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What Is an AI Agent? The Complete Guide for Developers

AI Agents•Alin Dobra••11 min read

What Is an AI Agent? The Complete Guide for Developers

Everyone's talking about AI agents. But strip away the hype, and you'll find most "agents" are just chatbots with extra steps. So what actually makes an AI system an agent?

This guide cuts through the noise to give you a clear, practical understanding of AI agents—what they are, how they differ from regular LLMs, and what it takes to build production-ready autonomous systems.

The Definition Problem

Ask ten developers what an AI agent is, and you'll get twelve answers:

  • "It's an LLM that can use tools"
  • "It's an autonomous system that pursues goals"
  • "It's a chatbot with memory"
  • "It's anything that runs in a loop"

All of these capture part of the picture. None capture all of it.

Here's a working definition that actually helps:

An AI agent is a system that uses an LLM to decide what actions to take, executes those actions, observes the results, and iterates until a goal is achieved—with minimal human intervention.

The key phrase is minimal human intervention. A chatbot waits for your next message. An agent figures out what to do next on its own.

Agent vs. Chatbot: The Core Difference

text
1
CHATBOT                           AGENT
2
────────                          ─────
3
User: "Analyze sales data"        User: "Analyze sales data"
4
Bot: "Here's the analysis..."     Agent: *thinks* Need to:
5
                                        1. Find the data file
6
User: "Now make a chart"                2. Load and clean it
7
Bot: "Here's a chart..."                3. Run analysis
8
                                        4. Create visualizations
9
User: "Email it to my team"             5. Generate report
10
Bot: "Here's a draft..."
11
                                  Agent: *executes all steps*
12
User: "Actually send it"          Agent: "Done. Report sent to
13
Bot: "Email sent"                        team@company.com"
14
 

The chatbot needs four prompts. The agent needs one.

This isn't just about convenience. It's about capability. Some tasks are simply impossible to complete through back-and-forth conversation—they require autonomous execution.

The Four Pillars of Agentic Systems

Every true AI agent has four essential components. Miss any one, and you have something less than an agent.

1. Goal Interpretation

The agent must understand what you want to achieve, not just what you said.

python
1
# User says:
2
"Make our website faster"
3
 
4
# Chatbot interprets:
5
"Tell user about website optimization techniques"
6
 
7
# Agent interprets:
8
"Goal: Reduce website load time"
9
"Sub-goals:"
10
"  - Analyze current performance"
11
"  - Identify bottlenecks"
12
"  - Implement optimizations"
13
"  - Verify improvements"
14
 

Goal interpretation means converting fuzzy human intent into concrete, measurable objectives.

2. Planning

Given a goal, the agent must decide how to achieve it—breaking complex tasks into executable steps.

text
1
Goal: "Deploy the new feature to production"
2
 
3
Plan:
4
├── 1. Run test suite
5
│   └── If tests fail → Fix issues → Re-run
6
├── 2. Build production bundle
7
├── 3. Create database migration
8
├── 4. Deploy to staging
9
├── 5. Run smoke tests
10
│   └── If smoke tests fail → Rollback → Investigate
11
├── 6. Deploy to production
12
└── 7. Monitor for errors
13
 

Good planning includes:

  • Task decomposition — Breaking big tasks into small ones
  • Dependency management — Understanding what must happen first
  • Contingency handling — Knowing what to do when things fail

3. Tool Use

Agents interact with the world through tools. A tool is any function the agent can call:

python
1
# Example agent tools
2
tools = [
3
    {
4
        "name": "read_file",
5
        "description": "Read contents of a file",
6
        "parameters": {"path": "string"}
7
    },
8
    {
9
        "name": "write_file",
10
        "description": "Write content to a file",
11
        "parameters": {"path": "string", "content": "string"}
12
    },
13
    {
14
        "name": "run_code",
15
        "description": "Execute Python code",
16
        "parameters": {"code": "string"}
17
    },
18
    {
19
        "name": "search_web",
20
        "description": "Search the internet",
21
        "parameters": {"query": "string"}
22
    },
23
    {
24
        "name": "send_email",
25
        "description": "Send an email",
26
        "parameters": {"to": "string", "subject": "string", "body": "string"}
27
    }
28
]
29
 

Tools are what give agents real-world impact. An LLM can describe how to analyze data. An agent with tools can actually analyze the data.

