What Are GPT Agents and How Do They Work?

GPT agents are systems that use a GPT or another large language model to pursue a goal across multiple steps. The model helps decide what to do next, the runtime executes configured tools, and the loop continues until it reaches a final result, an approval point, an error, or another stop condition. OpenAI describes agents as systems that independently accomplish tasks on a user’s behalf, while distinguishing them from single-turn chatbots and classifiers that do not control workflow execution (OpenAI’s practical guide to building agents).

An agent is not unlimited autonomy. The application defines its instructions, tools, permissions, state, guardrails, confirmation steps, and stopping rules. The model may choose among tools, but your application or an agent runtime configures and executes those tools.

What makes a GPT agent different from a chatbot?

System	Typical behavior	Agent?
Single-turn model call	Receives input and returns one answer	Usually no
Chatbot with conversation history	Answers follow-up messages but does not execute a workflow	Usually no
Classifier or extractor	Maps input to a label or structured record	No, by itself
Tool-using workflow	Chooses functions, observes results, and repeats until done	Yes
Multi-agent workflow	Delegates subtasks or hands work to specialist agents	Yes

The defining property is workflow control: the model participates in deciding and advancing the sequence of work. A chat interface can contain an agent, but a chat interface alone does not make an application an agent.

The GPT agent loop, step by step

1. Receive a goal. The user request, system instructions, policies, and relevant state are assembled.
2. Ask the model what to do next. The model receives the context and the descriptions of available tools.
3. Inspect the response. It may return a final answer, request a tool, hand off to a specialist, ask for approval, or produce an error.
4. Execute configured work. The runtime validates arguments and runs the selected function, API call, database query, browser action, or remote MCP tool.
5. Return the observation. The tool result is added to the conversation or run state.
6. Continue or stop. The loop repeats until a final output, maximum-turn limit, error policy, human approval, or other exit condition is reached.

OpenAI’s running agents guide presents this repeated model-and-tool process as the core run loop. Exact state storage, streaming behavior, retries, and handoffs depend on the runtime you choose.

The three building blocks

1. Model

The language model interprets the goal, reasons over supplied context, selects tools, generates arguments, and decides when it has enough information to finish. Model choice affects capability, latency, and cost; the agent design should not assume that a model is always correct.

2. Tools

Tools expose operations the model cannot perform from text alone. Data tools retrieve context, such as records, documents, or search results. Action tools change external systems, such as sending a message or updating a ticket. Orchestration tools call another specialist agent.

Tools can be hosted capabilities, application-defined function calls, programmatic calls, or remote MCP servers. The host application determines which tools exist and what permissions they have (OpenAI’s tools guide).

3. Instructions and guardrails

Instructions define the role, allowed behavior, output requirements, and escalation rules. Guardrails validate inputs, tool arguments, outputs, and side effects. They can include schemas, allowlists, moderation checks, regular expressions, rate limits, confirmation prompts, and human review.

A complete, runnable Python example

The following self-contained example demonstrates the loop with a deterministic mock model and two tools. It runs without an API key so you can see the control flow before connecting a real model or runtime.

from dataclasses import dataclass
from typing import Any, Callable

@dataclass
class Tool:
    name: str
    description: str
    run: Callable[..., Any]


def lookup_order(order_id: str) -> dict:
    orders = {"A100": {"status": "shipped", "eta": "Friday"}}
    return orders.get(order_id, {"status": "not_found"})


def format_answer(order_id: str, order: dict) -> str:
    if order["status"] == "not_found":
        return f"Order {order_id} was not found."
    return f"Order {order_id} is {order['status']} and is expected {order['eta']}."


