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AI Agents: The Next Evolution in Generative AI

generative ai large language models

Building and deploying AI systems has traditionally been complex and risky. However, a new development is gaining traction: AI agents. These agents leverage generative AI to offer more robust and manageable solutions than their predecessors.

According to McKinsey, AI agents are the "next frontier" in AI technology. These systems, which can autonomously interact in dynamic environments, are expected to become increasingly influential. The integration of generative AI enhances their capabilities, allowing them to plan actions, use online tools, collaborate with other agents and people, and continuously improve their performance.

As McKinsey’s report notes, the evolution from knowledge-based AI tools, such as chatbots, to more sophisticated AI-enabled agents marks a significant shift. These agents use foundational models to execute complex, multi-step workflows, transforming the technology from simple information processing to actionable tasks.

A recent survey by Capgemini revealed that 82% of tech executives plan to integrate AI-based agents into their organizations within the next three years, a notable increase from the current 10% who have operational AI agents. The report also found that 70% of respondents trust AI agents to analyze and synthesize data, while 50% would trust them to draft professional emails. Additionally, 75% plan to use AI agents for tasks like generating and refining code, with other applications including drafting reports, editing content, and analyzing data.

AI agents offer diverse capabilities. For instance, a virtual assistant might plan and book a personalized travel itinerary across various platforms. Another example includes Qventus’s Patient Concierge, an AI assistant that manages patient reminders, guidelines, and general care questions.

AWS outlines six levels of AI agents, each providing different functionalities

1. Simple Reflex Agents: Handle basic tasks based on predefined rules and immediate data, such as password resets.

2. Model-Based Reflex Agents: Evaluate probable outcomes and build an internal model of the world to make decisions.

3. Goal-Based/Rule-Based Agents: Handle complex tasks like natural language processing by comparing different approaches to achieve desired outcomes.

4. Utility-Based Agents: Assess different scenarios and their benefits, such as finding the best airline deals.

5. Learning Agents: Continuously improve by learning from past experiences and adapting based on feedback.

6. Hierarchical Agents: Manage other agents, breaking down complex tasks into smaller ones and coordinating efforts to achieve goals.

 

Takeaway

Historically, implementing software agents required intricate, rule-based programming or specialized machine learning training. However, generative AI is shifting this paradigm. By using foundation models trained on extensive, varied data sets rather than rigid rules, these agents can adapt to new scenarios similarly to large language models (LLMs).

Generative AI’s use of natural language processing further simplifies the automation of complex tasks. Instead of translating workflows into computer code—a labor-intensive process requiring technical expertise—agents can now be instructed using natural language. This allows for quicker and more accessible integration of complex workflows, potentially enabling non-technical employees to set up these systems.

Overall, AI agents represent a significant advancement in AI technology, offering greater adaptability and ease of use compared to traditional methods.