Sarvam AI Introduces Arya to Make Business AI More Reliable

Sarvam AI, a Bengaluru-based AI infrastructure company, launched a new platform called Arya recently, which is designed to make AI agents work reliably in real-world business environments, the company said.

The platform aims to solve a major industry challenge where many AI prototypes are developed, but only about 11% actually make it to reliable production, Sarvam said adding that AI agents often perform well in controlled demonstrations but behave unpredictably when handling real workloads which hinder broader business adoption.

How Arya Improves Reliability

At its core, Arya changes how AI agents manage and persist information. Traditional AI systems often treat data like a shared whiteboard, where entries can be overwritten and work lost if an agent fails. Arya instead treats every piece of information like a permanent accounting ledger, so nothing is erased. If an AI agent crashes, the platform can restart from the last clean checkpoint without losing progress.

Arya also blends human-coded logic with AI reasoning. For routine, rule-based tasks like routing requests, the system uses traditional code. For tasks that require judgment, such as analysing documents or summarising insights, it lets the AI model reason. According to Sarvam, this approach prevents AI from wasting resources on simple tasks it shouldn’t handle, improving both speed and consistency.

Sarvam demonstrated the difference with a financial document extraction example. When a cutting-edge AI model worked alone, it was inconsistent. When the same model worked within Arya’s structure, accuracy improved significantly because the system correctly controlled when to apply rules and when to allow flexible AI reasoning.

Developer-Focused Design and Debugging

Arya is built with developers in mind. Instead of writing complex code to manage AI agents, developers can describe workflows using simple configuration files. In some cases, advanced AI models can generate these configuration files automatically, lowering the barrier for building sophisticated systems.

When things go wrong, Arya’s Acta debugging tool shows exactly what happened at every step — including what the AI was processing and where errors occurred. This visibility allows AI coding assistants to automatically diagnose problems across thousands of runs, helping teams fix issues much faster.

Changes that would normally require major code rewrites — for example, switching to a different AI model or altering behaviour — can often be made by editing a single configuration line. The platform’s execution engine handles the rest automatically.

Positioning and Broader Context

Sarvam positions Arya not as a replacement for large AI models but as the foundation that ensures they run reliably in production. The company uses an analogy: AI models are like compilers that translate human intent into instructions, while Arya acts like an operating system that ensures those instructions run consistently and dependably.

The launch comes soon after Sarvam announced partnerships with the states of Odisha and Tamil Nadu to build sovereign AI infrastructure for government services. Arya is expected to serve as the execution layer for large-scale deployments, complementing Sarvam’s work on AI models optimised for Indian languages and local contexts.

Sarvam also highlighted that because everything in Arya is structured as configuration rather than hard-coded logic, the platform can experiment with different approaches and learn which ones perform best over time.

Why This Matters

Industry experts have noted that bridging the gap between AI prototypes and dependable production systems is a key barrier to broader adoption. By redesigning how AI agents manage data, execute actions and recover from failures, Sarvam aims to help organisations deploy AI with fewer disruptions and greater confidence.