The Invisible Layer Holding Up The AI Boom And How An Expert Is Building It

Every few months, enterprise software discovers a new obsession. Right now it is AI agents — programs that don't just answer questions but take actions: updating a CRM record, triggering a workflow, moving data between systems. The demos are impressive. The problem is what the demos hide.

An AI agent is only as good as the data it can see and the systems it can touch. And for most companies, that data lives scattered across dozens of third-party tools — Salesforce, HubSpot, Slack, project trackers, payment systems — none of which the AI application controls. Before an agent can act intelligently, someone has to build the plumbing that connects it to all of them. In integration-heavy SaaS, that plumbing routinely eats weeks or months of engineering time that should be going into the actual product.

It is unglamorous work, which is precisely why the engineers who do it well end up mattering so much. Anand Chaudhary is one of them.

From campus PHP projects to airline infrastructure

Chaudhary's career has followed a pattern that predates the AI era entirely. While completing his computer science degree in 2017, he was already shipping production software — a quiz platform for his campus, a social networking app for students, and britishlingua.com, a web product he launched as a student that remains live today. By graduation, he had more shipped software behind him than many working developers.

His first major test came at ThinkSys, where he architected the infrastructure behind BlueRibbonBags, a baggage insurance product sold to airline passengers at the point of travel. The system had to hold together across airline platforms, payment processors, and customer applications, each with its own data formats and failure modes. It now runs with major carriers including AirAsia, IndiGo, and Cleartrip — airlines that together serve tens of millions of passengers a year.

The lesson he took from it was structural, not technical: understand what a complex system actually demands before writing code, then build infrastructure that survives real-world conditions rather than lab conditions.

Billions of executions, none of them visible

In April 2023, Chaudhary joined Paragon, the integration infrastructure platform for AI and B2B products, as a tech lead. By March 2025 he was principal engineer — a climb from tech lead to principal in under two years, a progression that typically takes far longer in the industry. He now oversees the architecture, stability, monitoring, and scalability of Paragon's core platform services.

His signature work there is the Workflow Engine, the system at the heart of Paragon's Workflows product. Its job is what engineers call durable execution: making sure software events process reliably even when third-party APIs throttle requests or networks drop connections mid-run. That means maintaining execution state through failures and replaying incomplete processes without duplicating outcomes — problems that sound simple and are anything but.

The engine now powers billions of event executions across Paragon's customer base. Each one is a workflow running inside someone else's product, which means the infrastructure Chaudhary helped build is a layer that other companies' applications quietly depend on to function.

"When systems scale, the early foundation is very important," Chaudhary has said. "Write a clear spec, define the contract, and instrument the outcomes."

The AI problem nobody demos

When AI reshaped enterprise software, it didn't eliminate the integration problem. It multiplied it. Agents act autonomously on whatever data they can access — and the quality of the action depends entirely on the quality of the data pipeline behind it.

Chaudhary architected Managed Sync, Paragon's data ingestion product built specifically for AI agents, handling the pipeline between the third-party tools where user data lives and the AI systems that consume it as context. Its counterpart, ActionKit, solves the other half of the equation: turning existing integrations into actions agents can execute, from updating a CRM record to firing a workflow in a project management tool. Data in, actions out — the two halves of making an AI agent genuinely useful rather than merely conversational.

His near-term focus is expanding Paragon's connector library past 1,000 applications and tuning the Workflow Engine for the real-time demands of agent operations. Longer term, he intends to relocate to the United States to lead Paragon's global technical strategy, with an ambition of making the platform a universal standard for software connectivity.

The through-line of his career is a quiet argument about where value in software actually sits. Models will keep improving, and interfaces will keep changing, but every AI product that promises to "just work" with a customer's existing tools is resting on infrastructure someone had to make unbreakable first. For a decade — from airline check-in counters to the agent stack — that has been Chaudhary's job.

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