August 5, 2026
Agentic System Design: A Beginner's Guide to Building AI Agents
Learn how AI agents think, plan, use tools, remember context, and complete tasks with real-world examples
By Shashi
1 min read
Agentic System Design
What is an agent?
An AI agent is a system that performs tasks on behalf of a user. Unlike a normal LLM that only generates text, an agent can make decisions, use tools, remember information, and complete tasks autonomously.
An agent is built on top of an LLM and includes:
- LLM (Reasoning)
- Tool Calling (APIs, Database, Search, etc.)
- Memory (Store conversation or state)
- Planning & Decision Making
- ️ Execution
Example: A travel booking agent can search for flights, compare prices, book tickets, and send confirmation without requiring the user to perform each step manually.
How Does an Agent Work?
Steps:
- Receive user input.
- Understand the user's intent.
- Create a plan to solve the task.
- Select the required tools.
- Execute the plan.
- Evaluate the result.
- Retry if necessary.
- Return the final output.
What are single-agentic and multi-agentic?
A single-agent system uses only one AI agent to complete the entire task.
The same agent:
- Understands the request
- Creates a plan
- Uses tools
- Produces the final result
Best for:
- Flight booking
- Ticket booking
- FAQ chatbot
- Email assistant
A multi-agent system consists of multiple specialized agents working together. Each agent has a specific responsibility, and an orchestrator coordinates their work.
Best for:
- Software development
- Research automation
- Marketing automation
- Customer support platforms
What is a building block of an agent?
Model layer: The LLM responsible for reasoning and understanding. (GPT, Claude, Gemini)
Tool calling: Using the external tools (Gmail, API, database)
Memory: Who stores the required context (short-term memory, long-term memory)
Orchestration: who collaborates with the workflow (planning, execution of different tools, replanning, evaluating error handling)
Evaluate: checking whether the generated thing is correct or not
Approval: needs human approval for critical things
Production principal
Reliable and fallback
Cost and latency
Content and retention
Security and privacy
