Agentic AI

- Overview

AI chatbots use generative AI (GenAI) to provide responses based on a single interaction. A person asks a query and the chatbot uses natural language processing to reply.

The next frontier in AI is Agentic AI, which uses complex reasoning and iterative planning to autonomously solve complex, multi-step problems. It will improve productivity and operational efficiency across industries.

Agentic AI is a software system designed to interact with data and tools with minimal human intervention. Agentic AI (also known as AI agents) emphasizes goal-oriented behavior and can complete tasks by creating a list of steps and executing them autonomously. 

You can think of Agentic AI as a way to combine automation with the creativity of large language models (LLMs). To put Agentic AI into practice, you need to create a system that provides the LLM with access to external tools, and algorithms that provide the AI ​​agents with instructions to use those tools. 

The way the agent communicates with the tools involves orchestration, using either flows or graphs depending on the framework used. This approach allows the LLM to "reason" and determine the best way to answer a question - such as deciding whether a query can be answered with available information, or if an external search is necessary.

- Agentic AI Systems

Agentic AI, also known as autonomous AI, is a type of artificial intelligence (AI) that can operate independently to make decisions, plan, and adapt to achieve goals. Agentic AI systems can perform these tasks with little or no human intervention. 

Agentic AI refers to an advanced AI system that autonomously takes actions, adapts in real-time, and, solves multi-step problems based on context and objectives. Enterprises today navigate a complex landscape marked by escalating costs, fierce competition, and rapid technological evolution.  

Agentic AI systems ingest large amounts of data from multiple sources to independently analyze challenges, develop strategies, and perform tasks such as supply chain optimization, cybersecurity vulnerability analysis, and helping doctors with time-consuming tasks.

Agentic AI systems are revolutionizing business operations by introducing intelligent automation and enhancing decision-making capabilities. 

- Key Characteristics of Agentic AI

Agentic AI functions by utilizing autonomous "agents" that can perceive information from their environment, reason through complex situations, make decisions independently, and take actions to achieve specific goals, all while continuously learning and adapting through feedback loops, essentially acting like intelligent assistants capable of proactive problem-solving and decision-making without constant human intervention. 

This is achieved through a combination of technologies like large language models (LLMs), machine learning, and reinforcement learning, allowing them to understand context, plan actions, and execute tasks in dynamic environments. 

Key characteristics of Agentic AI:

• Autonomy: Agents can operate independently, making decisions and taking actions without needing explicit instructions for every step.
• Goal-oriented: Agents are designed to work towards specific objectives, constantly evaluating their progress and adjusting strategies to reach desired outcomes.
• Learning and adaptation: Through interaction with the environment and feedback loops, agents continuously refine their decision-making abilities and improve performance over time.
• Perception and reasoning: Agents gather data from various sources, process information using techniques like natural language understanding, and leverage reasoning capabilities to make informed decisions.
• Action execution: Once a plan is formulated, agents can interact with external systems and execute actions to achieve the set goals.

- Agentic AI in Practice

In practice, agentic AI typically works through a cycle of "perceive, reason, act, and learn," where an AI agent actively gathers information from its environment, analyzes it to make decisions, takes actions based on those decisions, and then learns from the outcomes to improve future actions, often within a complex system involving multiple agents collaborating to achieve a goal.

This can involve steps like: identifying relevant data, analyzing information, planning a course of action, executing actions, monitoring results, and adapting strategies based on feedback; essentially, simulating intelligent behavior that mimics how humans solve problems in real-world situations. 

Key steps of agentic AI in practice:

• Data input: An agentic AI system first gathers data from various sources like sensors, databases, and user interactions.
• Information processing: A large language model (LLM) analyzes the data, understands the context, and identifies relevant information.
• Decision making: Based on the processed information and defined goals, the agent generates a plan of action, considering potential outcomes and constraints.
• Action execution: The agent takes the necessary steps to implement the plan, interacting with external systems or performing actions in the real world.
• Feedback loop: The results of the agent's actions are monitored and used to refine its decision-making process through reinforcement learning, allowing continuous improvement.

[AI Agent - NVIDIA]

- Example Applications of Agentic AI 

In the world of logistics and supply chain management, every minute counts. Delays, outages and inefficiencies can ripple throughout the system, costing businesses time and money. Agent AI is becoming a powerful tool to address these challenges.

Software agents powered by Agentic AI can analyze large amounts of data in real time, optimize routes, predict potential bottlenecks, and even adjust inventory levels based on demand fluctuations. This dynamic optimization helps ensure efficient delivery of goods and services, reducing costs and increasing customer satisfaction.

Other example applications of Agentic AI:

• Customer service chatbots: Proactively addressing customer inquiries, providing personalized support, and resolving issues without needing human intervention.
• Supply chain optimization: Monitoring inventory levels, predicting demand fluctuations, and automatically adjusting orders to maintain efficiency.
• Autonomous vehicles: Navigating complex traffic situations, making real-time decisions based on road conditions and sensor data.
• Smart home automation: Adapting lighting, temperature, and other home systems based on user preferences and environmental factors.

- Potential Applications of Agentic AI

An Agentic AI system could be a customer service chatbot that not only answers basic questions but can also proactively identify customer issues, suggest solutions, and even schedule follow-up appointments based on the conversation flow.

Agentic AI has many potential applications, including:

• Streamlining insurance claims
• Optimizing logistics and supply chain management
• Empowering financial decision making
• Accelerating drug discovery and development
• Transforming customer service and support
• Generating and distributing content
• Orchestrating enterprise software

[More to come ...]