I literally hold a "retro" with my agent after a session.

> Let's conclude with a retro. Did you run into any issues during this session that you think could be improved? Any failed tool calls, confusing docs/prompts, or tricky wording that took you effort to figure out, etc? Any final thoughts that you want to raise? Anything minor you didn't mention? Help make this codebase easier for the next agent to work in.

It has ideas for coding changes, spots unmentioned small bugs, suggests invariants (I have a high-level list of invariants to keep), principles to adopt, lint rules, tooling tweaks (I've built SO MUCH agent-tooling; so many useful bash scripts), skill-file updates, follow up work, all kinds of stuff.

Listen to your agent's whinging.

Trying something like this in a reusable way at https://github.com/mateffy/gesetz

It only tries to solve the „quality“ aspect of that equation tho, at least for the code output.

But I think for actual evals, some heuristics about a coding agent session are needed. But since the input tasks are always different, it’s hard to make any KPI actually comparable.

Did the agent needing 3 rounds of feedback on a big task perform worse than the one that needed none, but only worked on a small change?

Ive been building my own ai voice agent harness from scratch for close to a year now and following good software architecture practices is a good start. So those rules have to be coded in agents.md somewhere also really helps to have a "vision" section or "spirit of the project" section that describes what the end goal vaguely looks like and things I care about in achieving for the project. This prevents agent from being brittle and "single minded" about its work. But yeah vibes most of the time is how I've also been doing it, but I did find one very important thing that has really sped up my work. So I figure I'd share it here. And that advise is to ignore front end design at all costs until the very end and you are ready to launch. UI related woes kill any type of inertia and are responsible for most of the issues. So I decided abandon all UI stuff until the very end and just focus on iterative refinement, cutting, and other back-end related work and its been great ever since. Ideas can be had, tested, validated/invalidates and you ' get stuck on the optimization thought loop. Recently I have started to consider how i can fully automate the development process as the capabilities are there but designing the instructions on how to do this well and how to handle niche cases without getting me involved takes careful planning in writing out the guide so that will be interesting to see once i get there.

Just working on my own stuff so YMMV, but from a cost reduction standpoint, the two most outsized ROI wins came from making sure the agent runs all terminal commands in quiet and using a codebase index mcp to speed up understanding and reduce full file reads [0]. I've also found the latter also dramatically improved the quality of output.

[0] https://github.com/DeusData/codebase-memory-mcp

You've just described what the AI industry refers to as "evals", a collection of which forms a particular benchmark. I definitely encourage you to define your own evals, because the public benchmarks are often either saturated (largely solved and only going to see small improvements going forward) or seemingly not predictive of real world performance. I could also go further and speculate that they are in the training data, although credible benchmarks avoid this, I'm just not sure how successful they are.

> The problem is defining what quality means, and providing a way for agents to optimize the harness using that lever.

I've built and my team uses plannotator [1] for specs and code review. It stores all human annotated feedback, which we compile from time-to-time and iterate on coding standard skills for automated review (that seems to get better with the more feedback we provide).

You could write a script that does the same for your PR review comments, which it sounds like you maybe do with a bit more structure.

[1] https://github.com/backnotprop/plannotator

maybe a bit counter-intuitive but:

I found that removing

  - system prompt
  - skills
  - agents.md
  - mcps

+ reducing tools to just 1 (sh)

gives better results than having 'more' of them

(e.g. look at these traces to see more vs less in action:)

https://smolenv.com/t/nested-template-includes-60636/

not saying the right context does not help

(it definitely does!, but it's not trivial to provide the right context)

  - same task result (passed)
  - finished faster
  - fewer tokens, less cost
  - fewer requests for inference
  - fewer tool calls
  - less peak RAM

[flagged]