Agents Overview¶
What is an Agent?¶
An AI Agent is a system that can:
- Understand a goal
- Make decisions
- Execute actions
- Observe results
- Adapt its behavior
Unlike a traditional Large Language Model (LLM), which simply generates the next token, an Agent can interact with external environments and perform tasks autonomously.
From LLM to Agent¶
A basic LLM works like this:
User Input
↓
LLM
↓
Response
An Agent extends this capability:
User Goal
↓
Agent
├── Reasoning
├── Planning
├── Memory
└── Tool Usage
↓
Action
↓
Observation
↓
Next Action
The Agent operates in a continuous loop until the goal is completed.
Core Components of an Agent¶
Reasoning¶
The Agent analyzes the current situation and decides what to do next.
Example:
Goal:
Find the latest OpenAI model release.
The Agent may reason:
I should search the web first.
Planning¶
Complex tasks are broken into smaller steps.
Example:
Build a blog website
1. Create project
2. Configure theme
3. Write content
4. Deploy
This process is called task decomposition.
Memory¶
Memory allows the Agent to retain information across multiple steps.
Examples:
- Previous conversation history
- Retrieved documents
- Completed tasks
- User preferences
Without memory, every step becomes isolated.
Tools¶
Agents interact with the world through tools.
Examples:
- Search engines
- Databases
- File systems
- APIs
- Browsers
- Code interpreters
A modern Agent is often described as:
LLM + Tools
The Agent Loop¶
Most agents follow a simple cycle:
Observe
↓
Think
↓
Plan
↓
Act
↓
Observe
or
Observation
↓
Reasoning
↓
Action
↓
Observation
This loop continues until the objective is achieved.
Agent Architecture¶
A common architecture looks like:
User
│
▼
┌──────────────────────┐
│ Agent │
│──────────────────────│
│ LLM │
│ Planning │
│ Memory │
│ Tools │
│ Reflection │
└─────────┬────────────┘
│
▼
Environment
│
▼
Observation
│
└──────────► Agent
Agent vs Traditional Software¶
| Traditional Software | AI Agent |
|---|---|
| Fixed logic | Dynamic reasoning |
| Predefined workflow | Adaptive workflow |
| Explicit rules | Goal-driven behavior |
| Deterministic | Probabilistic |
| Limited flexibility | High flexibility |
Traditional software answers:
How should this task be executed?
Agents answer:
What should I do next to achieve the goal?
Single-Agent Systems¶
A single agent handles the entire task.
Example:
User
↓
Agent
↓
Tools
Advantages:
- Simple architecture
- Easy deployment
- Lower operational cost
Disadvantages:
- Limited specialization
- Context grows quickly
- Harder to scale
Multi-Agent Systems¶
Multiple agents collaborate together.
Example:
Manager Agent
│
┌────┴────┐
▼ ▼
Research Coding
Agent Agent
Each agent has a specific responsibility.
Benefits:
- Better specialization
- Parallel execution
- Improved scalability
Challenges:
- Communication overhead
- Coordination complexity
- Increased cost
Common Agent Capabilities¶
Modern agents often support:
- Tool Calling
- Function Calling
- Web Search
- Browser Automation
- Code Execution
- File Manipulation
- Task Planning
- Reflection
- Long-Term Memory
- Multi-Agent Collaboration
Popular Agent Patterns¶
ReAct¶
Reason + Act
Thought
↓
Action
↓
Observation
↓
Thought
One of the most widely used agent architectures.
Plan-and-Execute¶
Create Plan
↓
Execute Step 1
↓
Execute Step 2
↓
Execute Step N
Suitable for long and complex tasks.
Reflection¶
The agent reviews its own work and improves the result.
Generate
↓
Review
↓
Improve
Agent Challenges¶
Building reliable agents remains difficult.
Common problems include:
- Hallucinations
- Poor planning
- Tool failures
- Context limitations
- Memory drift
- High latency
- High operational cost
These challenges are active areas of research.
Real-World Agent Systems¶
Examples of agent-based systems include:
- OpenCode
- OpenClaw
- Claude Code
- Cursor Agent
- Devin
- Manus
- Browser-based AI assistants
Each system combines reasoning, memory, planning, and tools in different ways.
Key Takeaways¶
An Agent is more than an LLM.
Agent = LLM
+ Reasoning
+ Planning
+ Memory
+ Tools
Agents operate through a continuous loop:
Observe
→ Think
→ Plan
→ Act
The goal of an Agent is not merely to generate text,
but to achieve objectives within an environment.