3 ms·

Great take. As it happens, I'm working with a number of other engineers on a solution that solves for this. However to dispel any false hope, static analysis +

by mrxdev 7mo ago

Great take. As it happens, I'm working with a number of other engineers on a solution that solves for this. However to dispel any false hope, static analysis + agentic development can only go so far. It's the holistic combo of these tools plus Agentic AI as both an implementation AND a feedback mechanism that unlock higher quality. If you're looking to see what that feedback mechanism might look like in action, you might like checking out one of the other projects I've worked on which pre-dates Orc: https://github.com/spencermarx/open-code-review https://github.com/spencermarx/open-code-review Love where your head is at though! DEFINITELY an important problem we've got to solve here.

AgentNode 7mo ago
[flagged]
- mrxdev 7mo ago
  You hit the nail on the head. Completely agree. This framing... > Otherwise you can end up with persuasive agreement rather than real verification. ... is very familiar and one of the inspirational elements that led to https://github.com/spencermarx/open-code-review/tree/main https://github.com/spencermarx/open-code-review/tree/main (not trying to shamelessly plug, just your articulation was right on the money). It's definitely the holistic set of static analysis and a robust automated test pyramid, then agentic analysis (ideally differentiated, and redundant with discourse -- like Open Code Review), that is proving to be effective at ensuring quality code. That said, and a bit of a premature callout to the other project I mentioned, I think structured knowledge graphs to describe and commit to software intent (beyond what nested AGENT/CLAUDE/rule files and free form docs give you) are going to help us do a better job of engineering context and prompts for agentic development. This is the area that still feels like the wild west. Standardization is helping, and I think the toolset available now between RAG, lifecycle hooks, progressive disclosure in context and prompts, plus multi-agent implementation, review and discourse will help to close the loop and get us to the point of really confident and consistent pure AI-driven development. That's just my POV lol, I'll hop off my soap box now haha

by mrxdev 7mo ago

AgentNode 7mo ago

[flagged]

mrxdev 7mo ago
You hit the nail on the head. Completely agree. This framing... > Otherwise you can end up with persuasive agreement rather than real verification. ... is very familiar and one of the inspirational elements that led to https://github.com/spencermarx/open-code-review/tree/main https://github.com/spencermarx/open-code-review/tree/main (not trying to shamelessly plug, just your articulation was right on the money). It's definitely the holistic set of static analysis and a robust automated test pyramid, then agentic analysis (ideally differentiated, and redundant with discourse -- like Open Code Review), that is proving to be effective at ensuring quality code. That said, and a bit of a premature callout to the other project I mentioned, I think structured knowledge graphs to describe and commit to software intent (beyond what nested AGENT/CLAUDE/rule files and free form docs give you) are going to help us do a better job of engineering context and prompts for agentic development. This is the area that still feels like the wild west. Standardization is helping, and I think the toolset available now between RAG, lifecycle hooks, progressive disclosure in context and prompts, plus multi-agent implementation, review and discourse will help to close the loop and get us to the point of really confident and consistent pure AI-driven development. That's just my POV lol, I'll hop off my soap box now haha