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To build a useful AI agent, give a model one clearly bounded job, instructions for doing it, and a narrowly scoped tool it can call. Then run a controlled cycle: send the task and available tools to the model, validate and execute any requested tool call, return the result, and stop when the model finishes or a limit is reached. Start with one agent and test it before expanding its permissions or adding more agents.
What makes an AI agent different from a model call?
A plain model call takes input and generates a response. An agent adds a controlled way to act: the application exposes tools, executes allowed requests, supplies observations back to the model, and decides whether the run should continue. The core pieces are a model, instructions, and tools; retrieval or memory can be added when the task actually needs them. OpenAI’s practical guide to building agents describes these components and the run-loop pattern. Anthropic likewise describes an LLM augmented with retrieval, tools, and memory in its article on building effective agents.
For a first project, make the job narrow enough that you can tell whether the agent did it correctly. This guide uses a read-only order-status lookup: the model can request a lookup, but the Python function—not the model—decides what data is returned. The sample records are local demonstration data, not a connection to a real store.
Choose how much of the run loop you want to own
There are three common implementation choices. They trade direct control against the amount of orchestration code and infrastructure you maintain; none is best for every workflow. OpenAI’s Agents documentation discusses its API and SDK approaches, while the Agents SDK documentation describes the SDK’s orchestration capabilities.
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| Approach | Run-loop control | Implementation and state | Fits best when |
|---|---|---|---|
| Direct API calls | Your application decides when to call the model, execute tools, return results, and stop. | You write and maintain the loop and decide what state to retain. This gives explicit control, but puts validation, error handling, and orchestration work in your application. | The workflow is short or fixed, and you need to inspect or customize each step. |
| SDK | The SDK can manage repeated orchestration tasks such as turns, tool execution, guardrails, handoffs, or sessions, depending on the SDK. | Less orchestration code to write; you still define the tools, their permissions, and the application boundaries. Check which state and runtime responsibilities the particular SDK handles. | You want a supported framework to handle recurring agent mechanics without building every part of the loop yourself. |
| Managed runtime | The service takes on more of the orchestration and session infrastructure. | Less runtime infrastructure to operate directly, with integration and deployment choices shaped by that service. Establish which responsibilities remain yours, including approvals and authorization. | The workflow is open-ended or multi-step and the managed capabilities match your deployment requirements. |
If a task has a known sequence, ordinary prompt chaining with programmatic checks may be simpler than an agent loop. A loop is useful when the number of steps depends on what the model learns from tool results; each extra step also adds opportunities for errors and additional model calls.
Define the task and its boundaries
Before writing code, specify the agent’s input, acceptable result, permitted actions, and prohibited actions. For the example, the task is: given an order ID, report its status if it exists. The tool is read-only and accepts one string. The agent must not claim that it changed an order, invent a status, or look up unrelated information.
- Input: a user-supplied order ID.
- Expected result: a concise status from the lookup tool, or a clear statement that the ID was not found.
- Permitted action: call the order-status lookup tool.
- Not permitted: modifying orders, accessing arbitrary systems, or making up a result when the tool has no record.
Keep the instructions aligned with the tool’s actual capabilities. A prompt can guide the model, but it is not an authorization mechanism; enforce permissions in application code.
Set up a small Python project
The following uses OpenAI’s Python Agents SDK as a vendor-specific example. The same high-level design can be implemented with other providers or libraries, but their setup and interfaces differ. The Agents SDK Python quickstart documents this project setup and first-run style.
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- Create and activate a virtual environment: run
python -m venv .venv. Activate it withsource .venv/bin/activateon macOS or Linux, or.venvScriptsactivatein Windows PowerShell. - Install the SDK: run
pip install openai-agents. - Set your API key: configure
OPENAI_API_KEYin your shell or secret manager. Do not put a real key in source code, commit it, or print it in logs. - Save the program below: use a file named
agent.py, then runpython agent.py.
Runnable single-agent example
from agents import Agent, Runner, function_tool
ORDERS = {
"A100": "shipped",
"A101": "processing",
}
@function_tool
def get_order_status(order_id: str) -> str:
"""Return the status for an order ID in the sample records."""
order_id = order_id.strip().upper()
status = ORDERS.get(order_id)
if status is None:
return f"No order found for ID {order_id}."
return f"Order {order_id} is {status}."
agent = Agent(
name="Order status assistant",
instructions=(
"Help the user check an order's status. Use get_order_status for an order ID. "
"Report only information returned by the tool. If no order is found, say so. "
"Do not change orders or claim to have taken other actions."
),
tools=[get_order_status],
)
if __name__ == "__main__":
result = Runner.run_sync(agent, "What is the status of order A100?")
print(result.final_output)The SDK exposes the decorated Python function as a tool and runs the interaction. With the sample input, the useful result is that order A100 is shipped. The lookup function is ordinary application code: it normalizes the ID, checks the local mapping, and returns a defined response when the record is absent. Replace that mapping with an authorized data source only after adding appropriate authentication, validation, and access checks.
Understand the agent loop behind the example
An SDK hides repeated orchestration, but the application still needs an exit condition and safe tool behavior. In a direct API implementation, the loop follows this sequence:
- Send the conversation, instructions, and tool definitions to the model.
- If the model returns a final answer, stop and return it.
