How multi-agent workflows are eliminating AI hallucination

Global enterprise software company Ramco Systems had recently announced its entry into the Agentic AI product segment with the launch of Chia, a conversational AI agent platform designed to redefine how enterprises engage with their customers. Chia is purpose-built for enterprises to reliably automate complex customer support interactions, reducing manual effort, response times, and operating costs, enabling businesses to deliver superior customer experiences at scale. This platform empowers organisations to shift from “human-in-the-loop” support models to a future of exception-based human involvement, where AI handles the heavy lifting.

From rule-based chatbots to autonomous Agentic AI systems

The core difference lies in the transition from assisting to acting. Traditional bots are often linear; they follow rigid "if-this-then-that" rules. If a user deviates from the script or makes a typo, the bot breaks.

An agentic product like Chia uses reasoning to decide and act autonomously. It handles unstructured data and multilingual inputs while maintaining context. If a user changes the subject mid-conversation, the agent adapts to the new flow without forcing a restart. It is designed to complete end-to-end tasks, like processing a refund or validating a policy, with minimal human intervention.

Deterministic multi-agent workflows power Chia’s architecture

“We solve this through deterministic multi-agent workflows. Instead of having one massive, "know-it-all" model that is prone to making things up, the architecture breaks tasks down. You might have 10 or even 100 specialised mini-agents within a single workflow,” said Abinav Raja, Managing Director, Ramco Systems, in an interaction with DQ Channels.

Each agent has a strictly defined boundary and goal. By using a Natural Language Workflow engine, the system operates within specific constraints. It doesn't "guess"; it follows the deterministic path laid out in plain language instructions, ensuring the AI only pulls from verified documents or specific API data.

Enterprise-grade AI requires scale, security and observability

According to Abinav, Scaling AI isn't just about handling more users; it’s about complexity and security. Enterprise-grade means the system can handle: Multimodal Data for Processing complex documents, various data formats, and high-volume transactions simultaneously, Observability, Providing 100% logs of why an agent took a specific decision, which is critical for debugging and audit trails. Another important aspect is the Security & Privacy, which is a built-in governance that allows for deployment across sensitive sectors like BFSI (Banking, Financial Services, and Insurance) and Healthcare.

Seamless integration with enterprise tech stacks using MCP tools

The platform is designed to sit on top of existing stacks rather than replacing them. It utilises Model Context Protocol (MCP) tools and integrates directly with a company’s existing APIs and policy documents. Because it uses a No-Code Agent Foundry, you don't need a heavy engineering roadmap to go live. You can point the AI at your current knowledge base and your systems of records, and it begins consuming that data to assist users immediately.

Natural language workflows simplify enterprise automation

Speaking about the ‘Natural Language Workflows’, he said, “Traditionally, a 20-step workflow for something like an insurance claim would require extensive backend coding or complex 'spaghetti" diagrams in a low-code tool. With Chia, you write the logic: "If a user asks for a refund, check if the purchase was made less than seven days ago. If yes, call the Refund API." The engine transforms these instructions into a fully functional, executable agent. This allows non-technical stakeholders to modify business logic on the fly without waiting for a developer sprint.”

Two operational streams: informational AI and personalised AI transactions

The architecture splits tasks into two streams:

Informational workflows powered by RAG

Informational (RAG): The agent scans uploaded policy documents to answer general questions (e.g., "What is the return policy?").

Personalised transactional workflows

Personalised (Transactional): The agent identifies the specific user, connects to the company’s internal database via API, fetches that user’s unique record, and performs actions based on that specific data.

Industry-agnostic Agentic AI platform for enterprise sectors

“It is entirely industry-agnostic. While the initial demand is highest in high-volume sectors like E-commerce, Telecom, and BFSI, the underlying logic applies to any field with complex workflows, including manufacturing and healthcare.”, he concluded. Any organization that manages large volumes of documentation and requires instant, accurate resolution can implement these agents to move their support from a "cost center" to a "revenue opportunity."

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