Introduction to Agentic AI and Agentic Workflow

As artificial intelligence (AI) continues to evolve, a new field has emerged known as Agentic AI. Unlike traditional AI, agentic AIs are designed to understand and make strategic decision for more complex scenarios.

In this post, we will look into the world of agentic AI. We cover its key components and history, how it differs from traditional AI. We will also discuss the benefits and risk of its implementation in business operations and the future of it.

‍

What is Agentic AI?

Agentic AI is an AI system that is specifically designed to understand complex workflows and achieve goals autonomously, with little to no human interference. Some key characteristics of agentic AI which include:

• Autonomy: Agentic AI is imbued with its own goals, objectives, and motivations, which drives its decision-making and actions. It can autonomously choose the most appropriate steps to pursuit their goals without human supervision.
• Adaptive learning and Reasoning: Agentic AI can gather information from their interactions with the environment and learn from past experiences. These agents can use reasoning and problem-solving abilities to update their knowledge, revise their strategies, and improve their performance over time.
• Language understanding: Agentic AI leverages state-of-the-art NLP models, such as transformers and large language models, to process and comprehend human language at a deep semantic level.
• Workflow Optimization: Agentic AI agents combine their language understanding with reasoning, planning, and decision-making abilities to optimize workflows and business processes. This includes optimizing resource allocation, streamlining communication and collaboration, and identifying opportunities for process automation.
• Multi-agent and System Conversation: Agentic AI can communicate between different agents to build a complex workflow. It can also connect to other systems or tools, such as email, code executor, or search engine to perform variety of tasks.

The integration of the above features enables agentic AI to become a powerful tool to enhance productivity and improve decision-making process. These features also differentiate agentic AI with traditional AI.

‍

Agentic AI vs. Traditional AI: A Comparative Analysis

Agentic AI and traditional AI have a huge dissimilarity from different aspects. The following table analyzes the differences between two types of AI and list some example of each type.

‍

Building Blocks of Agentic AI

Harrison Chase, the founder of LangChain has described that the building blocks of each agentic AI includes four main perspectives, Planning, Memory, Tool, and Action.

• Planning: The agent will plan a workflow to achieve its goal. The plan includes but not limited to reflection, self-critics, chain-of-thought, and subgoal decomposition. In order to allow the agent to make a better plan, prompt engineering is the foundation for the agentic assistants to generate something closer to the expected result. Find more insightful example prompts from the blog post "How to Create an AI Assistant Without Any Coding Skills". The planning process will be supported by the memory component to review the plan or previous subtasks in the the workflow.
• Memory: The agent will have a memory component to store the previous actions or subtasks which may be needed in the future planning or actions. The memories can be categorize into two types, short term memory and long term memory for storing data or previous task results with different dependencies.
• Tool: This refers the a set of tools, like calendar, calculator, code interpreter, that the agentic assistants acquire to perform specific task.
• Action: The agent take action to complete the task assigned by the plan and make use of the tools equipped.

These four perspectives will run in a loop and form a cycle until the given task is completed. The process starts from planning, to equipping tool, follow by action, then memory and back to planning to start the next stage in the plan or another task.

For instance, if we have to calculating (1+2)/3 by agentic AI, the agent will start with the planning stage. It plan to compute 1+2 first. The agent equip with the tool calculator, and take action to perform addition. After generating the calculation result, it will store the result in the short-term memory for the next step. Finally it will go back to the planning stage to reflect on the plan, then decide and perform division with the result stored in the memory to complete the task.

To perform a more complex task or work flow, a multi-agent system can be created with multiple agentic AI with different tools or planning that specify for different subtask.

‍

The Evolution of Conversational AI: From Reactive to Agentic AI

The past decades has witnessed a remarkable change in the field of conversational AI, from simple rule-based chatbots with predetermined responses to specific user inputs to advanced agentic AI systems capable of decision-making and goal-oriented behaviors. There are a few key stages in the evolution process.

1. Reactive Conversational AI

Early conversational AI systems were primarily rule-based, with predetermined responses to specific user inputs. They do not have memory and are task specific. These systems were limited in their ability to engage in natural, contextual dialogue, and often struggled with understanding complex language.

