Introduction
AI agents are designed to do more than simply generate a response to a user's message. They can combine an AI model with planning, memory, tools, observations, and actions to work toward a specific goal.
A simplified agent workflow looks like this:
Goal ↓ Understand ↓ Plan ↓ Use Tools ↓ Take Action ↓ Observe Result ↓ Continue or Finish
The exact process depends on the architecture and capabilities of the AI agent.
In Blog 69, we introduced the basic concept of AI agents. This article goes deeper into the components and workflow that allow agents to perform multi-step tasks.
AI Agent Architecture
An AI agent can be viewed as a collection of components working together.
A simplified architecture is:
User Goal ↓ ┌───────────┐ │ AI Model │ └───────────┘ ↓ ↓ ↓ Planning Memory Tools ↓ ↓ ↓ Actions ↓ Observation ↓ Next Decision
Not every agent uses exactly the same architecture.
Some systems may have additional components for retrieval, security, evaluation, or workflow management.
Core Components of an AI Agent
Several components commonly appear in agent systems.
AI Model
Interprets instructions and helps generate decisions or responses.
Planning
Determines what steps may be needed to achieve a goal.
Memory
Stores information that may be useful during a task or across interactions.
Tools
Provide access to external capabilities such as APIs, databases, search, or code execution.
Observation
Provides information about the result of an action.
Actions
Allow the agent to perform operations through its available tools.
Together, these components can create an iterative workflow.
The Role of the AI Model
The AI model often acts as the central decision-making component of an agent.
It can help interpret:
- User instructions
- Goals
- Available information
- Tool descriptions
- Tool results
- Previous actions
A simplified interaction can look like:
User Goal ↓ AI Model ↓ Determine Next Step ↓ Tool / Action ↓ Result ↓ AI Model
Large Language Models are commonly used in modern agent architectures.
Planning and Goal Setting
What Is Planning?
Planning involves determining the actions that may be required to accomplish a goal.
For example:
Create a report comparing five competitors.
An agent could organize the objective into:
Define Requirements ↓ Identify Competitors ↓ Collect Information ↓ Analyze Findings ↓ Compare Results ↓ Create Report
The planning process can vary depending on the system.
Some agents may create an explicit plan, while others may determine the next action dynamically.
Goal Setting
A clear goal gives an agent direction.
For example:
Find three suitable software tools and compare their pricing and features.
This is more actionable than:
Research software.
A well-defined goal can help determine:
- What information is needed
- Which tools may be useful
- What actions should be performed
- What the final output should contain
Agent Memory
Memory allows an agent to retain information that may be relevant to its task.
There are different approaches to memory.
For example, an agent may retain:
- Previous user instructions
- Task state
- Tool results
- Important intermediate information
- Conversation history
A simplified process is:
Information ↓ Memory ↓ Future Step
Memory implementation varies between AI systems.
Short-Term Context
An agent can use information from the current interaction or task.
For example:
The user wants the final report in a table.
The agent can retain this requirement while completing the workflow.
This type of information may be maintained through the conversation context or task state.
Long-Term Memory
Some AI systems can store information beyond the current interaction.
Examples might include:
- User preferences
- Previous interactions
- Saved information
- Historical task information
Long-term memory usually requires additional storage or retrieval mechanisms.
Not every AI agent has persistent memory.
Internal link:
Context Windows → Blog 61
Tools and Tool Use
Tools allow an AI agent to interact with external systems.
Examples include:
- Search tools
- APIs
- Databases
- Calculators
- Code execution
- File systems
- Business applications
A simplified workflow is:
Agent ↓ Select Tool ↓ Tool Executes ↓ Result ↓ Agent Interprets Result
Tools significantly expand what an agent can accomplish.
Example of Tool Use
Suppose an agent needs to calculate a complex value.
Instead of relying only on generated text, the agent can use a calculator or code execution tool.
User Request ↓ AI Agent ↓ Calculator Tool ↓ Calculation ↓ Result ↓ Final Response
The available tools depend on the system.
Observation and Environment
An agent needs information about the result of its actions.
This is often described as observation.
For example:
Agent Action ↓ External System ↓ Result ↓ Observation ↓ Next Decision
If an agent searches for information, the search results become part of what it can observe.
If an API returns an error, that error can also become an observation that influences the next step.
Actions and Execution
An action is an operation performed by the agent through its available capabilities.
Examples include:
- Calling an API
- Searching information
- Querying a database
- Running code
- Creating a document
- Updating a record
An agent cannot automatically perform every possible action. It can only use capabilities that have been made available and permitted by the system.
The Agent Loop
One of the most important concepts in agent architecture is the agent loop.
A simplified loop is:
Goal ↓ Plan / Decide ↓ Action ↓ Observation ↓ Evaluate ↓ Next Decision ↓ Action
The cycle may continue until:
- The goal is completed
- A stopping condition is reached
- The system encounters an error
- Human intervention is required
Complete Simple Example
Suppose an AI agent receives:
Find three laptops that meet my requirements and create a comparison.
A simplified workflow could be:
Step 1 — Understand
Identify the user's requirements.
Step 2 — Plan
Determine what information needs to be collected.
Step 3 — Search
Use an appropriate search or product tool.
Step 4 — Observe
Review the returned information.
Step 5 — Compare
Analyze the relevant specifications.
Step 6 — Create Output
Present the comparison.
User Goal ↓ Understand ↓ Plan ↓ Search ↓ Observe ↓ Compare ↓ Respond
Why These Components Work Together
An individual component is useful, but the combination creates a more capable workflow.
For example:
AI Model + Planning + Memory + Tools + Observation + Actions
can allow an agent to work through a multi-step objective.
Removing one component does not necessarily make a system impossible, but it can change what the system is capable of doing.
AI Agents and Traditional Automation
Traditional automation often follows predefined instructions:
IF condition ↓ THEN action
An AI agent can potentially interpret a broader goal and determine intermediate steps.
For example:
Traditional: IF order received → Send email Agent: Understand request → Check order → Determine appropriate response → Use system → Complete task
For predictable workflows, traditional automation can remain useful. Agents become more relevant when tasks require interpretation or flexible decision-making.
Key Takeaways
AI agents can combine several components to accomplish complex tasks.
The major components discussed in this article are:
- AI models
- Goals
- Planning
- Memory
- Tools
- Observation
- Actions
- Agent loops
A simplified agent workflow is:
Goal → Plan → Act → Observe → Decide → Continue or Finish
The exact implementation varies between systems, but these concepts provide a useful foundation for understanding how modern AI agents operate.