AI Agents

Write up done with the help of Google’s NotebookLM ( simply Amazing!) – Check out the Podcast it generated after my research was done.

An AI agent is an autonomous system that can make decisions and plan, rather than simply following a set of instructions. AI agents are also able to interact with their environment using multimodal perception, such as video, audio, and images.

Here’s a breakdown of different aspects of AI agents, based on the sources:

1. Levels of Autonomy and Control

• AI agents can range from those that require human supervision to those that can operate autonomously.
• Even highly autonomous agents are likely to have some level of human oversight and pre-planning to ensure they act within defined boundaries and have fail-safes.
• Some agents are designed to be assistants that help with tasks, while others can execute tasks on their own.
• Some agents can manage tasks from start to finish

2. Types of AI Agents

• Horizontal Agents: These are platforms that offer the ability to build agents for diverse use cases without specializing in a specific area.
  • Examples include Microsoft and OpenAI.
• Vertical Agents: These are companies or agents that specialize in building agents for a specific use case or function within an organization.
  • Examples include companies like Sierra, which focuses on customer support agents, and Devin, which is a software engineer agent.
• Multi-Agent Systems: These systems involve several AI agents working together to accomplish a goal, with each agent having a specific role.
  • They can act like a cross-functional project team, with agents for different aspects of a task (e.g., writing, testing, and debugging code).

3. How AI Agents Differ from Traditional AI Systems

• Traditional AI systems often follow pre-programmed instructions, whereas AI agents can make decisions and take actions autonomously.
• AI agents can use tools and APIs, allowing them to access information and interact with their environment.
• Unlike traditional systems, AI agents can learn from experience and adapt their behavior over time.
  • For example, an AI agent may provide personalized recommendations or suggestions based on past interactions and environmental awareness.

4. Key Aspects of AI Agent Development and Operation

• Cognitive Architectures: These provide a blueprint for building intelligent and autonomous systems, using guardrails and frameworks to control agents.
• Tools and Frameworks: There’s a growing need for dedicated tools to develop, test, and manage AI agents.
• Multi-Modal Abilities: Agents will need to process and understand different forms of input, such as video, audio, images and text.
• Benchmarks: New benchmarks are needed to measure agent performance, with evaluations that require multi-step reasoning and open-ended thinking.
• Agent-Oriented LLMs: There is an increasing need for large language models that are purpose-built for powering the autonomous activities of agents.

5. Applications of AI Agents

• AI agents are being developed for a wide range of applications across many different industries and can be deployed across a variety of business functions or roles.
  • Examples include customer service, sales, finance, and supply chain management.
• They can handle tasks such as lead qualification, order processing, supplier communications, account reconciliation, and customer support.
• In the workplace, agents can act as personal assistants, automate tasks, and even handle HR or IT requests.
• There are also potential consumer applications, such as assisting people with disabilities.

6. Business and Economic Considerations

• The pricing model for AI agents is still unclear, and there is considerable debate on how it should be approached. Some ideas include:
  • Pricing agents like labor, but at a discount.
  • Pricing agents on a per-outcome basis.
  • Pricing based on underlying AI costs.
  • Using a pure SaaS subscription model.
• Vertical agent companies are predicted to have a higher return on investment than horizontal ones, at least in the near term.
• There is a prediction that investment in agent companies will be substantial.
• There are concerns about the potential job displacement caused by AI agents, but also optimism about their potential to augment human work and improve efficiency.

AI agents represent a significant advancement in AI technology, offering the potential for greater automation and autonomy. They have a wide range of applications across both enterprise and consumer use cases, and are poised to become a major force in the coming years. However, it’s still early days for the technology, and many aspects are still evolving.