2. Conversational AI with Contextual Understanding

With advancement on natural language processing (NLP) and machining learning, AI can grasp the broader context and nuances of the conversation after training with much more data. Through integrating the language models, dialogue management, and knowledge bases , conversational AI systems became more adept at understanding context, maintaining coherent conversations, and adapting their natural-sounding responses based on the flow of the dialogue.

3. Agentic AI

Under enterprise-utilization, enterprises require not only responses, but also a system to autonomously manage and execute complex workflows or tasks. They also need AI to take initiative to anticipate, plan, and take independent actions to achieve their goals. Therefore, such agentic AI assistants are developed to fulfill the requirements, which includes autonomous goal-setting, context-aware decision-making, robust language understanding, and the ability to integrate with various enterprise system.

‍

What is Agentic Workflow?

Agentic workflow refers to connecting multiple agentic AI to form a more iterative or a multi-step framework such that the agents can perform more complex tasks and make decision autonomously. There are a few key aspects that enable agentic workflows to solve more complex tasks: data integration, decision points, and feedback mechanism. For further details, check out the post "The Power of AI Agent and How to Build AI Agents".

‍

The Agentic Process

The shift to agentic workflow from a single agentic AI indicates a shift in emphasis toward using AI to provide better results, showing that even less advanced LLMs may produce impressive outcomes when integrated into these intricate, multi-tiered systems. To set up such impressive system, there are a few major steps.

Step 1: Identify the Workflow and the Structure

Before you really start building the agentic assistants or the workflow, you should first identify the structure of the agentic workflow or the role of the individual AI agents such that you will have a clearer idea on how the workflow will look like. You may want to ask yourselves the following questions to understand the structure of your agentic workflow and the agentic AI assistants:

• What tasks or issue will the workflow deal with?
• How many agentic AI do you need to divide the complex task into smaller subtasks?
• What are the role or the target of the individual agents?
• What are the input and output of each agentic AI?
• How the agents interact or connect in the workflow?

When you have answers for the above questions, you should have a better idea on your workflow structure and you could proceed to the next step.

Step 2: Specify and Create the Agentic Assistants

After you know how many agents and the role of them, you may start building you agents according to their responsibility. To create an agentic AI, there are four main building blocks to considerate as mentioned above:

• Planning and Action: Determines the input and output of the agent and check whether there are intermediate steps like using the tool or the planning process outline that the agent have to make such that you can set up the prompt for your agent.
• Memory: Determined how many past message to retrieve and whether your need a long term memory. This may depends on the model or the framework you use. Some framework support memory system by its own but some required to set up the memory system by yourself.
• Tool: Refer to the role of your agent, determine the power ups or extensions that the agent may required. For example, if the agent is a researcher on your sales lead, then it may require a web search tool and web scrapper to gather information of the lead from their webpage or LinkedIn.

When your have determined the requirements of all building blocks, then you could set up your agents smoothly with the chosen LLM model. To learn more difference between different LLM model, please refer to the table in the blog post "How to Create an AI Assistant Without Any Coding Skills".

Step 3: Utilize Agentic Workflow for Automation

After setting up all agents, this is the time to build the agentic workflow. With the consideration in the first step, you can set up the connection between the agents by the passing one's agent output as another input or any other way as you want. You may have to refine your agents' prompts or add an extra step to reformat the output in order to fit in another agent's prompt as an input.

After setting up your agentic workflow, you should monitor its performance and refine its structure to optimize your system for efficiency, accuracy and creativity.

‍

Agentic AI and Workflows in Modern Applications

How Agentic Assistant helps?

Agentic AI could bring huge benefits to enterprises including increase efficiency with its ability to connect with multiple systems and tools, and automate complex workflow from the start to the end. It could also help to save time for human employees by taking over repetitive mundane tasks, while optimize the workflow and enhance decision-making process.

Use cases of Agentic AI and Agentic Workflows

The following are a few use cases of agentic workflow and assistant.