4. Observation & Iteration

The agent must observe the results of its actions and decide what to do next. This is the agent loop:

text
1
┌─────────────────────────────────────────────────────┐
2
│                                                     │
3
│    ┌─────────┐    ┌─────────┐    ┌──────────┐      │
4
│    │  Think  │───▶│   Act   │───▶│ Observe  │      │
5
│    └─────────┘    └─────────┘    └──────────┘      │
6
│         ▲                              │           │
7
│         │                              │           │
8
│         └──────────────────────────────┘           │
9
│                                                     │
10
│    Repeat until: goal achieved OR max steps OR     │
11
│                  agent decides to stop             │
12
│                                                     │
13
└─────────────────────────────────────────────────────┘
14
 

This loop is what makes agents autonomous. They don't just act once—they act, learn from results, and adapt.

The Agent Spectrum

Not all agents are equally autonomous. Think of it as a spectrum:

text
1
LOW AUTONOMY                                    HIGH AUTONOMY
2
──────────────────────────────────────────────────────────────▶
3
 
4
│           │               │                  │              │
5
Chatbot     Copilot         Task Agent         Goal Agent     Fully
6
                                                              Autonomous
7
│           │               │                  │              │
8
Single      Suggests        Executes           Plans &        Discovers
9
response    actions,        specific           executes       own goals,
10
            human           task               multi-step     self-improves
11
            confirms        sequences          plans
12
 
13
Example:    GitHub          Code               Research       AGI
14
ChatGPT     Copilot         Interpreter        Agents         (theoretical)
15
 

Most production AI systems today operate in the "Task Agent" zone—autonomous enough to be useful, constrained enough to be safe.

Anatomy of an Agent: Code Walkthrough

Let's look at a minimal but complete agent implementation:

python
1
import openai
2
import json
3
from typing import Callable
4
 
5
class SimpleAgent:
6
    """A minimal agent implementation demonstrating core concepts"""
7
8
    def __init__(self, tools: dict[str, Callable], max_iterations: int = 10):
9
        self.client = openai.OpenAI()
10
        self.tools = tools
11
        self.max_iterations = max_iterations
12
        self.memory = []  # Conversation history
13
14
    def run(self, goal: str) -> str:
15
        """Main agent loop"""
16
17
        # Initialize with goal
18
        self.memory.append({
19
            "role": "system",
20
            "content": f"""You are an AI agent. Your goal: {goal}
21
22
Available tools: {list(self.tools.keys())}
23
 
24
Respond with JSON:
25
{{"thought": "your reasoning", "action": "tool_name", "action_input": {{...}}}}
26
 
27
When the goal is complete, respond:
28
{{"thought": "goal achieved because...", "action": "finish", "result": "final answer"}}"""
29
        })
30
31
        for iteration in range(self.max_iterations):
32
            # THINK: Get LLM decision
33
            response = self.client.chat.completions.create(
34
                model="gpt-4o",
35
                messages=self.memory,
36
                response_format={"type": "json_object"}
37
            )
38
39
            decision = json.loads(response.choices[0].message.content)
40
            self.memory.append({"role": "assistant", "content": json.dumps(decision)})
41
42
            print(f"[Step {iteration + 1}] {decision['thought']}")
43
44
            # CHECK: Is goal complete?
45
            if decision["action"] == "finish":
46
                return decision["result"]
47
48
            # ACT: Execute the tool
49
            tool_name = decision["action"]
50
            tool_input = decision["action_input"]
51
52
            if tool_name not in self.tools:
53
                observation = f"Error: Unknown tool '{tool_name}'"
54
            else:
55
                try:
56
                    observation = self.tools[tool_name](**tool_input)
57
                except Exception as e:
58
                    observation = f"Error: {str(e)}"
59
60
            # OBSERVE: Record the result
61
            self.memory.append({
62
                "role": "user",
63
                "content": f"Observation: {observation}"
64
            })
65
66
            print(f"[Observation] {observation[:200]}...")
67
68
        return "Max iterations reached without completing goal"
69
 

