If software engineering is enough of a solved problem that you can delegate it entirely to LLM agents, what part of it remains context-specific enough that it can’t be better solved by a general-purpose software factory product? In other words, if you’re a company that is using LLMs to develop non-AI software, and you’ve built a sufficient factory to generate that software, why don’t you start selling the factory instead of whatever you were selling before? It has a much higher TAM (all of software)

When the author talks about "codifying" lessons, the instinct for most people is to update the rules file. That works fine for conventions - naming patterns, library preferences, relatively stable stuff. But there's a different category of knowledge that rules files handle poorly: the why behind decisions. Not what approach was chosen, but what was rejected and why the tradeoff landed where it did.

"Never use GraphQL for this service" is a useful rule to have in CLAUDE.md. What's not there: that GraphQL was actually evaluated, got pretty far into prototyping, and was abandoned because the caching layer had been specifically tuned for REST response shapes, and the cost of changing that was higher than the benefit for the team's current scale. The agent follows the rule. It can't tell when the rule is no longer load-bearing.

The place where this reasoning fits most naturally is git history - decisions and rejections captured in commit messages, versioned alongside the code they apply to. Good engineers have always done this informally. The discipline to do it consistently enough that agents can actually retrieve and use it is what's missing, and structuring it for that purpose is genuinely underexplored territory.

At level 7, this matters more than people expect. Background agents running across sessions with no human-in-the-loop have nothing to draw on except whatever was written down. A stale rules file in that context doesn't just cause mistakes - it produces confident mistakes.

It's very powerful and agents can create dynamic microbenchmarks and evaluate what data structure to use for optimal performance, among other things.

I also have validation layers that trim hallucinations with handwritten linters.

I'd love to find people to network with. Right now this is a side project at work on top of writing test coverage for a factory. I don't have anyone to talk about this stuff with so it's sad when I see blog posts talking about "hype".

Level 12: agent superintelligence - single entity doing everything

Level 13: agent superagent, agenting agency agentically, in a loop, recursively, mega agent, agentic agent agent agency super AGI agent

Level 14: A G E N T

> Look at your app, describe a sequence of changes out loud, and watch them happen in front of you.

The problem a lot of times is that either you don't know what you want, or you can't communicate it (and usually you can't communicate it properly because you don't know exactly what you want). I think this is going to be the bottleneck very soon (for some people, it is already the bottleneck). I am curious what are your thoughts about this? Where do you see that going, and how do you think we can prepare for that and address that. Or do you not see that to be an issue?

I am feeling like to go back to Level 5.

Level 6 helps with fixing bugs, but adding a new feature in a scalable way is not working out for me, I feed bunch of documents and ask it to analyze and come up with a solution.

1. It misses some details from docs when summarizing

2. It misses some details from code and its architecture, especially in multi-repo Java projects (annotations, 100 level inheritance is making it confuse a lot)

3. Then comes up with obvious (non) "solution" which is based on incorrect context summaries.

I don't think I can give full autonomy to these things yet.

But then, I wonder, people on Level 8, why don't they create bunch of clones of games, SaaS vendors and start making billions

Until you build an AI oncaller to handle customer issues in the middle of the night (and depending on your product an AI who can be fired if customer data is corrupted/lost), no team should be willing to remove the "human reviews code step.

For a real product with real users, stability is vastly more important than individual IC velocity. Stability is what enables TEAM velocity and user trust.

I think eventually 4-8 will be collapsed behind a more capable layer that can handle this stuff on its own, maybe I tinker with MCP settings and granular control to minmax the process, but for the most part I shouldn't have to worry about it any more than I worry about how many threads my compiler is using.

Like imagine if you could go back in time and servlets and applets are the big new thing. You wouldn’t like to spend your time learning about those technologies, but your boss would be constantly telling that it is the future. So boring

I've experimented with agent teams. However the current implementation (in Claude Code) burns tokens. I used 1 prompt to spin up a team of 9+ agents: Claude Code used up about 1M output tokens. Granted, it was a long; very long horizon task. (It kept itself busy for almost an hour uninterrupted). But 1M+ output tokens is excessive. What I also find is that for parallel agents, the UI is not good enough yet when you run it in the foreground. My permission management is done in such a way that I almost never get interrupted, but that took a lot of investment to make it that way. Most users will likely run agent teams in an unsafe fashion. From my point of view the devex for agent teams does not really exist yet.