- If it requests a tool, check that the tool is allowed and validate every argument in ordinary code.
- Execute the tool under the application’s permissions and capture either its result or a controlled error.
- Add the tool result to the conversation and ask the model to continue.
- Stop on an error or at a configured maximum number of turns. Do not silently retry forever.
OpenAI’s guide calls this repeated execution a “run” that continues until an exit condition is reached. Whether you write the loop or delegate it to an SDK, retain an application-level bound. Also distinguish a tool request from a successful action: only the tool’s result establishes what happened.
Make tools narrow and safe
A tool is code with real permissions, not merely a prompt feature. Give it a clear name, a small input schema, and an output the model can interpret. Validate arguments before use, authenticate requests as the application, and authorize each operation for the user and resource involved. Prefer read-only tools at first. If an action can send a message, spend money, change data, or affect a person, add a human approval step before execution.
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- Use least privilege: a status lookup should not have permission to edit orders.
- Reject malformed, oversized, or out-of-scope input in application code.
- Check tool output before returning it to the model; treat external content as untrusted input.
- Use a sandbox for tools that execute code or access files, where appropriate.
- Keep secrets out of prompts and tool results unless the task specifically requires them.
Prompt instructions help define behavior, but they do not replace authentication, authorization, validation, or isolation. A model may misunderstand a request or produce an invalid tool call; the execution layer must be able to refuse it.
Test behavior before expanding the agent
Build a small set of representative cases before adding more tools or autonomy. Check both the final answer and the trace of decisions and tool calls. Useful cases include a known order, an unknown ID, a blank ID, a misspelled request, a request to change an order, and an unrelated question. For each case, define in advance what counts as acceptable behavior.
- Did the agent choose the tool only when it was relevant?
- Were tool arguments valid and within the permitted boundary?
- Did it use the returned observation rather than inventing a result?
- Did it stop cleanly on a final response, an error, or the run limit?
- Did the final answer follow the task’s requirements?
When a test fails, first inspect whether the tool name and description, instructions, validation, or stop rule caused the problem. Improve the smallest failing part and rerun the cases. OpenAI’s Agents SDK quickstart also introduces tracing, which can help inspect SDK runs. Anthropic recommends extensive testing in sandboxed environments and warns that more agent autonomy brings higher costs and the potential for compounding errors; those are reasons to expand access only after evaluation, not quantified guarantees about any particular agent.
When to add memory, handoffs, or multiple agents
Persist state only when a requirement spans more than one run or interaction. Decide what must be remembered, for how long, and how it is tied to a user or session; do not retain conversation or personal data by default just because a framework offers state. For a single short lookup, an in-memory tool result is enough.
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Start with one agent and make its instructions and tools clearer before splitting responsibilities. Multiple agents can help when responsibilities genuinely differ or a single agent continues to choose tools unreliably, but they add handoffs, coordination overhead, and questions about which agent owns the final answer. Define those behaviors explicitly and measure whether the multi-agent version improves the representative tasks. OpenAI’s practical guide recommends maximizing a single agent’s capabilities first; Anthropic also advises adding complexity only when it demonstrably improves outcomes.
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curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpFor an agent integration, keep the API key on your server and expose only the capture operation and URL scope the agent needs. ScreenshotNeo removes cookie/consent banners, newsletter popups, and chat widgets before capture; each step can be turned off. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers report the page verdict and billing status. Its MCP server provides take_screenshot, get_page_info, and capture_pdf tools for Claude, Cursor, and other MCP clients.
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The program cannot find the SDK
Confirm that the virtual environment is active in the shell where you run the script, then install openai-agents there. If several Python installations are present, use that environment’s Python executable to run both the install command and python agent.py.
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The API key is missing or rejected
Check that OPENAI_API_KEY is set in the process environment, is available to the active shell, and is valid for the account and API being used. Do not solve this by hard-coding the key in the file. Keep any authentication error out of user-facing output unless it is safe and useful to disclose.
The agent answers without calling the tool
Make the instruction say when the lookup is required, and ensure the tool is included in the agent configuration with a specific description. Test with a known ID and inspect the run trace. Do not treat a plausible answer as proof that a lookup occurred.
The tool returns the wrong or no record
Check normalization, the data source, and the exact tool argument. The sample mapping recognizes only A100 and A101. For a production lookup, enforce the caller’s authorization at the data-access layer; knowing an order ID must not itself grant access.
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A run keeps taking steps or fails mid-run
Set a maximum run length, handle tool errors as explicit results or stop conditions, and avoid unbounded retries. Inspect the trace to find whether the repeated behavior comes from ambiguous instructions, a malformed tool result, or a genuine need for another step.
Plan for latency, reliability, and cost
Each model turn and external tool call can add waiting time, and an open-ended run can make an unpredictable number of calls. Bound turns, set sensible request timeouts, and return a useful error when a dependency fails. For predictable fixed workflows, compare an agent against a simpler sequence of model calls and code checks. Measure representative tasks—including failures—rather than assuming extra autonomy improves quality.
Log enough to diagnose behavior: the task identifier, selected tool, validated arguments, result status, elapsed time, and stop reason. Redact secrets and sensitive values, and set retention and access controls for traces. Estimate costs from the actual model and tool usage of your tested workflow; no universal success rate, performance figure, or cost per agent applies to every configuration.
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