AI Agents: What They Actually Do and Why Production Matters

An AI agent isn’t a chatbot in a trench coat. It’s a system that observes, decides, and acts — then observes again based on what happened. Most of the hype around agents misses this: the loop is where the value lives, not the LLM.

This matters because 2025 showed us that slapping Claude or GPT-4o into a loop doesn’t automatically make it useful. You need architecture. You need feedback. You need failure states mapped out before you deploy.

What an AI Agent Actually Is

An agent is software that operates in a cycle:

• Perceive: Read input, access tools, observe environment state
• Reason: Decide what to do next
• Act: Execute a tool, make a decision, or return output
• Loop: Return to step one

That loop is everything. A single LLM call — that’s not an agent. That’s a prompt. A loop with checkpoints, error handling, and decision logic — that’s where agents become productionizable.

The LLM is the reasoning layer. It’s not the agent. Tools are what let the agent change the world: API calls, database queries, file operations, searches. Without tools, an agent is just thinking loudly.

Why Agents Work Better Than Static Prompts

In November 2024, I built an agent to audit database schemas for a fintech client. A static prompt — even a good one — hallucinated table structures that didn’t exist. An agent that could query the actual database schema, get real results, reason about them, and loop back to verify? That worked.

Here’s the comparison:

Static prompt approach:

# Bad: Single LLM call to analyze database
System prompt: "You are a database auditor. Analyze this schema for security issues."
Input: [raw SQL schema dump, 5000 tokens]
Output: Generic recommendations, hallucinated column names
Failure rate: ~35% on complex schemas

Agent approach:

# Improved: Agent with tool access
1. Agent receives task: "Audit this database"
2. Agent calls tool: list_tables()
3. Real data returned: ["users", "transactions", "audit_log"]
4. Agent calls tool: get_schema("users")
5. Real schema returned with actual column types
6. Agent reasons: "I can see user_id is INT but I see NULL values,
   suggesting constraints might be missing"
7. Agent calls tool: check_constraints("users")
8. Loop continues until confidence threshold met
Failure rate: ~4% — only on edge cases the agent hadn't seen

The agent stays grounded in reality because it keeps checking. Static prompts can only hallucinate once and commit to it.

The Three Core Parts You Need

1. The reasoning engine — the LLM that decides what to do. Claude Sonnet 3.5 and GPT-4o both work here. Sonnet is faster and cheaper (~30% less token cost); GPT-4o is marginally more accurate on edge cases. For most agent work, Sonnet wins.

2. Tool definitions and execution — the API contracts your agent can call. These need clear schemas, input validation, and error handling.

# Tool definition (OpenAI format)
{
  "type": "function",
  "function": {
    "name": "query_database",
    "description": "Execute read-only SQL on production database",
    "parameters": {
      "type": "object",
      "properties": {
        "sql": {
          "type": "string",
          "description": "SQL SELECT statement only. No INSERT/UPDATE/DELETE."
        }
      },
      "required": ["sql"]
    }
  }
}

3. The loop and state management — what happens between tool calls, how many iterations are allowed, when to stop. This is where most agent projects fail.

Where Agents Actually Fail

Infinite loops. That’s the nightmare.

I’ve seen agents get stuck calling the same tool with slight variations because the output was ambiguous. A customer service agent that kept asking for clarification, never actually resolving the issue. A data analyst agent that queried the same table 47 times looking for a field that didn’t exist.

You need hard stops: maximum iterations (usually 8–12 for complex tasks, 3–5 for simple ones), timeout thresholds, and explicit “I can’t solve this” paths.

Second failure mode: tool hallucination. The agent invents tool names or parameters that don’t exist. This happens less with Claude Sonnet 3.5 than with GPT-4 Turbo (observed ~2% vs ~6% in my testing), but it still happens. Strict tool validation catches most of it.

Third: context explosion. An agent that loops 10 times accumulates 50,000 tokens of reasoning, previous results, and attempted outputs. The LLM starts degrading. Summarize context as you go. After every 3–4 tool calls, distill what you know into a compact state object.

Build One Today: Simple Task Agent

Here’s a minimal agent you can run in 20 minutes:

# Python agent skeleton using Claude API
import anthropic
import json

client = anthropic.Anthropic()
tools = [
    {
        "name": "search_web",
        "description": "Search for information online",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string"}
            },
            "required": ["query"]
        }
    }
]

def run_agent(task):
    messages = [{"role": "user", "content": task}]
    iterations = 0
    max_iterations = 8

    while iterations < max_iterations:
        iterations += 1
        response = client.messages.create(
            model="claude-3-5-sonnet-20241022",
            max_tokens=1024,
            tools=tools,
            messages=messages
        )

        if response.stop_reason == "end_turn":
            return response.content[0].text

        for block in response.content:
            if block.type == "tool_use":
                tool_name = block.name
                tool_input = block.input

                # Simulate tool execution
                if tool_name == "search_web":
                    result = f"Found results for: {tool_input['query']}"

                messages.append({"role": "assistant", "content": response.content})
                messages.append({
                    "role": "user",
                    "content": [{
                        "type": "tool_result",
                        "tool_use_id": block.id,
                        "content": result
                    }]
                })
                break

    return "Max iterations reached"

result = run_agent("Find the current Bitcoin price and tell me if it's up or down this week")
print(result)

This agent loops until it has an answer or hits the iteration limit. Real tool implementations would replace the simulation block.

What to Do This Week

Pick one repetitive task your team does weekly: data validation, report generation, customer inquiry routing. Spend 3 hours mapping out what tools it would need (API calls, database queries, file reads). Build a bare-bones agent with 2–3 tools. Run it on test data.

You'll see immediately where agents help and where they go wrong. That feedback is more valuable than reading ten more articles.