Author(s): Kaush B Originally published on Towards AI. GenAI, Agentic AI, Advanced AI Photo by Marija Zaric on Unsplash Content Introduction to Holistic Intelligence (HI) What are AI Agents? Types of AI Agents AI Agent Architecture: Topologies Enterprise AI Agents: Characteristics and Stages Agentic Workflows Agentic Design Patterns Implementation Guidelines Technical Risks Design Principles for Technical Risk Mitigation Introduction to Holistic Intelligence (HI) General-purpose AI (AI that can perform a wide variety of tasks) capabilities have grown rapidly in recent years. An AI agent is as an interactive system that interacts with environments using its perceptual abilities, performing actions that align with human intentions. With the help of AI agents, virtually any business — ranging from individual contributors to executives — will have the ability to manage not only human workforce, but also digital labor. This offers a promising pathway towards Artificial General Intelligence (AGI) or Holistic Intelligence (HI). Source: Arxiv Agentic AI operates in both physical and virtual worlds by leveraging cross-modal data that is acquired through interactions across diverse environments. Integrating LLMs (large Language Models) and LMMs (Large Multimodal Models) enhances agentic AI systems, making them more intelligent, adaptable, and able to handle a wide array of tasks across diverse use cases. Source: Arxiv What are AI Agents? AI agents are self-directed systems that sense their surroundings, make decisions, and exercise autonomy to accomplish specific objectives. Their operations can be broken down into four fundamental steps: Leverage Perception Module to collect pertinent data to grasp the context. Data can be collected from environments via sensors or ingested from external sources or accessed from memory containing historical interaction data. Leverage Cognitive Module to evaluate the task, identify the required actions, strategize the task by breaking it down into manageable steps, and analysing the data to determine the most effective approach. This module is enabled and powered by LLMs/LMMs. Leverage Action Module to carry out the task by using knowledge and tools to complete it, whether by delivering information or triggering an action. Leverage Learning Module to reflect on the outcomes of the action to enhance future performance. Source: UC Berkeley Types of AI Agents World Economic Forum has categorized AI agents into the following types: 1. Simple Reflex Agents Simple reflex agents rely on predefined condition-action rules to respond to environmental stimuli without considering past experiences. These rule-based systems are static, providing rapid but inflexible responses to specific inputs. Examples: Keyword-based spam filters Preprogrammed chatbots Automated email replies 2. Model-based Reflex Agents Model-based reflex agents use stored information from past observations to track unseen parts of their environment, enabling them to make decisions based on both current inputs and their internal model. This adaptability makes them more advanced than simple reflex agents, despite also relying on condition-action rules. Examples: Smart thermostats Intelligent robotic vacuum cleaners Advanced sensor-based irrigation systems 3. Goal-based agents Goal-based agents evaluate future scenarios and plan actions based on the desirability of their outcomes to achieve specific objectives. By using goal-oriented planning algorithms, they excel in complex decision-making tasks. Examples: AI chess engines Route optimization systems Customer service chatbots 4. Utility-based agents Utility-based agents leverage utility functions and decision theory to evaluate and prioritize potential outcomes, enabling optimal decision-making in complex scenarios with uncertainty or conflicting goals. This approach allows them to balance multiple objectives based on their relative significance. Examples: Autonomous driving systems Portfolio management systems Healthcare diagnosis assistants AI Agent Architecture: Topologies Agentic AI systems demand a robust and adaptable architecture that mirrors the processes humans use, both individually and collectively, to tackle complex tasks. These agents can function autonomously or cooperate within networks that feature varying topologies. A single agent system architecture involves 1 agent. Multi-agent systems (MAS) involve more than 1 agents and may be found in 2 different topologies. Vertical Architecture or Supervised Architecture: In this model, a “supervisor” agent manages interactions among other agents when their goals diverge, helping prioritize the system’s objectives and find a compromise. For example, if a buyer and seller agent can’t reach an agreement, the supervisor mediates the transaction. Network Architecture: In this model, agents or systems communicate to align with the MAS objectives, such as autonomous vehicles coordinating to avoid collisions while parking in tight spaces, ensuring both individual and collective goals of safety are met. Source: World Economic Forum Enterprise AI Agents: Characteristics and Stages An agentic AI system is characterized by the following capabilities: Autonomy: The ability to initiate and complete tasks independently, requiring minimal or no direct human supervision. This grants the AI greater flexibility in its operations and enhances efficiency in task execution. Reasoning: The capacity to make informed decisions based on contextual analysis, enabling sophisticated judgment. Reinforced Learning: The ability to continuously improve and adapt through interaction with its environment, learning from feedback. Language Understanding: The ability to comprehend and adhere to complex instructions, ensuring effective communication and execution. Workflow Optimization: The capability to efficiently manage and execute multi-step processes, streamlining operations. We are at the beginning of a three-stage future for Enterprise AI agents. Stage 1: Single Agents (Specialized Contributor) Source: Salesforce In the first stage of agentic evolution, specialized agents enhance business efficiency by excelling at defined tasks within specific industries, offering personalized AI-driven insights and high-quality generative guidance to optimize workflows, customer interactions, and marketing. These agents lay the groundwork for broader enterprise AI adoption, revolutionizing routine operations with speed, accuracy, and personalization. Stage 2: Collaborative Agents (Coordinated Agents) Source: Salesforce This stage involves an orchestrator agent coordinating specialized agents within a company, much like a restaurant’s general manager overseeing various roles to achieve a shared business goal. Stage 3: Agent Ecosystems (Ensemble Orchestrators) Source: Salesforce The final stage introduces advanced agent-to-agent (A2A) interactions across organizations, evolving beyond traditional business models into B2A and B2A2C, where AI agents act as intermediaries. This complexity will drive sophisticated enterprise processes, necessitating advanced negotiation, risk management, and conflict resolution skills as AI orchestrates personalized, meaningful collaboration across industries. Agentic Workflows When LLM calls are viewed as enhanced with […]
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