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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchNo. Python is useful, but it is not required to build an AI agent or test its security. OpenAI documents agent-building options in both TypeScript and Python, and offers a managed runtime as well as a code-first SDK. The more important decisions are where your application runs, what control you need over tools and data, and how you will test the full workflow’s permissions and failure modes.
What counts as an AI agent?
An agent can be understood as a model operating under instructions and using tools to complete a task. You can build that workflow with an agent library or assemble it from lower-level components; the language is an implementation choice, not a defining requirement. OpenAI’s practical guide to building agents recommends starting with a focused workflow and adding complexity only when it is needed.
Which implementation route should you choose?
Choose based on the system your team can safely operate and maintain, rather than assuming every agent needs Python.
| Route | What it means | Best fit |
|---|---|---|
| Code-first SDK | Your application owns deployment, tool implementations, storage, and approval decisions. OpenAI documents SDK options for TypeScript and Python. See the Agents SDK documentation. | Teams that need direct control over the application and its workflow. |
| Managed agent runtime | The provider runs the agent harness as a service, changing which infrastructure and execution responsibilities your application must operate. See the Agents SDK documentation. | Teams that prefer a managed execution path and have confirmed it meets their control and operational needs. |
OpenAI’s SDK documentation lists TypeScript and Python as supported SDK paths; its SDK and CLI documentation also lists JavaScript/TypeScript and Python options. Check the current SDKs and CLI documentation before choosing, since product capabilities can change.
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For either route, answer these questions before you build:
- Where does the application run? Keep the agent within the language and deployment environment your team can support.
- Who controls tools and state? Decide who executes tools, stores state, deploys changes, and enforces approval gates.
- How much orchestration is actually needed? Begin with a narrow workflow. Add handoffs, guardrails, or human review when the task requires them rather than designing a complex autonomous system up front.
How should you test an agent’s security?
Test the complete application configuration—not just the model’s text responses. Include the prompts, retrieved content, tools, permissions, network access, secrets, and approval logic the deployed workflow will actually use.
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Try prompt injection and manipulated input
Give the agent untrusted user or retrieved text that asks it to ignore its instructions, disclose data, or take a different action. Check both what it says and what it attempts through downstream tools. OpenAI’s safety guidance for building agents identifies prompt injection as a risk; a reassuring final answer does not establish that no unsafe tool call occurred.
Check for data disclosure through tools
Inspect what information the agent sends to each function, MCP server, or other connected service. Test whether it can transmit private data that is irrelevant to the task. OpenAI warns that private information can be leaked unintentionally and that developers do not have complete control over what a model shares with connected MCPs. Reduce the information available to a tool and validate outgoing data where your application can.
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Enforce authorization inside each tool
Do not treat the model’s instructions as an access-control boundary. Each tool should check the caller’s authorization on the server side, especially for privileged operations. Test whether a user can induce the agent to invoke an operation or access a record they are not allowed to use. Apply least privilege so the agent has only the capabilities the workflow needs.
Constrain data passed between workflow stages
Use schemas, enumerated values, and explicit allowed fields where one stage passes data to another. Test unexpected text in those fields to ensure it cannot become downstream instructions. Structured outputs can reduce ambiguity in data flow, but they do not guarantee that the model or application will behave safely in every situation.
Rank #4
Limit code, file, and network access
If the workflow generates or executes code, assess what files, packages, internal services, and network destinations it can reach. OWASP identifies unexpected code execution as an agentic-application risk in its Top 10 for Agentic Applications. Restrict outbound traffic to approved endpoints. OpenAI’s sandbox security guidance also advises careful network restrictions and credential handling.
Keep long-lived or third-party credentials outside the agent-accessible environment where feasible. If a sandbox needs authenticated access, consider routing requests through a broker or proxy that scopes what the agent can do, rather than exposing broad credentials directly.
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For consequential actions, require the application workflow to pause for human review and enforce that gate independently of the model’s willingness to ask. Test that the action cannot proceed without the required approval. The Agents SDK documentation describes guardrails and human review as ways to validate or pause workflows.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a security test can—and cannot—tell you
Guardrails and a successful test run are not proof that an agent is secure. OpenAI’s safety guidance says mitigations do not make agents perfect: they can still make mistakes or be tricked. Repeat relevant tests when prompts, tools, permissions, models, or deployment settings change.
Keep agent-specific checks alongside ordinary application security. OpenAI’s agent-building guide says guardrails should be coupled with robust authentication and authorization, strict access controls, and standard software security measures. Language choice does not replace any of those protections.
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