Building Agentic AI from Hype to Scalable Reality: Wingspan’s Vision, Explained

As enterprises race to harness the power of artificial intelligence, one of the biggest obstacles remains the same: turning vast, complex data ecosystems into reliable, actionable insights. Onix, a cloud and AI services firm, believes it has found a path forward.

At Google Cloud Next, the company unveiled Wingspan, a new platform built on what it calls “agentic AI”—an architecture powered by autonomous, purpose-built agents designed to automate key stages of the data-to-AI lifecycle. In an interview with Dataquest, Onix Chief Technology Officer Niraj Kumar explains how Wingspan was developed to bridge the gap between abundant data and real AI impact—without cutting corners on governance, accuracy, or control.

Excerpts from an interview:

Agentic AI Is Trending. What Makes Wingspan Truly Agentic?

There’s a lot of buzz around agentic AI, but we’re not just talking about it—we’re building it. Wingspan is the missing link between raw enterprise data and actual AI impact. It features over 18 autonomous, purpose-built agents that do more than assist—they plan, execute, optimize, and learn.

Each agent is specialized across the data-to-AI lifecycle: from discovery and migration planning to transforming legacy code, validating quality, generating synthetic data, and even deploying domain-specific LLMs. These agents operate independently yet cohesively, delivering outcomes at enterprise scale.

One financial services firm, for example, used Wingspan to assess and migrate legacy data systems, automatically convert thousands of ETL scripts to modern formats, and generate synthetic training datasets to bypass regulatory hurdles. That’s what agentic means in practice—automation with judgment.

How Does Wingspan Maintain Accuracy Across Disparate, Messy Data?

Context is critical—and often overlooked. Wingspan uses a context engine, driven by our Eagle agent, to build a detailed knowledge graph that maps lineage, relationships, and business meaning across all sources.

This isn't just a data catalog. Agents trace dependencies, monitor KPI drift, validate model output, and ensure regulatory alignment in real time. The observability layer flags issues before they become errors, ensuring AI models stay grounded in their intended context.

AI in Four Weeks Sounds Bold. What Are the Trade-Offs?

Speed without stability is dangerous, and we’re very aware of that. The reason Wingspan can get AI into production 2–3x faster—often in under four weeks—is because of automation, not compromise.

Our agents handle the most tedious and failure-prone parts of the pipeline: data prep, code transformation, validation, synthetic data creation, and deployment. Tools like Pelican ensure real-time quality checks, while Phoenix AI Studio continuously tunes model performance. Governance and security aren’t bolted on—they’re built in.

This approach reduces—not increases—long-term risk by minimizing technical debt and manual intervention.

Why Build Proprietary Tools Like Raven and Kingfisher?

Open-source tools are valuable, but they often fall short when it comes to enterprise-specific challenges. We built technologies like Raven, which automates complex code and ETL transformations, and Kingfisher, which generates high-fidelity synthetic data for regulated environments, because no existing tools could meet the depth or precision we required.

This wasn’t about control—it was about necessity. We needed tools that were deeply integrated with our agents and future-proofed for where AI is going.

Can Enterprises Trust Autonomous Agents?

Every Wingspan agent is designed with enterprise-grade trust in mind. We’ve built extensive validation frameworks, traceable decision paths, and continuous monitoring into every layer.

Explainability isn’t optional. Actions are logged, decisions are auditable, and fallback mechanisms are built in. Wingspan complies with strict access controls, governance protocols, and data protection standards—whether it’s finance, healthcare, or government.

Wingspan Is Tied to Google Cloud. Is There Lock-In?

Wingspan is optimized for Google Cloud but built for flexibility. Its modular architecture supports hybrid and multi-cloud deployments by design. Our pluggable agent interfaces integrate easily with AWS, Azure, on-premise systems—whatever the stack.

There’s no vendor lock-in. In fact, we often help customers orchestrate data workflows across cloud platforms. Freedom and interoperability are baked into the product.

Some say Wingspan is just another shiny label in a crowded AI market. How do you respond?

That skepticism is fair—there’s a lot of noise out there. But Wingspan isn’t a rebrand. It’s based on proprietary IP, refined through years of enterprise deployments. We’ve worked with over 200 large-scale organizations, and the platform reflects that experience.

This isn’t a generic toolkit. Wingspan includes over 18 specialized agents that solve real, specific problems—from transforming legacy pipelines to deploying AI in regulated industries. The depth and maturity of the technology are what set us apart. It’s not hype—it’s a platform built to deliver results, not just demos.

Has Wingspan ever failed? What did you learn from that?

Of course. Early in development, we hit a wall trying to build a “universal” problem-solving approach for all use cases. That didn’t work—enterprise contexts vary too much.

But that friction led to a major breakthrough: our decision-making engine, which now allows agents to dynamically select the most efficient and accurate solution paths based on context. What was once a bottleneck became one of Wingspan’s most important differentiators—adaptive, intelligent execution.

How do you measure ROI for a platform like this?

We look at both hard numbers and operational impact. Wingspan typically delivers 20–35% in cost savings, with payback in 3–6 months. But the deeper ROI is in productivity, agility, and innovation.

Clients report 15–40% gains in employee efficiency, 70% reductions in cycle times, and 3–5x faster data-driven decisions. AI initiatives move from concept to production in weeks, not quarters. And we’re seeing real improvements in customer experience, including faster response times and 20–30% higher satisfaction scores.

So yes, it’s dollars and cents—but it’s also speed, scale, and strategic advantage.

What’s next now that Wingspan has launched at Google Cloud Next?

The launch is just step one. We’re already onboarding early customers in finance, retail, and healthcare, with KPIs tied to time-to-insight, cost savings, and deployment velocity.

We’ve also formed a customer advisory board to ensure our roadmap stays grounded in real use. This isn’t a fire-and-forget product—we’re continuously updating agent capabilities and measuring live performance.

Our accountability is ongoing. We’ll be publishing benchmarks, hosting partner showcases, and reporting outcomes regularly. This is a long-term play, and we’re committed to proving that Wingspan delivers—at scale, and over time.