Understanding Agentic workflow

Agentic workflow or agentic ai represents a paradigm shift in artificial intelligence, enabling autonomous workflows that go beyond simple responses. Unlike traditional AI models, Agentic AI systems observe, plan, act, and adapt, making them ideal for handling complex, iterative tasks.

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Difference Between Traditional AI and Agentic AI

Traditional AI (Generative AI)

Traditional AI, such as ChatGPT, operates reactively. It generates responses or content solely in response to human commands.

A prompt given to a language model :
Prompt : " write an essay on squirrels."
Response : the model writes an essay in one step, including an introduction, body, and conclusion. However, it doesn’t critique its output or iterate to improve it.

Agentic AI

Agentic AI goes further. It doesn’t stop at producing an answer in one step. Instead, it iterates, reflects, plans, and adjusts. Agentic workflows allow AI to handle tasks proactively.

An agentic workflow for the same task :

1. Agent 1 : "create an outline for an essay on squirrels."
2. Agent 2 : "conduct research on key facts about squirrels."
3. Agent 3 : "write a draft based on the outline and research."
4. Agent 4 : "review the draft to identify inconsistencies or gaps."
5. Agent 1 : "refine the text based on Agent 4's feedback."

Result : an optimized essay refined through multiple iterations.

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Key Components of Agentic AI

1. Observation

Agents gather information from their environment using tools or external data sources.

An AI agent gathers the necessary information to plan a trip: it consults airline databases to check flight schedules, reviews weather forecasts, and examines hotel reviews for the selected destination.

2. Planning
Agents break down complex tasks into manageable steps and develop an action plan.

A travel agent plans a trip :

• searches for available flights and selects the best option based on price and schedule.
• books a hotel matching the user’s preferences.
• creates a travel itinerary, including places to visit and local transportation options.

3. Action
Agents autonomously execute the planned steps.

The agent books the flight tickets, confirms the hotel reservation, and sends the user a detailed itinerary, including departure times and transportation instructions.

4. Adjustment
Agents reassess and modify their plan based on new information or unforeseen events.

On the day of departure, the agent detects a cancellation of the originally booked flight. It updates the itinerary in real-time, suggests an alternative flight, adjusts the hotel reservation accordingly, and notifies the taxi driver of the updated pick-up time.

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How Does This Impact the Future?

Agentic workflows combine observation, planning, action, and adjustment to create intelligent, dynamic solutions. They redefine the way AI interacts with complex tasks, making it proactive and adaptive to real-world challenges.

Example in the Healthcare sector : Traditional AI : analyzes symptoms and suggests a preliminary diagnosis.
Agentic AI :

1. analyzes symptoms and patient history.
2. cross-references medical databases for tailored treatment options.
3. recommends personalized care plans.
4. adjusts recommendations based on new medical tests.

Example in the Education field : Traditional AI : provides a list of resources for a given topic.
Agentic AI :

1. identifies student strengths and weaknesses.
2. designs a personalized learning path.
3. provides adaptive exercises and feedback.
4. updates the learning plan based on progress.

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LLM-Based App Development : crafting Intelligent Customer Service Solutions

Agentic AI’s ability to observe, plan, act, and adjust offers a profound transformation in how AI handles complex workflows. This innovation extends beyond abstract principles into tangible, real-world applications. One area where this paradigm shines is in LLM-based app development, where the synergy of large language models and agentic workflows enables businesses to build intelligent, adaptable solutions that redefine operational efficiency and customer engagement.

Developing applications powered by Large Language Models (LLM) allows businesses to create intelligent and autonomous solutions, transforming how they interact with customers. By orchestrating key components, developers can design applications capable of understanding, reasoning, and proactively addressing user needs.

Key Components of LLM-Based Applications

1. Instructions:
   provide clear directives to the model to define its tasks. In a customer service application, instructions might include: "Respond to frequently asked questions about products, return policies, and shipping times."

2. Persona:
   define the personality and tone of the application to ensure consistent communication with users. The application adopts a friendly and professional tone, reflecting the brand’s values, to make customers feel comfortable while delivering accurate information.

3. Interface:
   design an intuitive user interface that facilitates interactions between customers and the application. A chat interface integrated into the company’s website where customers can ask questions and receive real-time responses from the LLM-powered application.

4. Knowledge:
   integrate external data sources to enrich the model’s responses with up-to-date information. The application accesses the product database, company policies, and shipping details to provide accurate, real-time answers to customer inquiries.

5. Memory:
   implement contextual memory to track past interactions and personalize responses. If a customer has previously asked about a specific product, the application remembers the interaction and can provide relevant updates or related recommendations in future conversations.

6. Tools:
   Extend the application’s capabilities by integrating additional tools. The application connects to a Customer Relationship Management (CRM) system via platforms like Zapier or Make, enabling it to fetch customer order details and handle requests such as returns or tracking updates.

Benefits of LLM-Based Applications

• Adaptability: Handles a wide range of customer queries and adapts to changing needs.
• Efficiency: Reduces response times and enhances customer satisfaction with quick and accurate answers.
• Scalability: Manages a high volume of interactions without compromising service quality.

tip

By integrating these components, developers can create customer service applications powered by LLMs that deliver enhanced user experiences while optimizing internal operations. This approach marks a significant step toward smarter and more autonomous solutions in the realm of customer engagement.