ericdodds

when you're reading a blog post, are you able to tell when it's ai generated? do you care? i generally can tell within the first few paragraphs and it puts me off of reading it / overall what the content is about

Strong Opinions, Loosely Held on Agent + Harness Engineering: 1. You can outperform any default harness+model (including codex & claude code) on pretty much any Task by engineering the harness around it. Using the exact same model, curate prompts, tools, skills, hooks for that Task. This harness optimization process is becoming much more agent driven with humans reviewing and curating evals/rewards to hill climb on. “Just say what you want”. 2. A “general purpose” agent/harness doesn’t really exist, it’s a tradeoff between time spent on customizing the agent and performance (cost, latency, accuracy) on a Task. I don’t exactly follow what a general purpose means tbh. Who decides what’s general and what’s not? 3. But if the “general purpose” agent/harness existed, it would look like a good coding agent 4. Building a Task specific harness will most likely converge to good prompt & tool design (probably packaged up as a Skill) as models become smarter and better at in-context learning 5. Evals are a moat and thus data to produce evals is a moat. Especially true for vertical agent companies. This is because agents can fit to most Eval sets today. If Evals measurably encode all the good behavior your agent needs to do, then this signal can be hill climbed to improve your agent 6. Frontier closed models are far too expensive for the large majority of tasks the world needs to do. As teams start mapping costs to ROI, Open Model Harness Engineering will take off even more. It is almost always worth the investment to at least try to get a potential 20x+ cost reduction 7. A large chunk of design decisions around Task decomposition and context engineering exist solely because our usable context window is 50-100k. Agents that become excellent at breaking down tasks, applying compaction appropriately, and orchestrating subagents as sub-task workers will be the most delightful products to do real work. 8. We’re entering an Age of Unbundled (& Rebundled) Agents where Subagents exposed as Tools do a ton of domain specific work on behalf of an orchestrator agent. The Harness becomes a box that gets populated with the exact set of tools, skills, and subagents needed to solve that task or sub-task. Examples include WarpGrep (search), Chroma Context-1 (search), Nemotron 3 Omni (small multimodal), etc. Bespoke agents that rock at narrow tasks orchestrated as tools. This also applies to software as tools that are used by agents via Skills like Remotion or Blender. Different harnesses bundle together the tooling needed to complete that narrow task. End of opinions, these may change by the time this tweet goes out or may double down and expand on these in an article

hot take :) The biggest and most productive people in the AI era are the folks who are already good at their jobs. AI as a multiplier, not an equalizer/democratizer

3 months ago I started building a coding agent that runs in the cloud. It's since written every line of code I've shipped, including itself. Today, I'm open sourcing it. Introducing Open Agents.