• Data Visualization: We could set up a multi-agent conversation where each agent perform a sub tasks. Starting from a coder to code a program extracting data from a website and plot the graph to visualized data, to a critic to give comments on the code for modification, and a executor to check whether the code run without any error. Critic and executor can pass their comments back to coder and coder will rewrite the code until there is no error.
• Retrieval Augmented Generation (RAG): Agents can extract and incorporate with data from other sources like webpages or database like VectorDB in different data types to generate responses based on the new data outside of the model's training dataset.
• Customer Service: Agents can provide 24/7 customer support by handling inquiries, resolving issues, and guiding customers through various processes. While the agents can continuously developed based on the user responses or preferences to achieve personalization and improve customers' satisfactory.

Potential Risks

While the implementation of agentic assistants can bring significant benefits, there are also potential risks that organizations should be mindful of:

A. Ethical and Accountability Concerns

Agentic AI systems, with their increased autonomy and decision-making capabilities, can raise ethical questions around transparency, fairness, and accountability. The complex algorithms that power AI’s decision-making capability can be difficult to understand and predict by average users. This lack of transparency may reduce the trustworthiness of AI’s decisions and prove to be an obstacle to its adoption and auditability. Therefore, there is a need to demand the agentic AI systems to provide explanations for their decisions in order to understand the logic behind AI's reasoning and enhance trustworthiness. Enterprise could also include AI ethics committee, bias detection tools to further ensure transparency, fairness, and accountability in the use of agentic AI.

B. Security and Vulnerability Risk

Agentic AI systems will connect to enterprise networks and data sources which may introduce new cybersecurity vulnerabilities if not properly secured. Malicious actors could potentially exploit weaknesses in the AI systems, leading to data breaches, system disruptions, or even the manipulation of the AI's decision-making processes. Comprehensive security measures, including robust access controls, encryption, and incident response protocols, are essential.

C. Scalability and Maintenance Complexities

As the systems grow in complexity and scale, enterprises may face challenges in maintaining, updating, and scaling these systems effectively. The need for continuous learning, model refinement, and system maintenance can be resource-intensive and require specialized expertise. Enterprises should invest in robust infrastructure, processes, and skilled personnel to ensure the long-term sustainability and scalability of their agentic AI initiatives.

‍

Future Trends in Agentic AI and Agentic Workflows

The agentic AI will continuously developed in the future and the followings some predictions on the future usage and development of the agentic AI.

• Increased Adoption: Agentic AI will see a significant rise in adoption across a wide range of enterprise departments. This is because the organizations recognize the potential benefits in improving efficiency, decision-making, and customer experience.
• Virtual Workforces: Rather than a complete replacement of human workers, agentic AI will increasingly be deployed in a collaborative, hybrid model, where humans and AI agents work together to leverage their respective strengths.
• Personalization and Contextual Adaptation: Agentic AI systems will become more personalized and adaptive, tailoring their interactions, recommendations, and decision-making to the specific needs of individual users. This could be achieved through advancement in machine learning techniques combining continuous feedback and learning.
• Security Measure: To ensure the safe and responsible use of the simulation framework, robust safeguards and control mechanisms are critical - this may involve imposing resource limitations, implementing access controls, embedding validation checks, and addressing ethical considerations like privacy, bias, and transparency, all of which help uphold the responsible and ethical deployment of the simulation capabilities.

‍

Conclusion

The emergence of agentic AI signals a transformative phase in the enterprise AI revolution. With the capacity for autonomous planning, reasoning, execution, and adaptation, agentic AI is poised to usher in a new era of enhanced efficiency and productivity across core business operations.

As we look into the implementation, it comes with a range of potential risks that organizations must be prepared to address, from ethical and accountability concerns to security vulnerabilities. However, as we’ve implemented different ethical and security measures, and further develop on infrastructure, this could have managed the risk and guide us to a successful implementation of the agentic AI.

By embracing this transformation, organizations can position themselves for long-term success, enhance their service quality, streamline their operations, and maintain a strategic advantage in the ever-evolving business landscape.

‍