Instruction-Based Agent

Introduction

An Instruction-Based Agent is a lightweight AI system architecture designed to perform tasks by following explicitly provided instructions, specified tools, knowledge bases, and input variables. Unlike complex multi-step reasoning or autonomous decision-making systems, the core idea of an Instruction-Based Agent is to complete tasks simply by receiving a clear prompt, a set of tools, relevant knowledge, and input data—without requiring sophisticated reasoning chains or extensive context management.

Tutorial Video

For a step-by-step walkthrough of creating an instruction-based agent, watch this tutorial video:

Key Components:

• Input Variables: Dynamic inputs from users that drive specific task execution.

• Prompt: Defines the agent’s role, behavior, and expected output format. For example, "You are a financial analyst responsible for calculating quarterly profits."

• Tools: External functionalities the agent can call, such as database queries, API calls, or calculators.

• Knowledge Base: Background information or data sources such as documents, databases, or APIs that support the task.

Workflow:

1. Receive input including task description and variables.

2. Construct an appropriate prompt based on the input.

3. Select and invoke the specified tool(s).

4. Process the tool’s output.

5. Generate the final response and return it to the user.

Advantages:

• High adaptability: Capable of handling open-ended and novel requests without predefined workflows.

• Tool-agnostic flexibility: Dynamically selects and combines tools based on the task context.

• Conversational continuity: Supports multi-turn interactions and maintains context effectively.

• Low design overhead: Reduces the need for manual task flow design, making development faster and more scalable.

Limitations:

• LLM dependency: Performance is heavily reliant on the reasoning, planning, and memory capabilities of the underlying language model.

• Unpredictable behavior: Outputs can vary significantly depending on context, reducing consistency and control.

• Scalability challenges for complex tasks: May struggle with long, multi-step tasks that require high precision or over 10 interaction rounds.

• Difficult to debug and evaluate: The decision-making path is implicit, making it harder to trace or fine-tune behavior.

Typical Use Cases:

• Open-ended question answering (e.g., "How do I prepare for a job interview?")

• Dynamic task execution using multiple tools (e.g., search → analyze → summarize)

• Conversational AI and personal assistants

• User-driven exploration tasks (e.g., travel planning, academic research support)

Creation

Prefix Prompt

A predefined text added before the user input to guide the model's response. Variables can be embedded in the prefix prompt to customize it dynamically.

Variables

Input data fields used to pass information between nodes in the workflow. These variables can be referenced inside the prefix prompt.

Knowledge

The external Knowledge Database used to support information retrieval or reasoning. In a prompt-based agent, the system automatically calls the relevant knowledge sources as needed.

Tools

External utilities or custom functions integrated into the workflow to perform specialized tasks. The instruction-driven agent automatically invokes these tools during processing.