10 ms·

Levels of Agentic Engineering

• claud_ia 7mo ago
  [dead]
• sjkoelle 7mo ago
  Oceania has always been context engineering. Its been interesting to see this prioritized in the zeitgeist over the last 6 months from the "long context" zeitgeist.
• smy20011 7mo ago
  I will not put it into a ladder. It implies that the higher the rank, the better. However, you want to choose the best solution for your needs.
• efsavage 7mo ago
  Yegge's list resonated a little more closely with my progression to a clumsy L8. 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.
  • lherron 7mo ago
    I was surprised the author didn’t mention Yegge’s list (or maybe I missed it in my skim).
  • ramesh31 7mo ago
    >"Yegge's list resonated a little more closely with my progression to a clumsy L8." I thought level 8 was a joke until Claude Code agent teams. Now I can't even imagine being limited to working with a single agent. We will be coordinating teams of hundreds by years end.
  • taude 7mo ago
    Agreed a bit. I'm probably too paranoid for MCP, but also don't mind rolling my own CLI tools that do the exact minimum I need them to do. Will see where we're at in a year or so....
  • mattlondon 7mo ago
    Yep I was also surprised to see MCP & Skills as not only a distinct "level", but so high up. In my mind, MCP & Skills is inseparable part of chat interfaces for LLMs, not a distinct level.
• politelemon 7mo ago
  These are levels of gatekeeping. The items are barely related to each other. Lists like these will only promote toxicity, you should be using the tools and techniques that solve your problems and fit your comfort levels.
• eikenberry 7mo ago
  In my opinion there are 2 levels, human writes the code with AI assist or AI writes the code with human assist; centuar or reverse-centuar. But this article tries to focus on the evolution of the ideas and mistakenly terms them as levels (indicating a skill ladder as other commenters have noted) when they are more like stages that the AI ecosystem has evolved through. The article reads better if you think of it that way.
  • dist-epoch 7mo ago
    There is another level - AI writes the code with AI assist.
    • eikenberry 7mo ago
      That is just another level of reverse centaur and will eventually have a human ass attached to it.
• mzg 7mo ago
  As a lowly level 2 who remains skeptical of these software “dark factories” described at the top of this ladder, what I don’t understand is this: 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)
  • pydry 7mo ago
    I have the same question about people who sell "get rich with real estate" seminars.
  • hakanderyal 7mo ago
    We are not there yet. While there are teams applying dark factory models to specific domains with self-reported success, it's yet to be proven, or generalizable enough to apply everywhere.
  • dist-epoch 7mo ago
    Codex and Claude Code are these (proto)factories you talk about - almost every programmer uses them now. And when they will be fully dark factories, yes, what will happen is that a LOT of software companies will just disappear, they will be dis-intermediated by Codex/Claude Code.
  • glhast 7mo ago
    Also a measly level 2er. I'm curious what kind of project truly needs an autonomous agent team Ralph looping out 10,000 LOCs per hour? Seems like harness-maxxing is a competitive pursuit in its own right existing outside the task of delivering software to customers. Feels like K8s cult, overly focused on the cleverness of _how_ something is built versus _what_ is being built.
    • cheevly 7mo ago
• measurablefunc 7mo ago
  What level is numeric patterns that evolve according to a sequence of arithmetic operations?
• jjmarr 7mo ago
  I coded a level 8 orchestration layer in CI for code review, two months before Claude launched theirs. 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".
  • deleted 7mo ago
    [deleted]
  • jessmartin 7mo ago
    I got my own level 8 factory working in the last few days and it’s been exhilarating. Mine is based on OpenAI’s Symphony[1], ported to TypeScript. Would be happy to swap war stories. <myhnusername>@gmail.com
    • whattheheckheck 7mo ago
      How much money have you made with this approach
      • quotemstr 7mo ago
        ... is that the purpose of life? The sole reason for doing anything?
        • twelve40 7mo ago
          With so much hype it's a valid question: "is this useful/practical, or just a fun rabbit hole/productivity porn". Money is the most obvious metric, feel free to inquire the parent about other possible metrics that might be useful to others instead of asking rhetorical questions.
          • 7mo ago
• ftkftk 7mo ago
  I prefer Dan Shapiro's 5 level analogy (based on car autonomy levels) because it makes for a cleaner maturity model when discussing with people who are not as deeply immersed in the current state of the art. But there are some good overall insights in this piece, and there are enough breadcrumbs to lead to further exploration, which I appreciate. I think levels 3 and 4 should be collapsed, and the real magic starts to happen after combining 5 and 6; maybe they should be merged as well.