Spec driven development can reduce the amount of re-implementation that is required due to requirements errors, but we need faster validation cycles. I wrote a rant about this topic: https://sibylline.dev/articles/2026-01-27-stop-orchestrating...

The idea that Claude/Cursor are the new high level programming language for us to work in introduces the problem that we're not actually committing code in this "natural language", we're committing the "compiled" output of our prompting. Which leaves us reviewing the "compiled code" without seeing the inputs (eg: the plan, prompt history, rules, etc.)

Moving past that, I'm not sure that I really trust it... I feel that manual review of product behavior and code matters a lot. AI agents often make similar mistakes to real people in leaking abstractions or subtle mistakes with security... So I do review almost everything, at least at the level where a feature PR makes sense. Though an AI pass at that can help too.

That's a smell for where the author and maybe even the industry is.

Agents don't have any purpose or drive like human do, they are probabilistic machines, so eventually they are limited by the amount of finite information they carry. Maybe that's what's blocking level 8, or blocking it from working like a large human organization.

https://factory.strongdm.ai/techniques

Techniques covered in-depth + Attractor open source implementations:

https://factory.strongdm.ai/products/attractor#community

https://github.com/search?q=strongdm+attractor&type=reposito...

https://github.com/strongdm/attractor/forks

I'm continuing to study and refine my approach to leverage all this.

Newer models are only marginally better at ignoring the distractors, very little has actually changed, and managing the context matters just as much as a year ago. People building agents just largely ignore that inefficiency and concentrate on higher abstraction levels, compensating it with token waste. (which the article is also discussing)

This is increasingly untrue with Opus 4.6. Claude Max gives you enough tokens to run ~5-10 agents continuously, and I'm doing all of my work with agent teams now. Token usage is up 10x or more, but the results are infinitely better and faster. Multi-agent team orchestration will be to 2026 what agents were to 2025. Much of the OP article feels 3-6 months behind the times.

Also, I’m struggling to take it to multiple agents level, mostly because things depend on each other in the project - most changes cut across UI, protocol and the server side, so not clear how agents would merge incompatible versions.

Verification is a tricky part as well, all tests could be passing, including end to end integration and visual tests, but my verification still catches things like data is not persisted or crypto signatures not verified.

Speak for yourself.

Also Level 7 is a misunderstanding of why plan mode is actually used even though one-shot works perfectly

I spend a great deal of my time planning and assessing/reviewing through various mechanisms. I think I do codify in ways when I create a skill for any repeated assessment or planning task.

> To be clear, planning as a general practice isn't going away. It's just changing shape. For newer practitioners, plan mode remains the right entry point (as described in Levels 1 and 2). But for complex features at Level 7, "planning" looks less like writing a step-by-step outline and more like exploration: probing the codebase, prototyping options in worktrees, mapping the solution space. And increasingly, background agents are doing that exploration for you.

I mean, it's worth noting that a lot of plan modes are shaped to do the Socratic discovery before creating plans. For any user level. Advanced users probably put a great deal of effort (or thought) into guiding that process themselves.

> ralph loops (later on)

Ralph loops have been nothing but a dramatic mess for me, honestly. They disrupt the assessment process where humans are needed. Otherwise, don't expect them to go craft out extensive PRD without massive issues that is hard to review.

  - It would seem that this is a Harness problem in terms of how they keep an agent working and focused on specific tasks (in relation to model capability), but not something maybe a user should initiate on their own.

Maybe it's just me, but I don't see the appeal in verbal dictation, especially where complexity is involved. I want to think through issues deliberately, carefully, and slowly to ensure I'm not glossing over subtle nuances. I don't find speaking to be conducive to that.

For me, the process of writing (and rewriting) gives me the time, space, and structure to more precisely articulate what I want with a more heightened degree of specificity. Being able to type at 80+ wpm probably helps as well.