What Are Agentic Workflows?
Everything You Need To Know

What Is Agentic Workflow in AI?

Agentic workflows go beyond traditional automation by adding flexibility and intelligence to task handling. Traditional automation follows a fixed process, but agentic workflows use AI agents that can adapt, make decisions, and adjust in real-time. What if tasks could adjust automatically instead of needing constant supervision? That is the power of agentic workflows.

In simple terms, agentic workflows are a series of actions handled by AI agents that can think, solve problems, and adapt as needed. These agents work with humans and other AI systems to make sure tasks are completed accurately and efficiently. Unlike basic automation, where machines follow commands, agentic workflows allow AI to adjust to new information and make decisions on its own.

How do Agentic Workflows Work?

How does agentic workflow in AI work? To make it easier to understand, let us compare them with non-AI workflows and AI workflows without agents.

Now, let us look into how agentic workflows work step by step.

1. Making A Plan:

Let's consider a scenario in which a customer requests to cancel an order. The AI agent’s first job is to understand the situation clearly. It starts by asking a few questions to gather more details.

What is the order number? When was the order placed? These questions help the AI figure out if the order can still be cancelled based on its current status.

What is the order number? When was the order placed? These questions help the AI figure out if the order can still be cancelled based on its current status.

2. Adaptive Tool Use

If the cancellation request is complicated, the AI adjusts its approach. The order could have already been shipped through a third-party courier. In that case, the AI might contact the courier’s system to see if the delivery can be stopped or redirected.

If the order is non-refundable or needs manual approval, the AI will pull up the cancellation policies. It can then decide to automate a support ticket generation and transfer it to a human agent if further action is needed.

3. Iterating Based on Results

If the cancellation request cannot be processed right away, the AI does not just pass it on. What if the issue is with the payment or an inventory problem? In that case, the AI might suggest other options, like offering store credit or recommending a different product.

The AI makes sure to try all possible solutions before escalating the issue. This helps make the process smoother for both the customer and the business.

4. Finalising & Learning:

After the cancellation request is processed, the AI logs all the details for future use. The AI saves this information to improve how it handles similar cases in the future. If the cancellation cannot go through because of a policy issue, the AI escalates it with a clear summary. This helps the human team review the case and find the best solution.

What Are The Components of Agentic Workflows?

The components of agentic workflow in AI are:

• AI Agents
• Agentic Workflows Tools
• Prompt Engineering
• Feedback Mechanisms
• Large language models (LLMs)
• Multiagent Collaboration
• Integrations

1. AI Agents

At the core of any agentic workflow is the AI agent. It is the system or program that can handle tasks on its own for a user or another system. The AI agent sets up its own workflow, using the available tools and data to get the job done. Without an AI agent, a workflow is not truly agentic. It would not be able to make decisions or adjust to real-time changes.

2. Tools

AI agents are powerful, but tools are key to making them even more effective. These tools let agents access information beyond their original training, like real-time data, external datasets, or APIs.

With the right tools, AI agents can handle more than just routine tasks. They can adjust responses to fit different situations and solve specific problems more accurately.

3. Prompt Engineering

The performance of generative AI models in agentic workflows depends a lot on prompt engineering. This means creating effective prompts to help AI agents understand and respond to different types of questions. Techniques like chain of thought (CoT), one-shot, zero-shot, and self-reflection help AI agents give accurate and relevant answers.

4. Feedback Mechanisms

Feedback mechanisms are key to improving agentic workflows. They help AI agents refine their decisions through human input or collaboration with other agents. In some cases, human feedback guides the AI to make sure its responses are accurate and relevant. In other cases, agents work together to review and adjust decisions, improving overall results.

5. Large language models (LLMs)

Large language models are at the core of AI agents. They process and create natural language, which makes them essential for tasks like understanding customer questions and providing answers. The performance of an AI agent depends a lot on the LLM it uses.

6. Multiagent Collaboration

Multiagent collaboration is important in complex situations. When multiple agents work together in an agentic system, each can have its own set of tools or expertise. Instead of learning the same information separately, they share knowledge and work together to solve more complex problems.

7. Integrations

Integrations are important for making agentic workflows work smoothly with existing systems. Connecting AI agents to a company’s infrastructure helps improve operations and performance. By adding context-specific tools and data sources, agents can generate outputs that match business needs more effectively.

Use Cases of Agentic Workflows in E-commerce

1. Customer Support

AI support agentswith natural language processing can handle many customer service tasks, like answering questions and managing complaints or order issues. These agents can create support tickets automatically and pass more complex issues to human agents. By handling routine queries, AI allows human agents to focus on more difficult problems.