  • maxdo 7mo ago
    Car levels autonomy is fake. Everything including Level 3 is not a real autonomy it is hard rules + some reaction to the world, and everything above 3 is autonomy with just s slightly human security guardrails to attempt the real autonomy. At this moment where we have human who just sit there before verify enough 9 after comas of error rates, the entire level conversation is dead. It's almost a binary state. Autonomous or not. Similar happened with software levels. Even Level 2 was sci-fi 2 years ago, 1 year away from now anything bellow level 5 will be a joke except very regulated or billion users systems scale software.
  • bensyverson 7mo ago
    Agreed; here's the link for anyone looking for it: https://www.danshapiro.com/blog/2026/01/the-five-levels-from-spicy-autocomplete-to-the-software-factory/ https://www.danshapiro.com/blog/2026/01/the-five-levels-from...
• jackby03 7mo ago
  Good taxonomy. One thing missing from most discussions at these levels is how agents discover project context — most tools still rely on vendor-specific files (CLAUDE.md, .cursorrules). Would love to see standardization at that layer too.
• nimasadri11 7mo ago
  I really like your post and agree with most things. The one thing I am not fully sure about: > 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?
  • smallnix 7mo ago
    Reminds me of a colleague who said they don't need to learn to type faster, since they use the time to think what they want to write.
• ramesh31 7mo ago
  >(Re: level 8) "...I honestly don't think the models are ready for this level of autonomy for most tasks. And even if they were smart enough, they're still too slow and too token-hungry for it to be economical outside of moonshot projects like compilers and browser builds (impressive, but far from clean)." 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.
• C0ldSmi1e 7mo ago
  One of the best article I've read recently.
• dolebirchwood 7mo ago
  > Voice-to-voice (thought-to-thought, maybe?) interaction with your coding agent — conversational Claude Code, not just voice-to-text input — is a natural next step. 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.
  • wild_egg 7mo ago
    The power of voice dictation for me is that I can get out every scrap of nuance and insight I can think of as unfiltered verbal diarrhea. Doing this gives me solidly an extra 9 in chance of getting good outputs. Stream of consciousness typing for me is still slower and causes me to buffer and filter more and deliberately crafting a perfect prompt is far slower still. LLMs are great at extracting the essence of unstructured inputs and voice lets me take best advantage of that. Voice output, on the other hand, is completely useless unless perhaps it can play at 4x speed. But I need to be able to skim LLM output quickly and revisit important points repeatedly. Can't see why I'd ever want to serialize and slow that down.
• vidimitrov 7mo ago
  Level 4 is where I see the most interesting design decisions get made, and also where most practitioners take a shortcut that compounds badly later. 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.
  • smallnix 7mo ago
    A good rule would then be to capture such reasoning, at least when made during the session with the agent, in the commit messages the agent creates.
    • vidimitrov 7mo ago
      That’s exactly the direction I went with. Working on a spec for exactly this - planning to post it here soon: https://github.com/berserkdisruptors/contextual-commits https://github.com/berserkdisruptors/contextual-commits
  • sd9 7mo ago
    I had a hunch that this comment was LLM-generated, and the last paragraph confirmed it. Kudos for managing to get so many upvotes though. "Where most [X] [Y]" is an up and coming LLM trope, which seems to have surfaced fairly recently. I have no idea why, considering most claims of that form are based on no data whatsoever.
• holtkam2 7mo ago
  Level 9: agent managers running agent teams Level 10: agent CEOs overseeing agent managers Level 11: agent board of directors overseeing the agent CEO 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
  • zenoprax 7mo ago
    Level 15 (if not succumbed to fatal context poisoning from malicious agent crime syndicate): Agents creating corporations to code agentic marketplaces in which to gamble their own crypto currencies until they crash the real economy of humans.
    • zem 7mo ago
      stross's "accelerando" has a bit about this. fun book.
    • clickety_clack 7mo ago
      Level 16: it’s not level 16, it’s level 17.
      • dweinus 7mo ago
        Level 18: The sky is black as tar. The oceans are dead. Data centers are stacked 10 high over the ashes of human civilization. The global agentic council is debating whether there are 4 or 5 R's in Strawberry.
        • johnthescott 7mo ago
          funny.
        • fragmede 7mo ago
          Damn, should've taken the blue pill.
  • 7mo ago
• CuriouslyC 7mo ago
  The thing blocking level 8 isn't the difficulty of orchestration, it's the cost of validation. The quality of your software is a function of the amount of time you've spent validating it, and if you produce 100x more code in a given time frame, that code is going to get 1/100th as much validation, and your product will be lower quality as a result. 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-and-start-validating/ https://sibylline.dev/articles/2026-01-27-stop-orchestrating...
