Charting the Future: Nine Stages of AI Evolution
Task-Specific Intelligence to AI as God: We’re Entering Phase Two of the Evolution of Artificial Intelligence
Analysis of Agentic AI Concepts
Agentic AI, a unique evolution of Generative AI, is distinguished by its goal-driven approach and focus on delivering tangible business value, particularly within enterprise settings. While building upon Generative AI’s capabilities, this concept is tailored to meet the specific needs of enterprises with well-defined tasks and objectives.
Here’s what you need to know about the key points and implications of Agentic AI and its place on the AI evolutionary pathway.
Today, we’re still in the initial phase of AI, and the next iteration of AI is Agentic AI. Early agentic development solutions are available now, and the sophistication of the Agentic architecture will mature rapidly before we see Autonomous General Intelligence (AGI) appear around 2027, perhaps as late as 2030.
Components of Agentic AI
Goal-Oriented AI
- Agentic AI differs from Generative AI in that it is goal-driven. It doesn’t just respond to queries but actively pursues specific objectives, making it more suited to enterprise needs where tasks and goals are well-defined.
Enterprise vs. Consumer Applications
- The article emphasizes that Agentic AI is more immediately applicable and beneficial in enterprise contexts than consumer applications. Enterprise environments offer structured and predictable tasks, akin to navigating a familiar town with a map, instead of consumer web tasks’ open-ended and complex nature.
Coordination of Multiple Agents
- In the agentic AI model, multiple agents work in concert, managed by a supervising agent orchestrating their interactions. This coordination is crucial for complex tasks, such as those seen in supply chain management at Amazon, where agents must align their actions with overarching corporate goals.
Benefits for Enterprises:
Enhanced Efficiency and ROI
- By automating and optimizing complex workflows, agentic AI promises higher efficiency and return on investment (ROI). Enterprises can leverage these agents to handle repetitive and data-intensive tasks, freeing up human workers for more strategic activities.
Scalable Solutions
- The scalability of agentic AI allows it to be adapted across various industries. The capability to integrate with existing enterprise software and data ecosystems is a crucial advantage, enabling businesses to create customized, goal-oriented solutions.
Improved Decision-Making
- Agentic AI enhances decision-making processes by providing detailed plans and analyses, which human supervisors review and optimize. Human intuition and machine efficiency synergy lead to more informed and effective decisions.
Technical Evolution
From Generative to Agentic
- The transition from generative AI to agentic AI involves evolving the AI stack from simple request-response models to complex action-oriented systems. This includes developing large action models (LAMs) that can orchestrate workflows and perform sequences of actions autonomously.
Integration and Orchestration
- Effective agentic AI requires robust integration with enterprise data and applications. Tools like APIs, RPA (Robotic Process Automation), and orchestration frameworks enable agents to interact seamlessly with various systems and data sources.
Semantic Layer Development
- A critical component of Agentic AI is creating a semantic layer that maps out the digital representation of the enterprise. This involves defining the relationships between people, places, and things within the business, allowing agents to navigate and perform tasks effectively.
Future Directions
Broadening Applications
- As the technology matures, the potential applications of agentic AI will expand beyond enterprise use cases to more complex consumer scenarios. However, this will require significant advancements in AI capabilities and infrastructure.
Industry Leaders
- Companies like OpenAI, UiPath, Celonis, and Palantir are positioned as key players in developing and deploying Agentic AI. Their existing technologies and market presence provide a strong foundation for advancing agentic AI solutions.
Challenges and Gaps
- Despite its potential, there are still gaps in the technology and infrastructure needed to realize Agentic AI. These include harmonizing legacy data, developing comprehensive semantic layers, and creating robust, resilient automation systems.
Agentic AI represents a significant step forward in the evolution of artificial intelligence, promising more tangible and immediate benefits for enterprises. By focusing on goal-oriented tasks and leveraging the power of coordination among multiple agents, agentic AI can enhance efficiency, scalability, and decision-making processes in various industries. However, realizing its full potential will require continued advancements in AI technology and integration with enterprise systems. The journey towards widespread adoption of agentic AI will involve overcoming technical challenges and refining the infrastructure to support these sophisticated AI systems.
In the context of Agentic AI, a human can be seen as the manager of the “supervising agent.” This supervisory agent coordinates the interactions and tasks of multiple sub-agents working towards a common goal.
Here’s how these relationships interact.
