For me it’s actually the opposite, and Claude Code’s plan mode isn’t nearly sufficient. Personally I ask Claude to write down a markdown file with its plan, then review the plan using plannotator, and then go back and forth (most of the time it’s actually the comments that are the problem, not the code).
Then start a fresh session, seed it with the plan, tell Claude to find ambiguities / friction points / oversights, resolve those, and then implement it.
Review once again with plannotator, go back and forth, and then send PR.
Maybe not the “vibe coding” that was once imagined, but this does ensure I am fully aware of the code and architecture, the quality, and this also prevents long term degradation.
Currently looking for a framework for managing this in a more formal way, and I think it's probably beads, but interested to hear from others.
Might be worth a look if you’re evaluating alternatives to Beads.
I have some older projects that use beads (I still run an old version without dolt that's imho pretty good overall) but lately with Fable also have a few newer projects where I just have the agent write docs and keep a worklog with the what/why/decisions etc. (I think I read it here on HN somewhere and figured I'd give that a try.)
The latter seems to work pretty well for now (slightly better than beads) but I'm always looking for ways to improve it. This could be an interesting replacement.
Great work man.
“ayy lmao”
I asked fable to look at my interaction patterns and clearly stated my frustrations and the problems I wanted solved, and it designed a simple process to track things in git and built a couple simple session hook skills. It’s pretty lightweight and I’ve been very happy with it for a couple months.
I get a long way using models like Opus to make a plan of action and a bunch of tasks, and then using Deepseek to implement that plan of action. Saves a bunch of money and is fast.
- I have a record of work done and work to be done that helps _me_ when I come back to the project after several months. It’s committed and lives with the code.
- when a task inevitably ends up more complicated than I thought, I can in that session break it up
- I initiate sessions from multiple computers, so things stay in sync (through git)
- I also have a “tooling” repo that builds out some views of the work and hosts it for me to see when I’m on my phone.
- The hooks let the agent manage all of the workflow/task management, so there’s very little management overhead for me.
I rejected beads and JIRA. I wanted something more lightweight.
My main conversation is usually with an orchestrator that hands off work to various (usually cheaper) subagents to plan / review / etc. It has instructions to find the correct model for each task and not to do too much itself so a multi-phase plan automatically gets a fresh subagent for each phase.
I also found that having the design reviewed by multiple agents has very little marginal value. The review agent will always find something to improve, but mostly it’s just nit and not anything super important.
I used to let Claude just upload the html design doc to Claude artifacts for me to review. Recently I switched to codex and started to use my own tool https://github.com/hyperlogue/r3 to complete this workflow.
I wonder if it's just a consequence of a gigantic training set full of comments completely out-of-date with the code, leading to the model considering this "normal"
I now make sure to do a big decommenting pass before every PR.
But I also am starting to just let go and stop caring. It’s not clear to me that it causes problems down the road, it’s easy to strip out en-masse if needed, and in my experience, agents now are really good at read git blame, the commit log, even prior agent transcripts if available to sleuth out when a change was made and why. So yeah, it’s annoying, but the code agents write for me is increasingly never read by a human, so does it matter?
When I read, I skip most comments, especially the larger "Javadoc" style. My brain sees them colored differently in the editor and it doesn't even take mental effort. Then, when I have a question about the code, I look back up for a relevant comment. That doesn't happen very often.
If Claude writes great code and leaves a garbled Claudese-but-accurate comment ... I can read and comprehend (with like 5x the effort of a human comment) ... that's a small price to pay.
(I do have detailed instructions for it on how to comment (or not) but it has not fixed this.)
It’s always “you explain only what but not why” or “this is way too much prose” or “these comments don’t belong here, they should be inline comments” or “this is completely redundant as it’s already obvious from the code”.
I do find that once I beat it into submission and the codebase is “clean”, the new code it generates gets better and better, which makes sense gives its pattern-prediction nature. But it seems like there is work to do for Anthropic in terms of getting Claude to not confuse code comments with dumping its interactive discussion state into there.
5.5 is much closer to Fable so i don't even need it. I am pretty sure it's got Fable's DNA in it.
I really need to find a role where I can do more DX...
I get great review results (as good or better than colleagues using superpowers or even adversarial review skills) just by asking Claude to review a PR and spit out results in order of severity.
Note that this is only really necessary for complex work that I don't know yet what the best way to do it is.
I've tried doing it your way as well, but there was just too much fiddling about with writing the plan somewhere, then having another session rebuild their context with whatever info is in the plan. It really didn't result in better output for me.
Currently 9 times out of 10 I just say to the model: xyz is the problem/bug/feature, fix it. Since about Fable and Opus 5, this is more than enough. Opus 5.5 (and previously Fable 5.1) got even better at this. However, this is in a codebase where there are already a few hundred thousand lines of code for the model to look at to see how we generally attack things in our codebase.
Claude Codes plan mode I never use anymore, it was useful a few months ago because the models had a tendency to just start doing work and forget I specifically told them not to. But the UX is just annoying and the models now do adhere when I tell them not to change anything.
