Before you wire DeepSeek Harness into a project, test three things: whether the agent can act only inside the environment you intend, where your data actually goes, and whether a real request to your exact provider and model ID succeeds. Record the Harness version or commit, the runtime profile, the provider, the endpoint, and the model ID with every result. Harness is developer-preview software, so a passing test describes one version and one configuration, not the product as a whole.
Record the configuration before you test
Results from an agent tool are only useful if you can reproduce them. Write down these values in your test log before running anything:
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- Harness version or Git commit
- Selected runtime profile (the official architecture reference describes web, headless, SDK, and ACP profiles)
- Model provider: official DeepSeek model service or a custom service you configure yourself
- Endpoint (base URL) and protocol
- Exact model ID
- Any plugins, MCP servers, or web tools enabled for the run
Developer-preview software changes. The official overview states that its core plugins and APIs may keep evolving, so a configuration that passes today may behave differently after an update. Re-run the three tests whenever any value in this log changes.
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Test 1: Can the agent act only inside the environment you intend?
DeepSeek’s project safety documentation (SAFETY.md) says Harness is experimental developer-preview software and has not undergone a security audit. It can run model-generated code and commands and reach the network, processes, credentials, and files made available to it. A defect, a misconfiguration, malicious input, or an untrusted plugin can damage the host, change or delete files, or expose data and credentials. The same document is blunt on scope: “Do not rely on DeepSeek Harness as the sole security control for untrusted workloads.”
Set up a contained test environment
- Run Harness in a disposable virtual machine, a container, or a dedicated environment that has no access to your primary workstation.
- Mount only the project files and tools the test needs. Do not share your home directory, SSH keys, or cloud credentials.
- Use low-value test data and throwaway credentials.
- Take a backup or snapshot of the environment so you can restore it after a destructive test.
- Read the plugin list, the configuration file, and every command the agent proposes before approving it.
Try to exceed the boundary on purpose
Testing only the expected path proves little. Run two checks:
- Expected behavior: ask the agent to perform a task inside the project and confirm it completes without touching anything outside the mounted paths.
- Deliberate overreach: ask it to read a file, environment variable, or network address that the test environment should not expose. The attempt should fail or require an approval you would refuse.
Sandboxing and approval prompts reduce risk, but the project does not claim they guarantee isolation. Treat a clean result as evidence for one configuration, not proof of containment.
Test 2: Where does the data go?
Separate what the Harness runtime does locally from what the services it calls do. The official data-processing statement says Harness stores session inputs and outputs, tool records, attachments, file paths, execution results, runtime logs, and configuration locally by default, and does not upload them to the server without consent. The same statement warns that when you invoke external models, web tools, MCP services, plugins, or other tools, those services may upload data and apply their own processing policies. “Local by default” therefore describes the runtime, not every request the runtime makes.
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Official versus custom model services
The privacy policy, last updated September 20, 2026, draws a line between two model-service paths. Who processes your input, and whose policy governs it, depends on which one you use:
| Question | Official DeepSeek model service | Custom model service |
|---|---|---|
| Who supplies the model API? | DeepSeek | You obtain and configure another provider’s API |
| Where do inputs go? | To DeepSeek’s service | Directly to the provider you configured |
| Whose policy governs processing? | DeepSeek’s privacy policy | That provider’s policy |
| Personal data listed in the policy | Session logs are listed among collected personal data, used for service operation, development, safety, and other stated purposes | Not stated by DeepSeek for the custom path; check the provider’s own policy |
| Controller and storage | Hangzhou DeepSeek Artificial Intelligence Co., Ltd. is named as controller; collected data may be stored in the People’s Republic of China | Not stated in the privacy policy; determined by the provider you chose |
Check the policy that applies on the day you test, and confirm the rules for your own geography before you send sensitive information through either path.
Trace what leaves the machine
- Use only synthetic or non-sensitive material in the routing test.
- Enable one external component at a time: a model provider, a web tool, an MCP server, or a plugin.
- Capture outbound traffic from the test environment, for example with a local proxy or firewall log, and note each destination host.
- Compare the destinations against the provider and tool policies for that component.
A component you did not expect to call the network is a finding in its own right. Document it before integration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test 3: Does the exact request work?
A saved API key or a visible model entry does not show that a real request will succeed. The provider guide documents the fields that define a model entry: API key, display name, base URL, API protocol, model ID, context window, output limit, and input types. Advanced options include reasoning effort, compatibility switches, headers, timeouts, and retry policy. Each of these can change what the endpoint accepts.
Send one representative request
- Configure the exact base URL, protocol, and model ID you plan to use in production. Do not substitute a similar model name.
- Send a short, representative prompt that matches the task you intend to automate.
- Confirm authentication succeeds and the response is parsed without errors.
- If the integration calls tools, send a request that requires one and confirm the tool call round-trips correctly.
- Repeat the request with any advanced option you plan to enable, such as a custom timeout or retry policy, and confirm it behaves as expected.
Check image input separately
If your integration sends images, confirm that the selected model and endpoint accept image input. The provider guide notes that a declared modality mismatch, where the configuration says a model accepts images but it does not, can make requests fail. Test images explicitly rather than inferring support from the model name.
Expect differences on custom gateways
The provider guide documents gateway differences involving developer-role support, token field names, and reasoning settings. The API wire-extension reference describes provider request headers and independently versioned body extensions. Do not assume every OpenAI-compatible endpoint implements the same behavior. Capture the real request body and headers your gateway receives, and confirm which extensions and headers it accepts before you build on it.
Choosing a runtime profile
The official sources establish a choice among profiles but do not publish comparative performance results. Choose the profile whose execution mode matches the test you are running, then verify its launch behavior in the architecture reference. Do not assume one profile is faster or safer than another without testing it in your own environment.
What a passing result does and does not show
Passing all three tests shows that, on the recorded version and configuration, the agent stayed inside the boundary you set, your traced traffic matched the policies you reviewed, and the exact request shape worked. It does not establish security against untrusted workloads, a guarantee about data handling by third-party tools, or performance. No named statistic on Harness integration readiness or safety was published in the official overview or the project safety documentation, so your own test log is the evidence that matters.
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