Artificial intelligence (AI) agents can be more effective tools compared to large language models (LLMs) or generative AI (GenAI) applications, opening new possibilities for driving enterprise productivity and program delivery through business process automation, according to a study by British professional services firm Deloitte. The report suggests that AI agents are reshaping industries by expanding the potential applications of GenAI and traditional language models. Multi-agent AI systems can significantly enhance the quality of outputs and the complexity of tasks performed by individual AI agents.
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Differences Between AI Agents and LLMs
With the help of an AI agent, cases previously deemed too complex for GenAI can now be scaled effectively and securely, the study noted.
By definition, an AI agent is an autonomous intelligent system that uses AI techniques to interact with its environment, collect data, and perform tasks without human intervention. Clarifying the distinction between GenAI and AI agents, the study explains that typical LLM-powered chatbots generally lack the ability to understand multi-step prompts or to plan and execute entire workflows from a single prompt.
"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.
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Applications of AI Agents Beyond GenAI
The study notes that GenAI use cases have mainly been limited to standalone applications, such as generating personalised ads based on a customer's search history, reviewing contracts and legal documents to identify regulatory concerns, or predicting molecular behaviour and drug interactions in pharmaceutical research.
In contrast, AI agents excel in addressing these limitations while leveraging the capabilities of domain- and task-specific digital tools to complete more complex tasks effectively.
"For example, 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," the report explains. "This capability contrasts with typical LLMs and SLMs, which often are limited to session-specific information."
Additionally, AI agents can automate end-to-end processes, especially those requiring sophisticated reasoning, planning, and execution. AI agents are opening new possibilities to drive enterprise productivity and program delivery through business process automation. Use cases that were once thought too complicated for GenAI can now be enabled at scale—securely and efficiently, the report further adds.
"AI agents don't just interact. They more effectively reason and act on behalf of the user," Deloitte said.
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Multi-agent AI Systems
The study further notes that while individual AI agents can provide valuable improvements, the transformative potential of AI agents is realised when they work collaboratively in multi-agent systems. Such systems are crucial, given the limitations of single AI agents. However, AI agents also introduce new risks, requiring robust security and governance measures.
"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," the study added.
"Multiagent AI systems don’t just reason and act on behalf of the user. They can orchestrate complex workflows in a matter of minutes," Deloitte report noted.
Deloitte envisions, "We see a future where agents will transform foundational business models and entire industries, enabling new ways of working, operating, and delivering value."
"It's important for C-suite and public service leaders to begin preparing now for this next chapter in the evolution of human-machine collaboration and business innovation," the report said.
