how to build ai agent harness: 3 repositories with different roles

If your research starts with “how to build ai agent harness”, these three repositories deserve separate attention, whether you are evaluating tools for your business or your MSP's clients. DeepSeek Harness describes a plugin-based architecture. Anatomy offers a skill for creating interactive technical figures. OpenClaw documents an assistant that runs on your devices and connects to messaging channels. Each is a starting point for different questions, not a list of features shared by all three projects.

AI agent harnesses: where the three repositories differ

deepseek-ai/deepseek-harness. The README presents DeepSeek Harness as an open source agent harness. It describes an everything-is-a-plugin architecture built on Cordis. That is the repository's architectural description; on its own, it does not specify which components you can replace or how every integration works.

The README also says DeepSeek Harness is in developer preview and warns that breaking changes are possible. For a local launch, it documents the command npx @deepseek-ai/dsh web, which starts a Web UI. It asks users to read a security notice before running the command; the contents of that notice are not among the sources reviewed here.

Recommendation: if you want to evaluate DeepSeek Harness for a project, record the version you test and check how updates affect your prototype. Question to investigate: which extension points does your use case actually need, and where are they documented? The README's architectural description helps guide your reading, but it does not replace that check.

Anatomy: a skill for a specific task

wheresryan22/anatomy. The README presents Anatomy as a skill for Claude that explains an idea by building a working object. It says the skill can also be used in OpenCode and Codex. The repository describes interactive isometric figures and distinguishes between SVG output, SVG figures with WebGL, and a 3D mode marked as beta. For SVG mode, it lists a standalone HTML page, an SVG file, and React or Next.js components as delivery options.

The focus here is the documented task: producing an interactive representation of a concept. That description should not be turned into a promise about an agent's entire workflow. The README provides examples of figures and instructions for getting started with the skill. Both are worth examining if you are considering explanatory material for a technical team.

Recommendation: choose a concept your colleagues struggle to explain and assess the result as a teaching aid. Questions to investigate: does the figure make the intended behavior clear? Are the details it depicts accurate? If you plan to publish it, who will review its text, interactions and accessibility?

OpenClaw: start with the Gateway and security

openclaw/openclaw. The README presents OpenClaw as an open source assistant that runs on the user's computer and connects to messaging channels. It describes the Gateway as a local control plane for sessions, tools, events and channel connections. It also says that models and agent harnesses are replaceable plugins without changes to the rest of the system. That claim concerns those components; the README's lists of tools, skills and plugins do not establish the same property for every item listed.

On security, the README says to treat incoming messages as untrusted input. It specifies that tools run on the host for the main session unless you configure sandboxing. It recommends reading the security guides before connecting other users or exposing the Gateway remotely. Those guides are mentioned in the README but are not among the pages reviewed for this article.

Recommendation: before a shared test, define who can send messages and which actions you want to allow. Questions to investigate: where do tools run in your chosen configuration? Which credentials can they access? What controls are needed before connecting a channel used by the team? These checks must be made against the actual installation; they are not guarantees that can be drawn from a general description.

Putting this comparison to work

For an SME or MSP, the recommended next step is to give each test a narrow question: does DeepSeek Harness's stated architecture meet your development needs? Does the Anatomy skill produce an explanation that is useful and can be checked for accuracy? Does your OpenClaw configuration respect the operational boundaries you have set? Keep the answers separate: a capability described by one repository does not carry over to the others.

If you are planning a broader evaluation, you can visit Altovar's page on artificial intelligence for businesses. Use it as a starting point for exploring the topic, and consult the respective repositories for details of each project.

Sources: deepseek-ai/deepseek-harness README; wheresryan22/anatomy README; openclaw/openclaw README.