rao claims. "They want to be tied to a platform from which they can get the value of an agentic system.
to displace "Army": Displacing expensive observation capacity
One of Newbird AI’s most fundamental claims is that agentic systems can actually reduce the amount of data that enterprises store. Currently, teams rely on huge storage platforms with complex query languages.
"People use very complex observation 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 important. This change, Rao argues, reduces human labor and effort as well as your reliance on these extremely expensive observational instruments.
its practical effect "incident prevention" Was recently featured in Deep Health. Rao explains how his agent discovered a systemic problem that was invisible to traditional tools: “Our agent was able to go in and prevent a problem from happening that could have caused this company, Deep Health, a huge loss in production. The customer is absolutely beside himself and is happy with what he was able to do”.
Falconclaw: Operationalizing ‘Tribal Knowledge’
One of the most frequent problems in IT operations is the loss of "tribal knowledge"-The hard-earned expertise of senior engineers that exists only in their minds. Newbird 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 solution steps "valid and compliance skills". The technology preview launches today with 15 introductory skills that work natively with Newbird AI’s toolchain.
According to Francois Martel, Field CTO of Newbird AI, this turns hard-earned expertise into reusable assets that AI can use automatically.
This is an effort to standardize how agents interact with the infrastructure by moving away from proprietary "black box" Systems toward a multi-agent world where different AI tools can share a common set of operational capabilities.
Bridging the gap: funding and leadership
The $19.3 million round was led by Temasek-backed firm Zora Innovation, with participation from Mayfield, M12, Stepstone Group and Prosperity7 Ventures. This brings Newbird AI’s total funding to approximately $64 million.
Investor interest was largely driven by the pedigree of the founding team. Gau Rao and Vinod Jayaraman previously co-founded Portworx, which was acquired by Pure Storage, and Ocarina Networks, which was acquired by Dell. He recently strengthened his leadership by appointing Venkat Ramakrishnan, another Pure Storage veteran, as President and COO.
For investors like Zora’s Phil Inagaki, the value lies in Newbird AI "Best-in-class results in accuracy, speed and token consumption". As cloud costs continue to rise, so does the AI agent’s ability to not only fix bugs but also optimize infrastructure capacity "must have" instead of "good for". Newbird AI claims its agent can save enterprise teams more than 200 engineering hours per month.
The path to ‘self-healing’ infrastructure
As stated in the Production Reliability Report report, current incident management practices are "no longer sustainable". With 61% of organizations estimating that one hour of downtime costs $50,000 or more, the financial risk of staying in the reactive loop is huge.
The launch of Falcon and FalconClaw by Newbird AI is a definite attempt to break that loop. By focusing on prevention and "reference engineering" Essential to making AI trustworthy for enterprise production, the company is positioning itself as the critical intelligence layer to the modern stack.
When "a divide" remains a significant hurdle for the industry among executives and practitioners, Newbird AI is betting that engineers see the value of a CLI-driven, 92%-accurate agent that can "Look around the corners," Doubts will disappear. For site reliability engineers currently inundated with non-actionable alerts, the arrival of a trusted AI teammate couldn’t come soon enough.
Newbird AI Falcon is available today, organizations can sign up for a free trial at newbird.ai.
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