What Are AI Agents?
An AI agent is a system that can perceive its environment, make decisions, and take actions to achieve specific goals. Unlike traditional chatbots that respond to single prompts, agents can plan multi-step workflows, use tools, and adapt their approach based on results.
Core Components
Every AI agent has three fundamental components:
1. Perception
The agent receives input from its environment — this could be user messages, API responses, file contents, or sensor data.
2. Reasoning
The agent processes inputs using an LLM to decide what action to take next. This includes planning, decomposition of tasks, and self-reflection.
3. Action
The agent executes actions — calling APIs, writing files, sending messages, or running code.
A Simple Example
Here's a conceptual agent loop:
class Agent:
def __init__(self, llm, tools):
self.llm = llm
self.tools = tools
def run(self, task: str, max_steps: int = 10):
messages = [{"role": "user", "content": task}]
for step in range(max_steps):
response = self.llm.chat(messages)
if response.is_final_answer:
return response.content
action = self.parse_action(response)
result = self.tools.execute(action)
messages.append({"role": "assistant", "content": response.content})
messages.append({"role": "user", "content": f"Tool result: {result}"})
return "Max steps reached"When to Use Agents
AI agents excel when:
- Tasks require multiple steps or decisions
- The agent needs to access external tools or APIs
- The problem space is too large for a single prompt
- You need autonomous operation with human oversight
What's Next
In upcoming articles, we'll dive deeper into agent architectures, multi-agent systems, and building production-ready agents. Stay tuned.