Usage:

python
1
# Define tools
2
def read_file(path: str) -> str:
3
    with open(path) as f:
4
        return f.read()
5
 
6
def write_file(path: str, content: str) -> str:
7
    with open(path, 'w') as f:
8
        f.write(content)
9
    return f"Written {len(content)} bytes to {path}"
10
 
11
def run_python(code: str) -> str:
12
    # ⚠️ UNSAFE: See security section below
13
    exec_globals = {}
14
    exec(code, exec_globals)
15
    return str(exec_globals.get('result', 'No result'))
16
 
17
# Create and run agent
18
agent = SimpleAgent(
19
    tools={
20
        "read_file": read_file,
21
        "write_file": write_file,
22
        "run_python": run_python
23
    }
24
)
25
 
26
result = agent.run(
27
    "Read data.csv, calculate the average of the 'price' column, "
28
    "and save the result to result.txt"
29
)
30
 

Output:

text
1
[Step 1] I need to first read the data file to understand its contents
2
[Observation] id,name,price\n1,Widget,29.99\n2,Gadget,49.99...
3
 
4
[Step 2] Now I'll write Python code to calculate the average price
5
[Observation] 39.99
6
 
7
[Step 3] I'll save the result to result.txt
8
[Observation] Written 5 bytes to result.txt
9
 
10
[Step 4] Task complete - I've calculated the average and saved it
11
Result: "The average price is 39.99, saved to result.txt"
12
 

The Security Problem: Why Agents Need Isolation

Notice the warning comment on run_python above? This is where most AI agents fail in production.

When an agent executes code, it's running LLM-generated instructions. LLMs can:

  • Hallucinate dangerous commands
  • Be manipulated by prompt injection
  • Produce syntactically valid but harmful code

Real example of what an LLM might generate when asked to "clean up disk space":

python
1
import os
2
import shutil
3
 
4
# "Cleaning up" by removing files
5
for item in os.listdir('/'):
6
    if item not in ['bin', 'boot', 'etc']:  # Hallucinated "safe" list
7
        shutil.rmtree(f'/{item}')  # Deletes critical system directories
8
 

The solution is isolated code execution. Every code action runs in a sandbox that can't affect your real systems:

python
1
from hopx import Sandbox
2
 
3
def safe_run_python(code: str) -> str:
4
    """Execute code in isolated sandbox"""
5
    sandbox = Sandbox.create(template="code-interpreter")
6
7
    try:
8
        sandbox.files.write("/app/code.py", code)
9
        result = sandbox.commands.run("python /app/code.py")
10
        return result.stdout if result.exit_code == 0 else f"Error: {result.stderr}"
11
    finally:
12
        sandbox.kill()  # Sandbox destroyed - nothing persists
13
 

The sandbox:

  • Has its own filesystem (can't read your files)
  • Has its own network (can't exfiltrate data)
  • Has resource limits (can't mine crypto)
  • Is destroyed after execution (can't persist malware)

For a deep dive on this topic, see Why AI Agents Need Isolated Code Execution.

