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The most important thing is to have a strong plan cycle in front of you agent work, if you do that, agents are very reliable. You need to have a deep research cycle that basically collects a covering set of code that might need to be modified for a feature, feeds it into gemini/gpt5 to get a broad codebase level understanding, then has a debate cycle on how to address it, with the final artifact being a hyper detailed plan that goes file by file and provides an outline of changes required.

Beyond this, you need to maintain good test coverage, and you need to have agents red-team your tests aggressively to make sure they're robust.

If you implement these two steps your agent performance will skyrocket. The planning phase will produce plans that claude can iterate on for 3+ hours in some cases, if you tell it to complete the entire task in one shot, and the robust test validation / change set analysis will catch agents solving an easier problem because they got frustrated or not following directions.

This may produce some successes, but it's so much more work than just writing the code yourself that it's pointless. This structured way of working with generative AI is so strict that there is no scaling it up either. It feels like years since this was established to be a waste of time.

If the goal is to start writing code not knowing much, it may be a good way to learn how and establish a similar discipline within yourself to tackle projects? I think there's been research that training wheels don't work either though. Whatever works and gets people learning to write code for real can't be bad, right?

Back in the day, when business computing emerged (COBOL, Mainframes...), it appear the distinction between systems analysts and programmers. Analyst understand business needs, programmers implemented those specs in code.

Years later, the industry evolved to integrate both roles, and new methodologies and new roles appear.

Now humans write specs, and AI agents write code. Role separation is a principle of labor division since Plato.

Imagine using a probabilistic tools and expecting deterministic results. The math doesn’t math.

Lucky LLMs. All I get are forwarded meandering email chains and to attend almost entirely discursive meetings.

There's some irony; far from handling the details, LLMs are forcing programmers to adopt hyper-detailed, disciplined practices. They've finally cajoled software developers into writing documentation! Worth noting we've always had the capacity to implement these practices to improve HUMAN collaboration, but rarely bothered.

Can anyone help me on how to integrate this with Claude-Code? I went through it, I already follow few things manually but when I think of integrating most of the parts (not all), I don't know where should I put it for the Coding-LLM to understand. I fear if I put everything in Claude.md it will be just too much context for the CC.

Reminds me when I was demonstrating Claude code to a friend recently. My friend was a huge cursor user and was just curious about the cli tool and stuff.

In the end, regardless of framework or approach, I believe there is a way to go about using llms that will optimize work for developers. I worked with this tech lead who reviews all PRs and insists on imports arranged in a specific order. I found it insulting but did it anyway. Now I don’t - the not does.

The same way that llms can be really helpful in planning and building out specific things like rest endpoints, small web components, single functions or classes and so on.

Glad people are attempting to work on such potential solution for approaching work to take advantage of these new tools

The most important you need always to do:

1. Plan, review the plan.

2. Review the code during changes before even it finish and fix ASAP you see drift.

3. Then again review

4. Add tests & use all quality tools don't rely 100% on LLM.

5. Don't trust LLM reviews for own produced code as it's very biased.

This is basic steps that you do as you like.

Avoid FULL AUTOMATED AGENT pipeline where you review the code only at the end unless it's very small task.

Modern agentic tools already draw up plans before implementation. Some even define "plan" and "build" agents: https://opencode.ai/docs/agents/#built-in