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right, that part makes sense. The part i'm a little confused on is what the utility is for actually routing across harnesses. Is the point that most harnesses h

right, that part makes sense. The part i'm a little confused on is what the utility is for actually routing across harnesses. Is the point that most harnesses have been co-trained with models thus perform the best in the native harnesses?

songrenchu 1mo ago
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
- Bnjoroge 1mo ago
  I appreciate that, but unfortunately I'm still unclear why I need to route across multiple (coding) agent harnesses vs just sticking with one(omp).
  - songrenchu 1mo ago
    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
    - Bnjoroge 1mo ago
      ah gotcha! For non-coding use cases I see the value/utility. Thanks, good luck!
      - songrenchu 1mo ago
        Thank you for the discussion! This really nudges us to think how to make the broader white collar agent use case message more clear

songrenchu 1mo ago

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

Bnjoroge 1mo ago
I appreciate that, but unfortunately I'm still unclear why I need to route across multiple (coding) agent harnesses vs just sticking with one(omp).
- songrenchu 1mo ago
  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
  - Bnjoroge 1mo ago
    ah gotcha! For non-coding use cases I see the value/utility. Thanks, good luck!
    - songrenchu 1mo ago
      Thank you for the discussion! This really nudges us to think how to make the broader white collar agent use case message more clear