AI agents can handle complex workflows more efficiently than traditional GenAI

New Delhi [India], November 11 : Artificial Intelligence (AI) agents are expanding the possibilities for business process automation and addressing the complex use cases that traditional Generative AI (GenAI) might not fully handle at scale, especially in secure and efficient deployments says a report by the global consulting firm Deloitte.
The report says that AI agents are reshaping industries and executive leaders should embrace this next era of intelligent organisational transformation.
AI agents can expand potential applications of Generative AI (GenAI) and typical language models. It also says that multiagent AI systems can significantly enhance the quality of outputs and complexity of work performed by single AI agents. Forward-thinking businesses and governments are already implementing AI agents and multiagent AI systems across a range of usages.
AI agents can more effectively reason and act on behalf of the user and don't just interact. They are opening new possibilities to drive enterprise productivity and program delivery through business process automation. Used cases that were once thought too complicated for GenAI can now be enabled at scale, securely and efficiently.
The report says multi-agent AI systems don't just reason and act on behalf of the user. They can orchestrate complex workflows in a matter of minutes.
By definition, the AI agent is an autonomous intelligent system that uses AI techniques to interact with its environment, collect data, and perform tasks without human intervention.
Explaining the difference between Gen AI and AI agents, the study adds that typical LLM-powered chatbots usually have limited ability to understand multistep prompts.
"They (LLM or Gen AI) conform to the "input-output" paradigm of traditional applications and can get confused when presented with a request that must be deconstructed into multiple smaller tasks. They also struggle to reason over sequences, such as compositional tasks that require consideration of temporal and textual contexts. These limitations are even more pronounced when using small language models (SLMs), which, because they are trained on smaller volumes of data, typically sacrifice depth of knowledge and/or quality of outputs in favour of improved computational cost and speed," it said.
The study says that GenAI use cases have mostly been limited to standalone applications such as generating personalised ads based on a customer's search history and reviewing contracts, among others.
Whereas AI agents also leverages the capabilities of domain- and task-specific digital tools to complete more complicated tasks effectively.
"AI agents equipped with long-term memory can remember customer and constituent interactions--including emails, chat sessions and phone calls--across digital channels, continuously learning and adjusting personalised recommendations. This contrasts with typical LLMs and SLMs, which are often limited to session-specific information," the study adds.
The study further adds that while individual AI agents can offer valuable enhancements, businesses also need multi-agent AI systems, given the limitations of single AI agents. The study notes that AI agents also introduce new risks that necessitate robust security and governance structures.
"A significant risk is potential bias in AI. algorithms and training data, which can lead to inequitable decisions. Additionally, AI agents can be vulnerable to data breaches and cyberattacks, compromising sensitive information and data integrity," it adds.