A hands-on entry point after the preview release

DeepSeek released the developer preview of DeepSeek Harness last Thursday and opened its source code. The immediate question for many developers is not only what the framework is, but how to start using it in a real workflow.

Unlike AI coding tools that mainly expose a chat interface, DeepSeek Harness focuses on the full runtime environment for agents: where the model comes from, how the agent operates, and how capabilities such as the terminal, file system, web search, planning and subagents are combined. In this context, an agent is an AI-driven program that can use tools, inspect context and continue working toward a task goal.

The framework’s central design idea is “everything is a plugin.” Models, tools, sessions, permissions and even the agent loop can be assembled and replaced through plugins. This gives developers flexibility, but it also creates a learning curve: which preset should be used, how standard mode differs from PTC mode, which plugins matter first, and where a beginner should begin.

Task Lens as the tutorial spine

To address that gap, the Geek Time teaching and research team has released an open introductory tutorial titled “DeepSeek Harness Minimal Getting Started Tutorial.” Rather than explaining configuration fields one by one, the tutorial uses a small project named Task Lens as a continuous thread.

The tutorial starts from an empty directory. Readers launch DeepSeek Harness, ask the agent to create a project, read files and run tests. The same project is then used to switch model providers, compare agent presets, configure common plugins and eventually complete a feature upgrade that includes code, tests, sample data and documentation.

A provider is the model access layer: it determines which model or service acts as the agent’s “brain.” By keeping the project constant while changing providers, presets and plugins, the tutorial tries to help readers understand what each layer contributes.

The full course contains seven short articles and can be completed in about one to two hours if followed along the main path. It does not require readers to study the source code or architecture documents in advance, and it avoids overwhelming newcomers with a long list of configuration options at the beginning.

What the tutorial covers

The tutorial organizes DeepSeek Harness around three core capability layers, plus a final integrated exercise:

• Model configuration: readers try the default DeepSeek setup, built-in providers and custom providers, learning how to choose which model powers the agent.
• Agent presets: the course compares four built-in modes — standard, PTC, minimal and creative — and shows how they differ in tool presentation, execution style and task fit. The source material lists PTC as a preset but does not expand the acronym.
• Plugin system: it introduces core plugins such as the terminal, agent loop and web search, along with representative plugins including skill, plan, subagent, sandbox and session. An agent loop is the repeated process through which an agent observes, plans, acts and incorporates feedback.
• End-to-end practice: readers recombine models, presets, plugins and permissions so a customized agent can complete a project upgrade, with tests and execution traces used to verify the result.

The tutorial is positioned as a shorter practical doorway, not a replacement for official documentation. Its goal is to help users first build a working mental model and then decide how deeply they want to explore the framework.

Why it matters for agent development

DeepSeek Harness points to a broader shift in AI coding tools: from single-purpose chat assistants toward composable agent runtimes. When models, tools, sessions, permissions and execution loops can be swapped as plugins, developers gain more control over how an agent behaves for different tasks.

That flexibility is valuable, but it also moves responsibility to the user. Developers need to decide which preset fits a task, what permissions are appropriate, which model provider should be used and how to verify an agent’s output. A project-based tutorial such as Task Lens is useful because it turns abstract framework concepts into an observable development process.

The next phase for DeepSeek Harness will depend not only on the framework’s openness, but also on the clarity of its examples, tutorials and plugin ecosystem. For developers curious about agent engineering, this minimal tutorial offers a low-friction first step: finish a small project, understand the moving parts, and then decide whether to build a more customized agent of their own.