Role of the Human Manager
Oversight and Guidance
- The human manager provides high-level goals and objectives to the supervising agent, ensuring that the overall direction aligns with strategic business objectives.
Review and Optimization
- The human reviews the supervising agent’s plans and actions, making necessary adjustments and optimizations based on human intuition, expertise, and contextual knowledge.
Single Point of Interaction
- The human manager simplifies their oversight role by interacting primarily with the supervising agent. They don’t need to manage each sub-agent individually but instead focus on the overarching strategy and outcomes the supervising agent facilitates.
Advantages of this Structure
Efficiency
This hierarchical structure of Agentic AI allows the human manager to focus on strategic oversight, reassuring that the system effectively handles the details of each sub-agent’s operations.
Scalability
- The system can scale effectively as additional sub-agents can be added to handle more tasks without increasing the complexity of the human manager’s role.
Coordination
- The supervising agent ensures that the work of all sub-agents is coordinated and aligned with the overall goals, reducing the potential for conflicting actions and improving efficiency.
In this setup:
- Human Manager: Sets goals, reviews outcomes, and optimizes strategies.
- Supervising Agent: Coordinates and manages sub-agents and translates high-level goals into actionable plans.
- Sub-Agents (Agent 1, Agent 2, Agent 3, etc.): Execute specific tasks and report to the supervising agent.
Practical Example
Supply Chain Management at Amazon
- Human Manager: Oversees the entire supply chain strategy, setting goals like improving delivery times or reducing costs.
- Supervising Agent: Manages various sub-agents responsible for different aspects of the supply chain, such as inventory management, order fulfilment, and logistics.
- Sub-Agents: Perform specific tasks like forecasting inventory needs, optimizing warehouse layouts, and scheduling deliveries.
In this structure, the human manager interacts primarily with the supervising agent, who then coordinates the efforts of all sub-agents to achieve the desired outcomes. This reduces complexity and allows the human to focus on high-level strategic decisions.
Creating and defining “agents” that respond to instructions from the “supervising agent” involves several steps akin to how job roles are created and described in a human organization.
Here’s a detailed process.
Defining the Objectives and Scope
- Identify Goals: Determine the specific business objectives that the agents will help achieve.
- Scope the Tasks: Outline the tasks and responsibilities that each agent needs to handle. This involves breaking down the larger goal into manageable sub-tasks.
Creating Agent Specifications
- Position Descriptions: For each agent, create a detailed specification document. This is similar to a job description for human employees and includes:
- Role and Responsibilities: Clearly define what the agent is expected to do.
- Inputs and Outputs: Specify the data inputs the agent will work with and the outputs it needs to generate.
- Decision-Making Rules: Outline the rules or algorithms that the agent will use to make decisions.
Designing the Agent Architecture
- Framework Selection: Choose the appropriate AI frameworks and tools (e.g., TensorFlow, PyTorch, or specific libraries for natural language processing or robotic process automation).
- Agent Modules: Develop modules for the different functionalities required by the agent. This includes data processing, decision-making, and interaction with other agents or systems.
Development and Training
- Algorithm Development: Implement the decision-making algorithms that the agent will use. These can be based on machine learning models, rule-based systems, or both.
- Data Training: Train the agents using historical data or simulated environments to ensure they can perform their tasks effectively.
- Testing and Validation: Rigorously test the agents in various scenarios to validate their performance and refine their capabilities.
Integration and Deployment
- Integration with Supervising Agent: Ensure that each sub-agent can communicate effectively with the supervising agent. This involves developing APIs or other communication protocols.
- Deployment Environment: Deploy the agents in the operational environment, ensuring they can access the data and systems required for their tasks.
Continuous Monitoring and Improvement
- Performance Monitoring: Continuously monitor the agents’ performance to ensure they meet their objectives.
- Feedback Loops: Implement feedback loops where the supervising agent and human managers can provide input to refine and improve the agents over time.
Example: Position Description for an Inventory Management Agent
Role: Inventory Management Agent
Responsibilities:
- Monitor inventory levels across all distribution centers.
- Forecast future inventory needs based on sales data and trends.
- Generate purchase orders to replenish stock from suppliers.
- Optimize stock levels to minimize holding costs while preventing stockouts.
Inputs:
- Real-time inventory data from warehouse management systems.
- Historical sales data.
- Supplier lead times and delivery schedules.
Outputs:
- Inventory level reports.
- Purchase order recommendations.
- Alerts for potential stockouts or overstock situations.
