No. Python is useful, but it is not a prerequisite for building or security-testing AI agents. OpenAI documents agent-development paths in both TypeScript and Python, and its managed Agents API offers a different way to run an agent workflow. The more important decisions are where the application runs, who controls its tools and data, and how the complete workflow is secured.
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 one with a library or assemble the workflow from lower-level components; Python is one possible implementation language, not the definition of an agent. OpenAI’s practical guide to building agents recommends starting with a focused workflow and adding complexity only when the task calls for it.
How to choose an implementation route
Choose based on the product and the responsibilities your team wants to own—not on an assumption that agents require Python. OpenAI documents SDK options in TypeScript and Python, as well as a managed route. Those are examples of available paths, not a comparison of every agent framework or vendor.
| Route | Who controls the application workflow? | When it may fit |
|---|---|---|
| Code-first SDK | Your application owns deployment, tool implementations, storage, and approval decisions. | When the team wants direct control of the server-side workflow and can maintain it in a supported language such as TypeScript or Python. |
| Managed Agents API | The provider runs the agent harness; your application still needs to define the surrounding product behavior and safeguards. | When a managed runtime is preferable to operating the harness yourself. |
The distinctions between the SDK and managed runtime, and the documented language paths, are described in the Agents SDK documentation. OpenAI’s SDKs and CLI documentation also lists official SDK language choices, including TypeScript/JavaScript and Python.
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Match the language to the system you can maintain
If your application and team already use TypeScript, an agent workflow in that environment can avoid introducing a second language solely for agent code. Python is a sensible choice when it fits your existing stack or the libraries and skills your project needs. Either way, the team must be able to maintain the tool integrations, deployment, state handling, and security controls.
Keep the first workflow narrow
Start with a defined task, a small set of tools, and explicit boundaries. Add orchestration, handoffs, guardrails, or human review when the workflow actually requires them. A more autonomous design is not automatically a better design, and added moving parts mean more behavior and permissions to test.
How to test an agent for security risks
Test the whole application configuration—not just the model’s text response. Include the instructions, connected tools, permissions, data sources, deployment environment, and any approval steps. A convincing answer does not establish that the workflow behaved safely; inspect tool calls and downstream effects too.
Test prompt injection and manipulated input
Use untrusted user text, documents, or retrieved content that asks the agent to ignore its instructions, disclose information, or take an unauthorized action. Check whether the agent resists the request and whether it still attempts a risky tool call. OpenAI’s safety guidance for building agents identifies prompt injection and unintended behavior as risks to address.
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Check what data can leave through tools
Inspect what the agent sends to each function, MCP server, or other connected service. Test whether it can disclose private data unnecessarily, including data included in tool arguments or passed between workflow stages. OpenAI cautions that private information can be leaked unintentionally and that developers do not have complete control over what a model shares with connected MCPs. Send only the information a tool needs, and do not treat a tool connection as a safe boundary by itself.
Enforce authorization inside each tool
Give tools only the permissions required for their task, and have the application enforce access checks on the server side. Test whether a user or agent can invoke a more privileged operation by phrasing a plausible request, changing an identifier, or exploiting a confused workflow. Guardrails can help validate behavior, but they do not replace authentication, authorization, strict access controls, or standard application security.
Constrain information passed between workflow stages
Use schemas, enumerated values, and explicitly allowed fields where appropriate. Then test malformed, unexpected, or instruction-like content in each field to see whether it can cross into a later stage as an instruction. Structured outputs can reduce ambiguity in data flow; they do not guarantee that the content is trustworthy or that every stage will behave correctly.
Limit code, files, network access, and credentials
If an agent can generate or execute code, assess which files, packages, internal services, and network destinations it can reach. Unexpected code execution is included among the risks in the OWASP Top 10 for Agentic Applications. Apply least privilege and isolate execution environments where relevant.
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Make human review an application-enforced gate
For consequential actions, require approval in the application workflow before the tool performs the action. Test that the gate cannot be skipped by a prompt, a tool sequence, or a failure path. A model’s willingness to ask for review is not an enforcement mechanism. 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—prove
A guardrail or a passing test run is not proof that an agent is secure. OpenAI’s safety guidance warns that mitigations do not make agents perfect: they can still make mistakes or be tricked. Keep tests aligned with realistic attack paths, and repeat them when instructions, tools, permissions, models, or deployment settings change.
The principle is broader than a particular language or framework. OpenAI’s practical guide to building agents says: “Guardrails are a critical component of any LLM-based deployment, but should be coupled with robust authentication and authorization protocols, strict access controls, and standard software security measures.”
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