Plan mode ensures I'm spending fewer tokens on the code-test loop, and more on the arch/design, and allows me to keep appraised of what's going on, while planning for future changes better.
Maybe folks who don't need planning, don't have as much concern for the details, and are happy enough with just evaluation of if it works or not.
I've tried doing the incremental, iterative approach with just Code and it's just not as effective unless you're working on something simple or experimental. Or you're shipping to something non-serious or perpetually beta.
Then telling Claude to work on a document, the instruction is kept to its core.
Now when bcherny explicitly mentioned that it merely adds a single line - it explains why I don't need it.
What may be concerning about "super plan" mode from the creators (or a skill, for that matter) - is that tuning the amount of effort, and how much deep to dig - may become too hard, as it will interfere with several embedded paragraphs explaining what to do, how to do, where to do, etc'.
What I do look for is even better plannotator ability to track changes, combining historical comments (like Google docs), and git blame of several "generations" before current reviewed doc.
Roughly speaking, I'd be happy if plannotator would persist something similar to github PR reviews combined with Google docs comments & suggestions.
I personally still find planning a valuable mental exercise; it's not so different from pre-LLMs and whiteboarding or otherwise taking the time to consciously plan a set of work.
How do I use plannotator to review an arbitrary markdown file? It always opens the Claude Code plan file for me.
So again: You don't need plan mode, auto mode works just fine, there is no difference in the workflows here.
- strategy document
- "sprint" document with technical implementation
- actual implementation
- e2e testing scenarios updates
Every step involves iterating with Claude on it with me in the loop (setting the direction then resolving the "founder questions" as they appear), and importantly a different model for review/code-review, be it Codex (usually, it's great at it) or Antigravity/Gemini (sometimes finds novel things, its precision and recall are abysmal but on the odd occasion it has good accuracy). This iteration on the high-level plan then on the implementation plan is essential to me, and IMHO part of why people are surprised that I tend to get solid results from LLMs. At the very least, it allows me to fill gaps in my own knowledge (primarily front-end development) and be more productive than writing the code myself. I cannot stress enough how nice it is to have a partner in the high-level system design – yes, it often suggests utterly moronic ideas, but the overall experience is still net positive and getting better every quarter.
Either way, plan mode isn’t going away. You can always /plan or ask Claude to enter plan mode. We might re-map the shift+tab keyboard shortcut to something else by default for people that don’t use plan mode.
Strange response. I agree with the parent comment here, plan mode lets me ensure that I have specified everything correctly before it gets built which is far too late. I don't see how an improved models even matter to this workflow. Is Fable going to read my mind?
Boris is saying that you don't need /plan to get the model to plan, you can just say "Let's plan this out" or similar, which at least matches my experience. Your experience may differ, of course, but it's not even clear we are talking about the same thing.
Maybe some people have not been long enough on this rodeo: This used to be an actual issue. You told the model "DONT START CODING YET" and yet, surely enough, starting to code it did. That is what /plan etc were supposed to fix.
Sounded to me like you need a plan.
My approach is to take the statement of work or problem definition and iterate on that myself until I'm really clear on what is the goal. I therefore have a good some good ideas about what the plan should be.
If extending an existing application, which is usually the case, then make use of the plan documents that I had written before AI arrived on the scene. These sre documents in markdown form that say step-by-step how to, for example, add a new report to the system.
It makes some plausible choices and you can retroactively ask it to make different ones later, if you want.
This hasn't been my experience.
> Or it will litter the code base with defensive code and comments about the path not chosen.
I've definitely seen this, though.
Basically every big tech company maintains "codebases with millions of lines built upon decades." Talk to your friends at a FAANG and ask them how they're using Claude/Codex.
Merging after reading the PR description is just how it's done these days, and if you can't do it reliably, your harness, devloop, or model is simply behind the times.
But you're doing the same with your "how it's done these days".
These days things are done in many contradicting ways, and it probably will take at least a few years to settle on common normal.
I mean, you're discussing this with a marketer/someone wearing a marketing hat, who works for a company which needs people to use as many tokens a possible. That's their reality
I see you didn't disagree with the main thrust of my post.
You'll also note their 'About' is empty. Regardless, engaging with HN in this manner is de facto marketing/PR. Trillion dollar companies don't just let anyone post on high-profile social media websites for any length of time without permission from marketing/legal/PR.
Which is fine because it just put together a plan and didn't spend 10 minutes rearchitecting everything.
It tends to be small decisions way down the stack that bubble up, or an incoherent data model that can’t handle what you’re asking for cleanly.
Eg I was messing with a state tracker the other day. The state tracker assumes a container is either currently running, or fully removed from disk.
The LLM chose to remove the state file when the container is stopped and then to remove it after, which leaks container storage.
The LLM is kind of stuck though, because every option other than “rewrite the data model” has negative outcomes and it probably violates user expectations to launch a massive rewrite there.
