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I just dump all user messages from all sessions in a project into a flat .md file and have agents synthesize the user's intent. Then, using that extracted inten

by visarga 10d ago

I just dump all user messages from all sessions in a project into a flat .md file and have agents synthesize the user's intent. Then, using that extracted intent, the agents review code and tests. I call this a retro/reflection pass. It checks whether the code matches the intent and whether the tests match the code. Compactly formatted user messages are something an agent can ingest in a few minutes, even if they are thousands of lines long. And the quality of those messages is great: they don't track what the agent does well, only what changes and what breaks. Having this top-down view helps a lot. Usually, within a session and deep into a task, the agent loses the global perspective and optimizes for local success. I find it weird there is no harness that treats user messages as high value signal (except my own, of course, I have it, https://github.com/horiacristescu/playbook-harness https://github.com/horiacristescu/playbook-harness).

sagenschneider 10d ago
Keeping all the specifications and user discussion does create more context, which is useful for AI. However, I'd bring in Brooks discussion on essential and accidental complexity. In other words, there being No Silver Bullet https://www.cs.unc.edu/techreports/86-020.pdf https://www.cs.unc.edu/techreports/86-020.pdf The problem with specification and user discussion is they still have errors that code has. But unlike code, there are no tests to confirm correctness. So now we have a definition of the system in a non-exact language with no ability to test to confirm it's correctness. The code holds the essential complexity and now we are adding accidental complexity on top to manage. Again agree the specifications and user discussion provides context for the AI. However, a well written test suite provides similar context that can actually confirm correctness of the system. However, saying all the above. Focus of ImpactGate ( https://impactgate.officefloor.net https://impactgate.officefloor.net ) is about erosion of the code, not correctness.
- visarga 10d ago
  You usually don't know what you want upfront, in real life it is a stream of specification and steering.
  - sagenschneider 10d ago
    Yes, agree. It's a learning process. I tend to find when I build systems that at some point you need to stop analysis and just start building things to explore the problem. As you do, you prototype, refactor and possibly throw out ideas in favour of understanding the problem and discovery the real solution. The code becomes a reflection of that. I'm interested in your experiences of capturing specifications and user discussion on whether this captures the end intentions? Or whether it keeps you focused on earlier dead end directions?
    - visarga 10d ago
      I think agents are pretty capable of reading a log when given the explicit task of extracting the latest version of what the user wants. In general, they work well for direct tasks like this. They don't forget and do something else the way they do when they are deep into development work or debugging. Besides intent, I also mine signs of "user friction," which I use as input for the agent to come up with new tests. What I complain about is one of the signals driving testing.
      - sagenschneider 10d ago
        Yes, agree very capable of consuming large amounts of information I'd be interested to see what happens: - to token counts after a year of so of changes, as the specification list grows? - how it goes with concurrent changes in teams? Plus whether asking AI to add good commenting to the code could achieve the same thing?

by visarga 10d ago

sagenschneider 10d ago

Keeping all the specifications and user discussion does create more context, which is useful for AI. However, I'd bring in Brooks discussion on essential and accidental complexity. In other words, there being No Silver Bullet https://www.cs.unc.edu/techreports/86-020.pdf https://www.cs.unc.edu/techreports/86-020.pdf The problem with specification and user discussion is they still have errors that code has. But unlike code, there are no tests to confirm correctness. So now we have a definition of the system in a non-exact language with no ability to test to confirm it's correctness. The code holds the essential complexity and now we are adding accidental complexity on top to manage. Again agree the specifications and user discussion provides context for the AI. However, a well written test suite provides similar context that can actually confirm correctness of the system. However, saying all the above. Focus of ImpactGate ( https://impactgate.officefloor.net https://impactgate.officefloor.net ) is about erosion of the code, not correctness.

visarga 10d ago
You usually don't know what you want upfront, in real life it is a stream of specification and steering.
- sagenschneider 10d ago
  Yes, agree. It's a learning process. I tend to find when I build systems that at some point you need to stop analysis and just start building things to explore the problem. As you do, you prototype, refactor and possibly throw out ideas in favour of understanding the problem and discovery the real solution. The code becomes a reflection of that. I'm interested in your experiences of capturing specifications and user discussion on whether this captures the end intentions? Or whether it keeps you focused on earlier dead end directions?
  - visarga 10d ago
    I think agents are pretty capable of reading a log when given the explicit task of extracting the latest version of what the user wants. In general, they work well for direct tasks like this. They don't forget and do something else the way they do when they are deep into development work or debugging. Besides intent, I also mine signs of "user friction," which I use as input for the agent to come up with new tests. What I complain about is one of the signals driving testing.
    - sagenschneider 10d ago
      Yes, agree very capable of consuming large amounts of information I'd be interested to see what happens: - to token counts after a year of so of changes, as the specification list grows? - how it goes with concurrent changes in teams? Plus whether asking AI to add good commenting to the code could achieve the same thing?