TOOLS = {
    "lookup_order": Tool(
        "lookup_order",
        "Look up an order by its ID.",
        lookup_order,
    )
}


def mock_model(messages: list[dict], tool_descriptions: list[dict]) -> dict:
    """A stand-in for a GPT call. Replace this with your model API."""
    last = messages[-1]
    if last["role"] == "user":
        words = last["content"].split()
        order_id = next((w for w in words if w.startswith("A") and w[1:].isdigit()), None)
        if order_id:
            return {"type": "tool_call", "name": "lookup_order", "arguments": {"order_id": order_id}}
        return {"type": "final", "content": "Please provide an order ID such as A100."}
    if last["role"] == "tool":
        order_id = last["metadata"]["order_id"]
        return {"type": "final", "content": format_answer(order_id, last["content"])}
    return {"type": "final", "content": "I could not continue."}


def run_agent(user_goal: str, max_turns: int = 6) -> str:
    messages = [
        {"role": "system", "content": "You check order status. Use tools only when needed."},
        {"role": "user", "content": user_goal},
    ]

    for _ in range(max_turns):
        response = mock_model(messages, [
            {"name": t.name, "description": t.description} for t in TOOLS.values()
        ])

        if response["type"] == "final":
            return response["content"]

        if response["type"] != "tool_call":
            raise RuntimeError(f"Unknown model response: {response}")

        tool = TOOLS.get(response["name"])
        if tool is None:
            raise RuntimeError(f"Tool is not allowed: {response['name']}")

        args = response["arguments"]
        if set(args) != {"order_id"}:
            raise ValueError("Invalid tool arguments")

        result = tool.run(**args)
        messages.append({
            "role": "tool",
            "name": tool.name,
            "content": result,
            "metadata": args,
        })

    raise TimeoutError("Agent exceeded its maximum number of turns")


if __name__ == "__main__":
    print(run_agent("Where is order A100?"))

In production, replace mock_model with the model call provided by your selected runtime. Keep the surrounding checks: allowlisted tools, validated arguments, a turn limit, structured results, logging, and an explicit failure path.

Tool design: retrieval, actions, and orchestration

Data tools

Return the smallest useful result, include source timestamps where relevant, and make permission failures explicit. Search, CRM lookup, document retrieval, and database reads are common examples.

Action tools

Separate preview from commit when an operation has consequences. For example, let an agent draft an email and require approval before sending it. Use idempotency keys for operations that may be retried.

Orchestration tools

A manager agent can call specialist agents as tools. A decentralized design lets agents hand off to one another. Start with one agent and add specialists only when clearer tool boundaries or ownership justify the extra coordination.

Single-agent and multi-agent architectures

Pattern	How it works	Use it when	Main risk
Single agent	One model has tools and repeats the run loop	The workflow is understandable and tool count is manageable	Prompt and tool set become difficult to evaluate
Manager pattern	A central agent calls specialist agents as tools	One coordinator must combine domain results	Extra calls increase latency and cost
Handoffs	Peer agents transfer control to the best specialist	Ownership should move between distinct domains	State and permissions can be lost at boundaries

OpenAI’s developer guidance describes three implementation routes: a managed Agents API, an application-controlled Agents SDK, and the lower-level Responses API for direct calls or a custom agent loop. Compare them by who manages orchestration, where state lives, how tools execute, and how much control your application needs (Agents overview and Agents API overview).

State, memory, and context

• Run state: messages, tool calls, outputs, approvals, and errors for one workflow.
• Conversation state: information carried into later user turns.
• External memory: durable records retrieved when needed, such as account preferences or prior cases.
• Working context: a compact summary used to avoid resending large histories.

Store only what the workflow needs. Treat retrieved text as untrusted input, enforce tenant boundaries, and remove secrets before sending context to a model.

Guardrails and human control

• Validate every tool name and argument against a schema.
• Use least-privilege credentials and separate read from write access.
• Require confirmation for irreversible or externally visible actions.
• Set maximum turns, timeouts, budgets, and concurrency limits.
• Detect prompt injection in retrieved pages and documents; never let untrusted content redefine system policy.
• Log decisions, tool arguments, results, approvals, and failures without storing unnecessary sensitive data.
• Provide a clear stop, cancel, and transfer-to-human path.

