Agent Loops Explained: The Observe-Think-Act Cycle That Powers AI Agents

The Agent Loop Is the Heart of Every Agent

If you strip away the frameworks, the fancy UIs, and the marketing, every AI agent is fundamentally a loop. The agent receives input, thinks about what to do, takes an action, observes the result, and repeats — until it either achieves its goal or hits a stopping condition.

Understanding this loop deeply is the single most important concept in agentic AI. Everything else — tools, memory, planning, multi-agent systems — is built on top of this core cycle.

Anatomy of the Agent Loop

┌─────────────────────────────┐
│         User Goal           │
└──────────┬──────────────────┘
           │
           ▼
┌─────────────────────────────┐
│   1. OBSERVE                │◄──────────────┐
│   Gather current state      │               │
└──────────┬──────────────────┘               │
           │                                  │
           ▼                                  │
┌─────────────────────────────┐               │
│   2. THINK                  │               │
│   Reason about next step    │               │
└──────────┬──────────────────┘               │
           │                                  │
           ▼                                  │
┌─────────────────────────────┐               │
│   3. ACT                    │               │
│   Execute tool or respond   │───────────────┘
└──────────┬──────────────────┘
           │ (if final response)
           ▼
┌─────────────────────────────┐
│      Result to User         │
└─────────────────────────────┘

Each pass through the loop is one iteration or step. A simple question might take one iteration (the agent has enough knowledge to answer immediately). A complex task like "research competitors and write a report" might take 15-20 iterations.

flowchart TD
    Q{"Pick by primary<br/>design constraint"}
    NEED1{"Need explicit<br/>state graph plus<br/>checkpoints?"}
    NEED2{"Need role and task<br/>based teams?"}
    NEED3{"Need conversation<br/>style multi agent?"}
    NEED4{"Need full control<br/>Claude native?"}
    LG[/"LangGraph"/]
    CR[/"CrewAI"/]
    AG[/"AutoGen"/]
    CS[/"Claude Agent SDK"/]
    Q --> NEED1
    NEED1 -->|Yes| LG
    NEED1 -->|No| NEED2
    NEED2 -->|Yes| CR
    NEED2 -->|No| NEED3
    NEED3 -->|Yes| AG
    NEED3 -->|No| NEED4
    NEED4 -->|Yes| CS
    style Q fill:#4f46e5,stroke:#4338ca,color:#fff
    style LG fill:#0ea5e9,stroke:#0369a1,color:#fff
    style CR fill:#f59e0b,stroke:#d97706,color:#1f2937
    style AG fill:#ede9fe,stroke:#7c3aed,color:#1e1b4b
    style CS fill:#059669,stroke:#047857,color:#fff

Implementing the Loop from Scratch

Here is a clean, production-style agent loop:

import json
from dataclasses import dataclass
from openai import OpenAI

client = OpenAI()

@dataclass
class LoopConfig:
    max_iterations: int = 15
    model: str = "gpt-4o"
    system_prompt: str = "You are a helpful agent with access to tools."

@dataclass
class LoopResult:
    output: str
    iterations: int
    terminated_reason: str  # "complete", "max_iterations", "error"

def agent_loop(goal: str, tools: list, tool_executor, config: LoopConfig = None) -> LoopResult:
    config = config or LoopConfig()
    messages = [
        {"role": "system", "content": config.system_prompt},
        {"role": "user", "content": goal},
    ]

    for iteration in range(1, config.max_iterations + 1):
        try:
            response = client.chat.completions.create(
                model=config.model,
                messages=messages,
                tools=tools if tools else None,
            )
        except Exception as e:
            return LoopResult(
                output=f"API error: {e}",
                iterations=iteration,
                terminated_reason="error",
            )

        choice = response.choices[0]
        assistant_msg = choice.message
        messages.append(assistant_msg)

        # Termination: no tool calls means the agent is done
        if not assistant_msg.tool_calls:
            return LoopResult(
                output=assistant_msg.content or "",
                iterations=iteration,
                terminated_reason="complete",
            )

        # Execute tools and append observations
        for tool_call in assistant_msg.tool_calls:
            args = json.loads(tool_call.function.arguments)
            result = tool_executor(tool_call.function.name, args)
            messages.append({
                "role": "tool",
                "tool_call_id": tool_call.id,
                "content": json.dumps(result),
            })

    # Fell through the loop without completing
    return LoopResult(
        output="Task incomplete — reached maximum iterations.",
        iterations=config.max_iterations,
        terminated_reason="max_iterations",
    )

Termination Conditions

How does the loop know when to stop? There are four common termination strategies.

Natural completion is the most common. The LLM decides it has enough information and returns a text response instead of a tool call. This is implicit — no special logic needed.

Maximum iterations is your safety net. Always set a cap. Without one, a confused agent can loop forever, burning tokens and money.

Explicit stop signals are useful when you want the agent to indicate completion through a specific tool call, like a task_complete function:

tools = [
    # ... other tools ...
    {
        "type": "function",
        "function": {
            "name": "task_complete",
            "description": "Call this when the task is fully complete.",
            "parameters": {
                "type": "object",
                "properties": {
                    "summary": {"type": "string", "description": "Summary of what was accomplished"}
                },
                "required": ["summary"],
            },
        },
    },
]

Budget-based termination stops the loop when token usage or cost exceeds a threshold. This is important for production systems where cost control matters:

total_tokens = 0

for iteration in range(max_iterations):
    response = client.chat.completions.create(...)
    total_tokens += response.usage.total_tokens

    if total_tokens > 50_000:  # Token budget exceeded
        break

Preventing Infinite Loops and Runaway Costs

The biggest operational risk with agent loops is runaway execution. An agent gets stuck in a cycle — repeatedly calling the same tool with the same arguments, or alternating between two states without making progress.

Three defensive patterns handle this:

1. Duplicate action detection: Track the last N tool calls. If the agent calls the same tool with the same arguments three times in a row, force termination.

2. Progress checks: Every K iterations, inject a system message asking the agent to evaluate whether it is making progress toward the goal.

3. Exponential backoff on failures: If a tool call fails, do not let the agent immediately retry. Add a delay or ask it to try a different approach.

FAQ

How many iterations does a typical agent task require?

Simple lookup tasks (search and summarize) typically take 2-4 iterations. Multi-step workflows (research, analyze, write) take 8-15 iterations. Tasks beyond 20 iterations usually indicate the problem should be decomposed into smaller subtasks rather than handled in a single loop.

What happens if an agent hits the max iteration limit?

The agent returns whatever partial result it has accumulated. Best practice is to return the last assistant message along with a flag indicating the task was incomplete, so the calling code can decide whether to continue, retry with a different strategy, or escalate to a human.

Should I use synchronous or asynchronous loops?

Use asynchronous loops for production systems. Synchronous loops block the thread during each API call, which does not scale when handling multiple concurrent agent sessions. Python's asyncio with await client.chat.completions.create() is the standard approach.

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