AI Agents: Agent Cody Banks on AI: How businesses are optimizing workflows with AI agents

Amazon CEO Andy Jassy recently mentioned that his AI coding assistant, Amazon Q, has saved the company $260 million and 4,500 years of development. Meta CEO Mark Zuckerberg believes there could be more in time AI Agents people in the world. Major technology companies such as OpenAI, Meta, Amazon and Google envision agent AI as the future of personal assistance and business functions, automating tasks in ways previously unimaginable. An AI agent is a software program that can interact with its environment, learn, adapt, choose its own goals, and execute actions on behalf of a user. It can automate tasks, solve complex problems, think on its feet, and act autonomously. If OpenAI’s great language model, ChatGPT, released in November 2022, took the world by storm, AI agents based on some of these models are the next big thing in the world of AI, especially for business use.

Productivity, efficiency and cost savings

Companies like E42.ai, Fractal, Zoho, Supervity.ai, and many others are building these AI agents, capable of acting independently and solving complex tasks to automate processes in industries like finance, HR, and customer service, and improve data analytics. The AI ​​agent market is growing rapidly. Enterprises are leveraging these agents to reduce costs, optimize workflows, and unlock new capabilities, and the AI ​​market is projected to expand significantly in the coming years. According to Emergen Research, the agent AI market was valued at $30.89 billion, with 20% of the business concentrated in North America. The segment is expected to grow at an annual rate of 31.68% over the next few years. This is evident in the ways startups are integrating agents into functions like HR, finance, IT service management, customer services, data science, procurement, and more. New York-based Fulcrum Digital, which serves large operations in India, is set to introduce an enterprise agent store this year, offering over 250 agents across a variety of areas.

“In insurance, we offer agents for claims processing, premium price forecasts and underwriting support,” he said. Rajesh SinhaFounder and President of Fulcrum Digital. “For education, we have student admissions and course recommendation agents. Retail benefits from inventory management and personalized purchasing agents.”

“Financial services use our agents for transaction anomaly detection and real-time credit scoring. HR departments leverage our auditing and performance appraisal feedback agents. We also offer specialized agents for document processing, sales support and customer service across industries,” Sinha added.

Pune-based E42.ai reported that its cross-functional “AI workers” have automated over 200 processes, saved 200,000 man-hours, and resolved 3 million queries. For instance, its accounts payable AI executive, Neil, processed 10 million invoices from various channels like chat, vendor portals, and emails in 13 languages ​​without human intervention. Similarly, its AI IT operations manager, Walterautonomously resolves queries from the IT support service, according to the company.

“These agents excel at processing and analyzing massive data sets at unprecedented speeds, enabling the discovery of valuable insights that would be laborious or impossible for humans to find,” he said. Sanjeev MenonCo-founder and Chief Product and Technology Officer at E42.ai.

Ramprakash Ramamoorthydirector of artificial intelligence research at Manage Engine, the enterprise IT software solutions division of Zoho Corp, said companies are likely to start deploying agents from 2025.

For example, in the case of an IT monitoring system, an LLM can provide a summary of the logged faults, but it cannot determine the exact number of faults in a given time period. “But if you add an agent there, the agent can simply do a count query on a search API and give you the deterministic response from the logs along with the descriptive response from the LLM,” he explained.

Another Virginia-based startup, Supervity.ai, which serves clients such as Daikin, Mondelezand Ultratech, believe GenAI is a force multiplier, significantly reducing their time to market. Co-founder Vijay Navaluri explained that GenAI’s multimodal capabilities are unlocking new use cases that were not possible using traditionally siloed automation technologies.

“The biggest challenges for senior executives are increasing revenue, reducing costs, managing risk and improving customer experience,” said Paramdeep Singh, co-founder of Shorthills AI. “The answer to all of these challenges potentially lies in the data lake of large enterprise customers. Shorthills AI uses LLM agents on top of these data lakes.”

The company has created AI agents to build a SaaS tool in the legal field that understands and encodes laws, old judgments, rulings and legal cases. These agents have reasoning capabilities and can argue for and against cases, providing the probability of the most likely outcome, Singh explained.

But there are obstacles

While there is great potential for AI agents to improve productivity, save costs, and automate routine tasks, the process of creating and deploying these agents is fraught with challenges. The most critical of these is data scarcity and quality. In many areas, the amount of data needed to train AI systems is scarce. Healthcare and banking companies also refrain from training AI with sensitive customer data due to privacy concerns.

Greater autonomy of AI agents could also mean that they perform incorrect actions – for example, interfering with health data to give incorrect prescriptions or taking biased actions when processing insurance claims. Sometimes, AI systems trained on past data may fail to make future predictions, in areas such as making stock investment recommendations. It is also difficult to integrate agents with companies’ legacy IT applications. For example, integrating a new AI-based recommendation engine with an old e-commerce platform could require significant changes to the platform’s API.