songrenchu

We added 4 things to remediate lowest common denominator. The first is task interface, it is portable cross harnesses. Second is capability discovery. Each implemenation needs to specify capabilities it doesn't support. Third is config that captures harness differences. Last is extensions like metadata and extra fields.

Yes, these two are good use cases. With no harness lock-in, a fallback and context handoff can solve LLM vendor service unreliable. Loop engineering can also be done for achieving long horizon goals a single harness loop struggles to close

Thank you for the discussion! This really nudges us to think how to make the broader white collar agent use case message more clear

You are right, for coding scenario, I also stick with one (CC in my case, really got disappointed at codex during gpt-5.4 time and never came back since then)

We need HarnessRouter when we need to package the harness agent as part of the product backend to serve the end users. In that scenario, the harness needs specific instructions, MCP tools, skills pre-configured, so it can reliably receive requests from upstream product components and deliver result to downstream product components.

We put 4 demo agent products for white collar working scenarios in our starter kit: PPT agent, Spreadsheet agent, Bi Dashboard agent, Video editing agent. Each of them is backed by a different harness setup. Video editing is most sophisticated so it's CC + Opus 5. The other 3 are more simpler use cases so default setup in the kit is set to Hermes + DeepSeek V4 Pro.

Take the PPT agent use case, for sure you can hook the same tools and skills to local Claude Code or Codex, but it only works for yourself using it locally. If you are building a AI PPT product (like Gamma), you need to host the harness setup somewhere in the cloud together with other product code. That's when you can use HarnessRouter as the PPT generation/manipulation component of the product, with the chosen harness baked in. For sure you can build the same harness wrapper plumbing as we did in HarnessRouter to make the same stack work, but using HarnessRouter the development time is shorten as we have already get the nitty gritty engineering details covered

The router sits between application layer and the harness layer. HarnessRouter is the spec translation layer that translates the unified interface into each harness's own api format. Each harness is treating somehow like a blackbox, and they talk to the models as is. For routing across harnesses, think about it as an aggregator, like OpenRouter. The application layer have multiple use cases and each function could backed by a different harness. We do have smart routing feature on our roadmap to support use cases of harness fallback, cost optimization, etc

Think of it as OpenRouter, but for different agent harnesses, not models. Instead of sticking to any one harness, you can route to and use any of them through a unified API