Example: A beauty brand’s AI support agent can recommend products, track orders, and resolve delivery issues. If a customer asks about product ingredients or has specific concerns, the AI can quickly pass the query to a customer service agent for a fast resolution.

1. Sales Processes

AI sales agentscan independently answer product questions, recommend items, and even complete sales. These agents guide customers through the buying process, making suggestions based on past interactions or browsing behaviour.

Example: A fashion brand’s AI sales assistant can suggest outfits based on customer preferences, past purchases, or current trends. When the customer is ready to buy, the AI can guide them through checkout and recommend matching accessories or deals that fit their style.

TechMonk: Build And Deploy AI Agents Without Coding

TechMonk is a first-of-its-kind agent as a service full-stack customer engagement platform for e-commerce businesses. By combining AI with automation, the platform helps businesses optimise processes and improve customer experiences throughout the entire customer journey. The setup process is simple, allowing companies to create custom AI agents without coding expertise.

Building Agentic Workflows With TechMonk

TechMonk's Agent Flow is a system that uses AI agents to automate tasks and processes within a business. For example, when a user interacts with a chatbot or sends an email, Agent Flow automatically triggers the right agents to carry out the requested actions.

Tool Library

TechMonk's Tool Library provides various tools to help businesses run their operations smoothly. Businesses can also create custom tools to meet their specific needs.

• Text : Text tools handle simple text-based tasks, like greetings or responding to messages, and can include AI for smarter replies.
• API : API tools allow the system to connect with external services to perform actions.
• Code :This lets businesses write their own custom code to perform specific tasks, giving complete flexibility for any unique requirements.
• RAG :The RAG (Retrieval-Augmented Generation) tool helps businesses upload documents and search through them to find information. For example, it can search company documents to answer questions like who the company founders are.
• Form :The form tool is used to collect and manage data, such as capturing leads or gathering specific information from users.

AI Agents

TechMonk lets you create AI agents using the tools available in the system. These agents can handle specific tasks and workflows. It also comes with pre-built agents for common business needs, such as:

• About Company Agent : This agent answers any questions about the company, providing consistent and accurate information to users.
• Lead Generation Agent : This agent helps capture leads by collecting user information for sign-ups, contact details, and other potential business opportunities.
• Order Management Agent :This agent manages everything related to orders, including tracking, cancelling, viewing past orders, and scheduling deliveries.

In addition, TechMonk allows businesses to create Custom AI Agents that effortlessly meet specific needs.

Agent Flow

When creating a workflow, businesses can add AI agents to handle different tasks within that process. For example, in an e-commerce workflow, agents are used for tasks like managing tickets, handling products, processing purchases, chatting with customers, and providing company information.

When a request is made, the orchestrator (the main system that controls the agents) decides which agent is best for the task. The orchestrator can also use multiple agents to handle different parts of the request. If a user asks several questions, the orchestrator makes sure the right agents answer each one. This makes it easy to adapt and customise workflows as business needs change.

PreBuilt Workflows

TechMonk comes with Prebuilt Sales and Support workflows. Here is what theory can do.

• Prebuilt AI Sales Agents : TechMonk’s AI Sales Agents automate and simplify the sales process for e-commerce businesses. These agents engage customers and recommend products based on their preferences. The AI agents for sales guide customers toward making a purchase while handling repetitive tasks and upselling opportunities.
• Prebuilt AI Support Agents : TechMonk’s AI customer support agents provide 24/7 customer support. They handle inquiries, order tracking, returns, and issue resolution. If an issue is too complex and needs backend processing, the AI creates a support ticket and informs the customer about the resolution timeline.
  
  If the AI detects frustration or negative sentiment, it escalates the conversation to a human agent for personalised help.
• Full-Stack Marketing Platform :TechMonk’s full-stack marketing platform provides a strong set of tools to power the AI agent's capabilities. It includes a Customer Data Platform that consolidates all customer data. Behavioural segmentation helps create targeted marketing, and e-commerce personalisation tools deliver tailored experiences.
  
  Businesses can effortlessly build loyalty programs with a simple builder from TechMonk. At the same time, it offers a ticket management tool for businesses to access and resolve support tickets raised by AI agents and customers.
  
  The platform also supports omnichannel communication, helping businesses reach customers across different touchpoints. A customer journey orchestration allows businesses to design smooth, automated experiences. These tools ensure that AI agents are effective and well-integrated into broader marketing and customer engagement strategies.