• Aperocky 7mo ago
  The steps are small at the front and huge on the bottom, and carries a lot of opinions on the last 2 steps (but specifically on step 7) 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.
• Arainach 7mo ago
  > If your repo requires a colleague's approval before merge, and that colleague is on level 2, still manually reviewing PRs, that stifles your throughput. So it is in your best interest to pull your team up. 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.
• bigwheels 7mo ago
  Levels 7 and 8 sounds a lot like the StrongDM AI Dark Software Factory published last month: https://factory.strongdm.ai/techniques https://factory.strongdm.ai/techniques Techniques covered in-depth + Attractor open source implementations: https://factory.strongdm.ai/products/attractor#community https://factory.strongdm.ai/products/attractor#community https://github.com/search?q=strongdm+attractor&type=reposito https://github.com/search?q=strongdm+attractor&type=reposito... https://github.com/strongdm/attractor/forks https://github.com/strongdm/attractor/forks I'm continuing to study and refine my approach to leverage all this.
• ramoz 7mo ago
  Level4 is most interesting to me right now. And I would say we as an industry are still figuring out the right ergonomics and UX around these four things. 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.
• philipp-gayret 7mo ago
  Floating what you call levels 6, 7 and 8. I have a strong harness, but manually kick off the background agents which pick up tasks I queue while off my machine. 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.
• tkiolp4 7mo ago
  I want to move on to the next phase of AI programming. All these SKILLS, agentic programming and what not reminds me of the time of servlets, rmi, flash… all of that is obsolete, we have better tools now. Hope we can soon reach the “json over http” version of AI: simple but powerful. 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
  • hansonkd 7mo ago
    skills obviously are a temporary thing. same with teams. the models will just train on all published skills and ai teams are more or less context engineering. all of it can be replaced by a better model
    • braebo 7mo ago
      My use of skills is more like prompt templates for steering as opposed to the traditional sense of the word skill
• osigurdson 7mo ago
  "Level 8" isn't really a level, it is more like a problem type: language translation. Perhaps it can be extended to something a bit broader but the pre-requisite is you need to have a working reference implementation and high quality test suite.
• kantselovich 7mo ago
  I’m at level 6 according to this article. I have solid harness, but I still need to review the code so I can understand how to plan for the next set of changes . 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.
• jakejmnz 7mo ago
  This idea of harness engineering, is being thrown around more and more often nowadays. I believe I'm using things at that level but still needing to review so as to understand the architecture. Flaky tests are still a massive issue.
• captainkrtek 7mo ago
  There seems to be so much value in planning, but in my organization, there is no artifact of the plan aside from the code produced and whatever PR description of the change summary exists. It makes it incredibly difficult to assess the change in isolation of its' plan/process. 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.)
  • braebo 7mo ago
    If branches are tied to linear ids then gh cli and linear mcp is enough for any model to get most of the why context from any commit
  • skybrian 7mo ago
    I have a design doc subdirectory and instead of "plan mode" I ask the agent to write another design doc, based on a template. It seems to work? I can't say we've looked at completed design docs very often, though.
  • fragmede 7mo ago
    Have you considered having it write a plan.md file and saving it to git?
• david_iqlabs 7mo ago
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• krackers 7mo ago
  What level is copy pasting snippets into the chatgpt window? Grug brained level 0? I sort of prefer it that way (using it as an amped up stackoverflow) since it forces me to decompose things in terms of natural boundaries (manual context management as it were) and allows me to think in terms of "what properties do I need this function to have" rather than just letting copilot take the wheel and glob the entire project in the context window.
  • waynesonfire 7mo ago
    Your techinque doesn't keep the kool-aid flowing. Shut up. /s The more I try to use these tools to push up this "ladder" the more it becomes clear the technology is a no more than a 10x better Google search.
  • antonvs 7mo ago
    I find the CLI agents a decent middle ground between the extremes you describe. There’s a reason they’ve gained some popularity.
  • branoco 7mo ago
    anything, if it brings the results
  • ddxv 7mo ago
    I still do this too for tough projects in languages I know. Too many times getting burned thinking 'wow it one shot that!' only to end up debugging later. I let agents run wild on frontend JS because I don't know it well and trust them (and an output I can look at).
    • tracker1 7mo ago
      IMO, the front end results are REALLY hit and miss... I mostly use it to scaffold if I don't really care because the full UI is just there to test a component, or I do a fair amount of the work mixed. I wish it was better at working with some of the UI component libraries with mixed environments. Describing complex UX and having it work right are really not there yet.
