The Future Is Autonomous: How Multi-Agent AI Is Reshaping Business at Scale

Discussion with Atanu Dasgupta

With over 25 years of experience in software engineering, leadership, and AI-driven automation, Atanu Dasgupta has led transformative initiatives at companies such as Informatica, Sun Microsystems, Hewlett Packard and ABB Corporation. Currently serving as  Chief Product and Technology Officer at SimpliAutomation.com, he is at the forefront of deploying AI-driven automated agents for the UK insurance market. His expertise lies in developing scalable AI solutions that enhance operational efficiency, reduce costs, and integrate seamlessly with enterprise systems. In this discussion, Atanu provides valuable insights into the concept of multi-agent AI systems, the challenges, and opportunities they present for different industries, and how these systems are reshaping traditional workflows.

Understanding Multi-Agent AI Systems: A Paradigm Shift

Traditional AI models, particularly Large Language Models (LLMs), operate in a predictive and non-deterministic manner, generating responses based on training data. While these models can provide valuable insights, their reliance on historical data and lack of real-time adaptability pose limitations in dynamic business environments.

Multi-agent AI systems, on the other hand, represent a significant evolution in AI technology. These systems consist of multiple AI agents that collaborate autonomously to perform complex tasks, making them far more effective than single-task AI solutions. "Unlike traditional systems where human intervention is required for approvals and decision-making, multi-agent AI systems enable seamless orchestration of tasks, reducing bottlenecks and improving efficiency", says Dasgupta .

For example, in an insurance brokerage, a multi-agent system can simultaneously analyze policy options, validate compliance requirements, and generate detailed reports, tasks that would otherwise take human agents hours or even days. This capability fundamentally changes how businesses operate, allowing for greater scalability and enhanced decision-making. “We at SimpliAutomation plan to build a suite of AI Agents that would orchestrate various workflows, to address business requirements for the B2B Insurance market. So far we have received positive feedback from our customers”.

Key Challenges in Developing Scalable Multi-Agent AI Systems

Building multi-agent AI systems at scale comes with a unique set of technical and organizational challenges. “One of the biggest challenges is that different LLMs have their own methodologies, which makes integration complex”, Dasgupta explains. Some LLMs, such as OpenAI and Anthropic, have their own ways of creating agents, leading to interoperability issues. His team addresses this by leveraging open-source frameworks like LangChain, LangGraph, and LangFuse, which provide a standardized layer of abstraction, allowing AI agents to work seamlessly across different models.

Another critical issue is observability and performance monitoring. Traditional enterprise systems have well-established tracking and observability mechanisms, whereas AI-driven systems require new approaches. “We use LangFuse for real-time observability, ensuring that interactions with AI agents are traceable and user feedback is incorporated for continuous improvement”, says Dasgupta . This ensures that system interactions remain transparent and accountable.

Scalability and response time optimization are also vital. Multi-agent systems must handle thousands of simultaneous queries without performance degradation. To address this, Dasgupta’s team deploys Kubernetes clusters with auto-scaling capabilities, ensuring that AI-driven solutions remain responsive and efficient even under heavy loads.

Transforming the Insurance Industry with Multi-Agent AI

The insurance sector, particularly broker networks, has been slower to adopt cutting-edge technology compared to industries like healthcare. This presents both a challenge and an opportunity for AI-driven automation.

One of the key benefits of multi-agent AI systems in insurance is their ability to improve explainability and trust. “Brokers need to trust that the AI system’s recommendations are accurate and transparent”, Dasgupta emphasizes. To achieve this, AI-generated responses include citations and page references, ensuring that every decision can be traced back to a verifiable source.

Another major advantage is workflow automation. One of SimpliAutomation’s AI-driven applications, Policy Review Agent, helps brokers analyze and compare insurance policies—a task that traditionally takes 10 to 18 hours—to just 20 minutes. “With automation, brokers can focus on high-value tasks while AI handles the heavy lifting”, Dasgupta notes. This shift not only saves time, but also significantly enhances productivity and accuracy. “We are also witnessing strong demand for our Knowledge Agent, which delivers a highly accurate information retrieval system for large repositories of documents, videos, and other unstructured data”.

Additionally, AI-driven automation extends beyond policy management. Integration with voice systems and customer support platforms enables a more seamless experience, reducing friction in policy inquiries, claims processing, and customer interactions. By optimizing these processes, insurers can offer faster, more reliable services to their clients.

Ensuring Data Privacy and Security in AI-Driven Insurance

Given the sensitive nature of customer data in insurance, ensuring robust security and privacy protections is a top priority. Dasgupta outlines a multi-layered approach to data security. Role-based access control is crucial, as it restricts access based on user roles and minimizes the risk of data breaches. Another essential practice is anonymization and tokenization, where personally identifiable information (PII) is replaced with anonymized tokens before being processed by AI models. This ensures that no sensitive data is exposed. “Our products are designed to be cloud-agnostic and can run seamlessly in any public or private cloud environment with an auto-scaled setup. We also support data residency and sovereign cloud configurations”.

Real-time monitoring is another essential component. Continuous oversight allows teams to detect and mitigate potential breaches before they escalate. “We follow strict security protocols to ensure that no personal information ever leaves a secure environment”, Dasgupta affirms. This approach balances innovation with compliance, enabling insurers to harness AI’s power without compromising on data integrity.

Integrating Multi-Agent AI with Legacy Systems

Many insurance firms rely on legacy systems that were not built with AI integration in mind. Dasgupta explains that a structured approach is required to ensure seamless AI adoption. The first step is parallel deployment, where AI-driven systems run alongside traditional workflows, allowing for direct comparisons and building trust with users.

The second phase involves a human-in-the-loop approach, where AI-generated recommendations are verified by human agents before full automation is implemented. This step ensures that AI systems function effectively and align with business needs. Finally, the last phase involves gradual rollout by use case, where organizations automate one process at a time, ensuring a smooth transition without disrupting existing operations.

This phased implementation strategy helps mitigate resistance to change while ensuring a fastest process working hand to hand with AI, taking into consideration always the human approval.

Scaling AI Systems for Future Growth

As multi-agent AI systems grow in complexity, ensuring their scalability and maintainability becomes paramount. Dasgupta highlights several best practices that help organizations manage this growth effectively. One essential approach is structuring AI agents as microservices, which allows teams to scale specific components independently without affecting the entire system.

Another key practice is hierarchical agent delegation, where tasks are distributed among specialized agents, reducing computational overhead and optimizing efficiency. Memory management is also crucial. AI agents must retain short-term and long-term memory, enabling them to deliver more context-aware responses over time. This capability ensures that AI systems provide accurate and relevant answers while adapting to evolving user needs.

“A well-structured AI ecosystem ensures that scaling does not come at the cost of efficiency”, Dasgupta explains. By focusing on modularity and adaptability, organizations can future-proof their AI investments, ensuring long-term success.

The Future of AI-Driven Insurance: Key Takeaways

Dasgupta concludes with three core insights for executives considering AI adoption in insurance:

1. AI-driven agentic systems will disrupt the insurance industry, unlocking unprecedented efficiency and accuracy.
2. Successful implementation can significantly reduce costs, improve customer experience, and open new revenue streams.
3. High-quality AI applications that deliver reliable and transparent results will be the key differentiator in the market.

As AI continues to evolve, businesses that embrace multi-agent AI systems will gain a competitive edge, driving innovation and transforming traditional processes. “The key is not just in adopting AI, but in ensuring its output is of the highest quality”, Dasgupta concludes.