Agent
noviceAn autonomous AI system capable of perceiving its environment, making decisions, and taking actions to achieve specified goals. Modern AI agents typically combine large language models with tool access and memory systems.
Overview
An AI agent is more than just a chatbot—it's a system designed to accomplish tasks autonomously. While a traditional chatbot responds to individual messages, an agent maintains context, uses tools, and works toward goals across multiple steps. Think of the difference between asking someone a question versus hiring them to complete a project. A chatbot answers your question; an agent takes on the project and figures out what steps are needed to complete it. Modern agents are built on large language models (LLMs) but add crucial capabilities: memory to remember past interactions, tools to interact with external systems, and planning abilities to break down complex tasks.
Key Concepts
Perception
The ability to receive and interpret input from the environment—user messages, API responses, sensor data, or file contents.
Reasoning
Using the LLM to analyze situations, plan approaches, and make decisions about what actions to take.
Action
Executing operations in the real world through tools, APIs, or other interfaces.
Memory
Retaining information across interactions to maintain context and learn from experience.
Code Examples
from openai import OpenAI
client = OpenAI()
def run_agent(task: str):
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": task}
]
while True:
response = client.chat.completions.create(
model="gpt-4",
messages=messages
)
assistant_message = response.choices[0].message
# Check if agent wants to use a tool
if assistant_message.tool_calls:
# Execute tools and continue
tool_results = execute_tools(assistant_message.tool_calls)
messages.append(assistant_message)
messages.extend(tool_results)
else:
# Agent is done
return assistant_message.contentThis shows the basic agent loop: receive input, reason about it, optionally use tools, and either continue or return a result.
Real-World Use Cases
- 1Customer service agents that can look up orders, process returns, and escalate to humans when needed
- 2Research assistants that search databases, synthesize information, and write reports
- 3DevOps agents that monitor systems, diagnose issues, and execute fixes
- 4Personal assistants that manage calendars, send emails, and coordinate tasks
Practical Tips
- •Start with narrow, well-defined tasks before expanding agent capabilities
- •Always implement a maximum iteration limit to prevent runaway loops
- •Use structured outputs to make tool calls more reliable
- •Build in human-in-the-loop checkpoints for high-stakes decisions
Common Mistakes to Avoid
- ✗Giving agents too much autonomy without proper guardrails and human oversight
- ✗Not implementing proper error handling for when tools fail
- ✗Ignoring cost implications of long-running agent loops
- ✗Failing to log agent actions for debugging and audit purposes