Common Agent Patterns

Pattern 1: ReAct (Reasoning + Acting)

The most common pattern. The agent explicitly reasons before each action:

text
1
Thought: I need to find the user's most recent order
2
Action: query_database
3
Action Input: {"query": "SELECT * FROM orders WHERE user_id=123 ORDER BY date DESC LIMIT 1"}
4
 
5
Observation: {"order_id": 456, "status": "shipped", "date": "2024-01-15"}
6
 
7
Thought: Found the order. Now I need to get tracking information
8
Action: get_tracking
9
Action Input: {"order_id": 456}
10
 
11
Observation: {"carrier": "UPS", "tracking": "1Z999...", "eta": "2024-01-18"}
12
 
13
Thought: I have all the information needed to answer the user
14
Action: finish
15
Result: "Your order #456 was shipped via UPS. Tracking: 1Z999... Expected delivery: Jan 18"
16
 

Pattern 2: Plan-and-Execute

The agent creates a full plan upfront, then executes it:

python
1
class PlanAndExecuteAgent:
2
    def run(self, goal: str):
3
        # Phase 1: Planning
4
        plan = self._create_plan(goal)
5
        # Returns: ["Step 1: ...", "Step 2: ...", "Step 3: ..."]
6
7
        # Phase 2: Execution
8
        for step in plan:
9
            result = self._execute_step(step)
10
11
            # Optional: Replan if step failed
12
            if not result.success:
13
                plan = self._replan(goal, completed_steps, step, result.error)
14
15
        return self._synthesize_results()
16
 

Better for complex, multi-stage tasks. Worse for exploratory tasks where the next step depends heavily on previous results.

Pattern 3: Reflection

The agent reviews its own work before finishing:

text
1
[After completing task]
2
 
3
Self-Review:
4
- Did I answer the original question? ✓
5
- Did I miss any edge cases? Found one: empty input
6
- Is the code efficient? Could optimize the loop
7
- Any security issues? Need to sanitize input
8
 
9
[Agent decides to improve before finishing]
10
 

Adding reflection significantly improves agent output quality at the cost of more LLM calls.

Building Production Agents: Checklist

Ready to build? Here's what you need:

Infrastructure

  • LLM access — OpenAI, Anthropic, or self-hosted
  • Isolated execution — Sandboxes for code/commands (HopX, E2B, or self-built)
  • Persistent memory — Vector DB for long-term context
  • Observability — Logging every thought and action

Safety Controls

  • Max iteration limit — Prevent infinite loops
  • Cost limits — Cap LLM API spending
  • Action allowlists — Restrict dangerous operations
  • Human-in-the-loop — Approval for high-stakes actions

User Experience

  • Streaming output — Show progress, not just final result
  • Cancellation — Let users stop runaway agents
  • Transparency — Show what the agent is doing and why

When NOT to Build an Agent

Agents aren't always the answer. Use a simpler approach when:

ScenarioBetter Alternative
Task is predictableHardcoded workflow
Single LLM call sufficesSimple prompt
User wants full controlCopilot (suggestions)
Errors are catastrophicHuman-in-the-loop pipeline
Real-time latency requiredPre-computed responses

Agents add complexity. Only use them when that complexity buys you something—typically handling unpredictable, multi-step tasks that can't be templated.

The Future of Agents

We're still early. Today's agents are impressive but limited:

Current limitations:

  • Expensive (many LLM calls per task)
  • Slow (sequential reasoning)
  • Unreliable (hallucinations compound)
  • Narrow (struggle with truly novel tasks)

What's coming:

  • Cheaper models — More reasoning per dollar
  • Better planning — Fewer wasted steps
  • Multi-agent systems — Specialized agents collaborating
  • Learning from experience — Agents that improve over time

The agents of 2025 will make today's agents look primitive. But the fundamentals—goal interpretation, planning, tool use, observation—will remain constant.

Start Building

Here's your quickstart path:

  1. Understand the loop — Build the minimal agent above
  2. Add real tools — File operations, web search, API calls
  3. Add safety — Isolate code execution with sandboxes
  4. Add memory — Let agents learn from past sessions
  5. Add streaming — Show users what's happening
  6. Iterate — Watch agents fail, improve, repeat

The best way to understand agents is to build one. Start simple, add complexity only when needed, and always prioritize safety.

Ready to build agents that execute code safely? Get started with HopX — isolated sandboxes that spin up in 100ms.

Further Reading

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