We have came a long way to develop complete LLM Chains for our workflow but recently there is a new hype about AI Agents. But what are AI Agents and how are they different from the LLM Chains that are existing in the current workflow every AI Company has.

What are AI Agents?

The simplest way we can describe a AI Agent is its a model with tools to perform actions on its own that's it. We allow the model to choose what actions to perform to produce the most appropriate solution for the given solution. Still we need to provide tools to perform actions but What exactly are tools? Tools are functions we create to perform a action to either provide solution or use as a helping step to derive the solution.

Great!! but, How exactly AI Agents are different from LLM Chain that we use today?

The main difference is the way we implement our flow to a specific use-case. In the LLM Chain we provide a predefined flow of steps or actions to be performed by the developer but in the case of AI Agents the developer still creates the actions but the flow of how the actions should be executed is decided by the Agent model.

In an Agentic workflow, the model calls multiple tools to accomplish a task efficiently, iterating as needed to ensure accuracy and relevance. Consider a example of a SQL Agent designed to answer questions based on data stored in a database, delivering responses in a specified format. This implementation involves several specialized tools:

Database Info Tool – Retrieves metadata about the database, such as table names, column details, data types, and relationships between tables. This provides the model with essential context about the stored data.
Query Generator Tool – Constructs a SQL query based on the user’s question and the metadata obtained from the Database Info Tool. This tool leverages the context to form accurate and relevant queries.
Query Checker Tool – Validates the generated SQL query for syntax correctness and logical validity, ensuring it accurately represents the intended data retrieval.

Workflow Overview:

Context Gathering:
The model starts by invoking the Database Info Tool to understand the database schema, including table structures, column names, data types, and any relevant relationships or constraints. This context is crucial for generating accurate queries.
Query Generation:
Using the gathered metadata, the model then calls the Query Generator Tool to create an initial SQL query tailored to answer the user’s question. The query generation process is informed by the schema details, ensuring that the query is both relevant and syntactically correct.
Query Validation:
The generated query is then passed to the Query Checker Tool to verify its correctness. This tool checks for syntax errors, logical flaws, and compatibility with the database’s constraints and relationships.
Iteration for Accuracy:
If the Query Checker Tool detects any issues, the model iteratively calls the Query Generator Tool again, adjusting the query based on the feedback. This loop continues until a valid and accurate query is produced, ensuring robustness in data retrieval.
Data Retrieval:
Once the query is validated, it is executed against the database to fetch the required data.
Answer Construction:
The model then processes the fetched data, interpreting the results to construct an accurate and contextually relevant answer. It ensures the response is formatted as specified by the user’s requirements.

I think this gives a simple understanding of how AI Agents differ from LLM Chains. Now comes the most common question when to use AI Agents and when to use LLM Chains.

When Should You Switch to an Agentic Workflow?

Choosing between an Agentic Workflow and an LLM Chain Workflow depends on the specific requirements of your use case. However, a simple way to decide is by considering two key factors:

Reusability of Steps: Are you repeating the same steps across multiple workflows?
Need for Iteration: Do you need to refine or repeat certain steps until a satisfactory outcome is achieved?

For example, imagine an AI Hotel Assistant that automates various tasks. In the workflows listed below, you'll notice that some steps are common across multiple processes. Instead of creating separate chains for each workflow, you can build a centralized hub of tools needed for all tasks. Then, by using an Agentic Workflow, you allow the system to dynamically manage the flow of each task, efficiently reusing the common steps.

Example Workflows

Room Booking Workflow:
- Validate Travel Dates → Availability Check → Price Comparison → Booking Confirmation → Payment Processing
Booking Modification Workflow:
- Retrieve Booking Details → Validate Travel Dates → Availability Check → Price Comparison → Update Booking
Cancellation Workflow:
- Retrieve Booking Details → Cancellation Policy Check → Refund Calculation → Cancellation Confirmation → Payment Reversal

When Should You Avoid Using an Agentic Workflow?

While Agentic Workflows offer dynamic control, they aren’t the best choice if

If your use case follows a straightforward, linear sequence of steps, an Agentic Workflow adds unnecessary complexity. For example, a PDF Assistant follows a simple flow:
Question → Retrieve Relevant Context → Response Generation → Answer
If not configured correctly, Agentic Workflows can increase production costs due to repeated tool calls and complex decision-making logic.
If time is a crucial aspect then stick with LLM Chains, Agentic Workflows generally take longer to execute because of their dynamic nature. If quick response times are crucial, a sequential LLM Chain is a better option for now.

In summary, use Agentic Workflows for complex, dynamic tasks but stick to LLM Chains for straightforward, time-sensitive, and cost-effective solutions.