Autonomy is a design choice with boundaries, not a guarantee of correctness or safety. OpenAI’s practical guide recommends guardrails and human-in-the-loop intervention as part of reliable deployment.

Reliability, performance, and cost

Reliability

Use deterministic checks around model output, retries only for transient failures, idempotent actions, bounded loops, and a fallback response when a tool is unavailable. Test incomplete data, contradictory tool results, malformed arguments, permission errors, and duplicate events.

Performance

Reduce unnecessary turns by giving tools precise descriptions and returning concise results. Parallelize independent reads where your runtime supports it. Cache stable reference data, stream progress for long runs, and move slow work to an asynchronous job with a status endpoint.

Cost

Each model call, tool call, retrieval operation, and downstream action may add cost. Track cost per run and per workflow outcome. Set a maximum budget and stop when additional calls are unlikely to improve the result. The supplied OpenAI sources do not establish a universal success rate or benchmark, so measure your own task-level quality.

Common errors and fixes

Symptom	Likely cause	Fix
The agent loops forever	No explicit exit condition	Add maximum turns, completion criteria, and a timeout.
Wrong tool is selected	Overlapping or vague descriptions	Rename tools, define required arguments, and remove unused tools.
Tool arguments fail validation	Model output does not match the schema	Validate before execution and return a structured error for correction.
Duplicate side effects	Retry repeated a non-idempotent action	Use idempotency keys and separate preview from commit.
Private data leaks across users	Shared state or unrestricted retrieval	Partition state by tenant and enforce authorization inside each tool.
Prompt injection changes behavior	Untrusted content is treated as instructions	Mark retrieved content as data and keep policy in higher-priority instructions.
Run times out	Slow tools, too many turns, or oversized context	Trim context, add per-tool timeouts, cache reads, and use asynchronous work.

Choosing an implementation path

1. Use a managed runtime when you want hosted state and orchestration with less infrastructure.
2. Use an SDK when the application should own the loop, tools, approvals, and persistence.
3. Use a lower-level responses interface when you need direct model control or are building the loop yourself.

Review the current official documentation before committing to an API because product availability and interfaces change. The Agent Builder guide currently says Agent Builder is being deprecated and scheduled to shut down on November 30, 2026, while ChatKit remains available; verify that timeline at publication (Agent Builder documentation).

Or skip the browser setup

If your GPT agent needs website screenshots as visual context, ScreenshotNeo provides a single HTTP request instead of maintaining browser automation. Cookie and consent banners, newsletter popups, and chat widgets are removed before the shot. Bot checks, blank pages, timeouts, failed loads, and cache hits are not billed, and each response reports its verdict and billing status. Its MCP server exposes take_screenshot, get_page_info, and capture_pdf for Claude, Cursor, and other MCP clients.

See the ScreenshotNeo API documentation for the full option set.

curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webp

import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
open("shot.webp", "wb").write(r.content)

const q = new URLSearchParams({ access_key: 'YOUR_API_KEY', url: 'https://stripe.com' });
const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);

One thousand screenshots per month are free with no card. Paid plans start at $5 for 3,000 shots. Create a free ScreenshotNeo account.

FAQ

Do agents always need tools?

No. A model can be wrapped in an agent loop with no external tools, but tools are what let an agent gather fresh context or affect external systems.

Can an agent guarantee a correct answer?

No. Use validation, constrained tools, monitoring, evaluations, and human review for consequential workflows.

Is multi-agent always better?

No. A single agent is easier to operate and evaluate. Add specialists when separation improves tool clarity or ownership.

What is an MCP server?

It is a remote tool server that an MCP-compatible client can call. The client still controls which servers and permissions are available.

When should a deterministic workflow be used instead?

Use ordinary code when the steps, inputs, and decisions are stable and predictable. Agents fit better when the workflow contains ambiguity, unstructured data, or exceptions that are expensive to encode as rules.