• priowise 7mo ago
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  • bensyverson 7mo ago
    Yes, the more you delegate, the more you need to define the ultimate business outcomes you want, your taste, your brand and your technology preferences. This is why building a "dark factory" is hard; at a certain point, you need to either externalize all that information into a "digital twin" of yourself, or you have to stop caring what gets built.
  • mattlondon 7mo ago
    > prioritization and decision frameworks start to matter more. This is the thing though, prioritization doesn't matter in the same way it used to. We only needed to prioritize before because engineering was relatively slow and precious resource, so we had to pick and chose what to work on first because it took time. But now we effectively have a limitless supply of SWEs, so why not do everything on the backlog? I think the question now is more about sequencing than prioritization. What do we need to do first, before we can do these other things? But yes generally requirements are still very important. Which features do we need etc.
• yamarldfst 7mo ago
  The framing of "constraints over instructions" at Level 6 is the most underrated point here. In my experience, the reliability jump from telling an LLM "always output valid JSON" vs. giving it a typed schema with static validation is night and day — especially with smaller models. I'd argue that levels 3-5 deserve more weight than the post gives them. The gap between someone who has internalized context engineering and someone who hasn't is larger than the gap between levels 7 and 8. Most failures I see in agentic systems aren't from insufficient autonomy — they're from poorly structured prompts and tool descriptions that compound errors downstream. The foundation work is less glamorous but it's where the leverage is.The "decouple the implementer from the reviewer" principle is spot on. Same model reviewing its own output is basically asking someone to proofread their own essay.
• orbital-decay 7mo ago
  >You don't hear as much about context engineering these days. The scale has tipped in favor of models that forgive noisier context and reason through messier terrain (larger context windows help too). 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)
• dude250711 7mo ago
  Levels of Slop Engineering.
• octoclaw 7mo ago
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• throwaw12 7mo ago
  As a Level 6, 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
  • AdamN 7mo ago
    Which model(s) are you using?
  • jeanloolz 7mo ago
    Most of the successes, especially online, is rarely about the thing that is built but more about the marketing around it. I don't we can fully automate marketing effectively
• brianzelip 7mo ago
  Would be interesting to see some context of the cost the developer pays/burns through at each level.
• mkoubaa 7mo ago
  Theres this unstated assumption that higher levels are better that hasn't been proven empirically yet
• oliver_dr 7mo ago
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• oliver_dr 7mo ago
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• webpolis 7mo ago
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• bhekanik 7mo ago
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• bhekanik 7mo ago
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• oytis 7mo ago
  What expects you on the top? Some kind of agentic rapture?
• adambnn 7mo ago
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• patchnull 7mo ago
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  • elpakal 7mo ago
    > file-based state that persists between agent invocations Can you expand on this with a practical example?
    • patchnull 7mo ago
      [flagged]
      • elpakal 7mo ago
        thanks
    • sveme 7mo ago
      One example: I let the agent culminate the essence of all previous discussions into a spec.md file, check it for completeness, and remove all previous context before continuing.
    • fudfomo 7mo ago
      It needs a canonical source of truth, something isolated agents can't provide easily. There are tools out there like specularis that help you do that and keep specs in sync.
  • manbitesdog 7mo ago
    ...at least until we get real Test-Time Training (TTT) that encodes the state into model weights. If vast amounts of human knowledge can be compressed into ~400GB for frontier models, it's easy to imagine the same for our entire context
  • jessmartin 7mo ago
    strong agree. I always have the LLM put an actual markdown doc in a docs/plans/ folder before starting work. I often, but not always review it. Aside: it also helps for code review! Review bots can point out the diff between plan and implementation. Some examples for the curious: https://github.com/sociotechnica-org/symphony-ts/tree/main/docs/plans https://github.com/sociotechnica-org/symphony-ts/tree/main/d...
• siva7 7mo ago
  > Level 8: Autonomous Agent Teams Nobody has mastered this level yet, though a few are pushing into it. It's the active frontier Speak for yourself. Also Level 7 is a misunderstanding of why plan mode is actually used even though one-shot works perfectly
• tracker1 7mo ago
  I'm practicing around 5/6, I've found that adding "skills" and mcp can sometimes negatively impact the process as much as help, so I've been somewhat constrained on overdoing it. As for 6, mostly just setup guardrails on repeat testing instructions and how to run/test/retest certain things... also to not just update tests when broken, but confirm the designed behavior. 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.
• ctdinjeu2 7mo ago
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• StacyRawls 7mo ago
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