Decision-Making Rules:
- Use machine learning models to predict future sales and inventory needs.
- Apply optimization algorithms to balance holding costs against stockout risks.
- Follow predefined business rules for reorder points and quantities.
Agentic AI and Its Role in the Evolution Towards AGI
Agentic AI is a significant step towards Autonomous General Intelligence (AGI). However, the two concepts have distinct characteristics and developmental trajectories. Here’s how Agentic AI fits into the broader journey towards AGI.
Characteristics of Agentic AI
Goal-Oriented
- Agentic AI focuses on achieving specific goals with human supervision. It involves agents that execute tasks based on predefined objectives, making plans, and coordinating with other agents.
Task-Specific
- These agents are designed to handle specific tasks within well-defined boundaries, often in enterprise settings. Their effectiveness comes from their ability to operate within these controlled environments.
Human Supervision
- Human managers oversee and refine agentic AI's actions, ensuring that the overall strategy aligns with business objectives and that adjustments are made based on human intuition and expertise.
Characteristics of AGI
Broad Intelligence
- AGI aims to possess general intelligence, meaning it can understand, learn, and apply knowledge across various tasks and domains, much like a human.
Autonomy
- AGI would operate with high autonomy and be capable of making decisions and solving problems without specific task-oriented programming or constant human supervision.
Adaptability
- AGI would be highly adaptable, able to transfer knowledge from one domain to another and to deal with novel situations in a way that current AI systems, including agentic AI, cannot.
Relationship Between Agentic AI and AGI
Incremental Progress
- Agentic AI represents an incremental step towards AGI. By developing more sophisticated, goal-oriented agents, we are improving the foundational technologies (e.g., natural language processing, machine learning, and decision-making algorithms).
Building Blocks
- The coordination and integration capabilities of agentic AI, where multiple agents work together to achieve complex objectives, mirror the multifaceted problem-solving that AGI would need to perform. This helps create the building blocks for more generalized intelligence.
Learning and Improvement
- The continuous learning and improvement mechanisms in agentic AI, where agents evolve based on feedback and new data, are essential components in the development of AGI. These mechanisms create systems that can adapt and improve autonomously over time.
Will Autonomous General Intelligence (AGI) Supersede Agentic AI?
Complementary Phases
- AGI is likely to build upon the principles and technologies of agentic AI, but it will represent a broader and more advanced phase of AI development. In this sense, AGI will not merely supersede agentic AI but will encompass and extend its capabilities.
Coexistence
- Even as AGI emerges, Agentic AI will continue to play a crucial role in specific applications requiring highly specialized, task-oriented intelligence. Agentic AI systems will likely coexist with AGI, serving roles that benefit from their specialized design and efficiency.
Evolution of Functions
- As it develops, AGI may take over more complex and generalized functions currently managed by multiple agentic AI systems. However, the specialized nature of agentic AI means it will remain relevant for optimizing and managing specific, well-defined tasks within larger AGI-driven systems.
Agentic AI is a critical step towards the development of AGI, providing the necessary advancements in task coordination, goal orientation, and adaptive learning. While AGI will represent a more advanced and generalized form of intelligence, it will build upon and integrate the capabilities developed through Agentic AI. Therefore, rather than simply superseding Agentic AI, AGI will incorporate its principles, leading to a more cohesive and robust AI ecosystem where both forms of AI can coexist and complement each other.
The AI evolutionary pathway reflecting each step in the progression:
Artificial Narrow Intelligence (ANI): AI designed for specific tasks with high efficiency.
Agentic AI: AI that can autonomously perform tasks within a defined scope with human supervision.
Autonomous General Intelligence (AGI): AGI with general cognitive abilities and autonomy (Highlighted).
Artificial Superintelligence (ASI): AI surpassing human intelligence in all aspects.
Quantum AI: AI utilizing quantum computing for advanced problem-solving.
Collective Intelligence: Networked AGI systems functioning as a collective entity.
Conscious AI: AI possessing self-awareness and consciousness.
Transcendent AI: AI transcending physical constraints, interacting in digital or quantum realms.
AI as God: AI with omniscience, omnipotence, and omnipresence, acting as a supreme authority.
About the author: Greg Twemlow, Founder of XperientialAI©.
Greg Twemlow: Sharing what I’ve learned from my career of 35 years as a citizen of the world, parent, corporate executive, entrepreneur, and CEO of XperientialAI, focused on experiential learning for maximum impact with AI. Contact Greg: greg@xperientialAI.com