0. https://github.com/mattpocock/skills/blob/main/skills/produc...
/plan is still useful, I still need to review what it's going to do and still make revisions. But there's two phases: hammer out key design decisions then write and amend the document.
If I knew exactly how I was going to build something, I would have built it myself. But since there's some ambiguity in the portions of the project I'm less familiar with, I rely on the plan to not only help me understand the decisions Claude has made for me but to keep Claude constrained to the decisions I've made. It's very frustrating to waste tokens on having to refactor something because
> But for larger things, I try to take a waterfall approach with well defined milestones.
> If I knew exactly how I was going to build something, I would have built it myself
Aren't these contradictory? If you don't know exactly what/how to build, how can you do waterfall?
1) "Maybe waterfall works now" - plan mode, take care of all the nits and issues that the bot leaves on your PR, wildly overengineered "enterprisey" solutions with a lot of bells and whistles all over the place but very poor end-to-end user story test coverage that results in user experiences with a lot of good test coverage of the edge cases of how a given step might fail but little thought towards overall user flow and throughput. Because part of the issue with waterfall was assuming you could design the right tool for your user up front.
2) "Maybe code doesn't matter anymore" - The just ask for something when you need it approach, which results in weird janky individually-sorta-working but strangely-overlapping six-variants-of-the-same-thing that makes it hard for your users to develop a single consistent mental model of the thing they're using, and that changes super frequently.
They both end up with a lot of other bad-for-velocity things that I assume are inherent to how the tools have been refined in response to last year's criticism, too. Super verbose comments. Extensive - without much eye toward runtime - low-level test coverage that might miss the forest for the trees and also slows down the next round of iteration cycles. A plethora of new proper nouns all over the place that make the documentation an ouroboros without a good entry point.
This is weird to ask because I feel like of course the model isn't omniscient? Isn't the whole point of iterating on a plan to assess impact, risk, know your (the user) variables, user impact, product impact, etc for making a change? I cannot count the times even in the past few months where I start a conversation with my C suite because their desired outcome would have a potential negative impact elsewhere for other products or users.
Is this just not something that comes up at Anthropic?
It would be nice if with new model releases claude code also gave a bit of a model 101 that tells you evolving ways of prompting it that the insiders have picked up. I know there’s the prompting guide in the claude docs, but this is often very broad and most people don’t know about it.
I absolutely see fable and opus 5.5 misunderstanding intent, but that just seems to be a feature of necessarily underspecifying in a written prompt. Just today, I gave opus 5.5 a simple task to spin up a new environment for work. It read the ticket, which was decently specified and knowing the codebase as well as "Ghasp... reading the code" I had to correct it about 5 times to do it in a way that I would have expected it to. Getting the pipelines right, environment variables, and configs. It was all relatively straight forward imo. Then I had to prompt it to clean up its corrections, because it left a workflow variable in the github action that some intermediate step required but the final solution didn't. I definitely would not have caught that if I didn't read the output. Idk, there seems to be a natural limit as to how much it can infer and I have no idea how to fix it. I did write about it [here](https://javiergonzalez.io/blog/the-assumption-problem/) though.
OP wasn’t even actually critical of LLMs, they were just saying that plan mode was helpful to stop the model from making incorrect assumptions when you want you don’t specify everything you should.
Summaries that don't tell me when it's changed direction in a timely fashion, but I am only told way later, when I have to undo. Really bad judgement calls regarding where to fix bugs, changes in implementation decisions, taking action when I am asking a question directly, not passive aggressively asking for action... when 5, 3 days ago, was proven to be untrustworthy, switching to very little supervision sounds like a strange thing for a customer to do.
5.5 and 5.1 have major Rain Man (savant) syndrome. Excellent at many hyper-technical things, absofuckinglutely boneheaded at anything that a human (or an earlier model) would understand - like how to write copy, what a human would expect in a given situation, various types of norms...
it's infuriating because it's a sophies choice - dumber model but better human understanding, or better technical model that you have to explain things to over and over like a toddler.
I greatly prefer this, since it lets me iterate on the plan with Claude for a while without it repeatedly asking if I’m ready to implement the plan.
Once I’m satisfied, I usually start a fresh session and tell it to implement the plan.
For smaller plans, you don’t need the file. Just ask it to come up with a plan. I don’t recall the last time it just started implementing if I only asked for a plan.
It wastes a ton of tokens as well and those are not cheap.
But as skills and memory are populated over time, Plan mode isn't as necessary. It becomes simpler to let Claude just build and get something general in place that works, and then refine from there. Auto mode will ask essential questions.
I still use Plan mode for big feature changes, to confirm that I've asked for what I want in the right way. I tend to prompt casually, with only a few specific details. Plan mode helps me see the whole picture before committing. In a few cases, it also helped me decide the feature I asked for was wrong.
So it sounds like we use it somewhat similarly, just taking a glance at it before the work starts, and that's becoming harder to do in my experience. It is important because problems with the overall plan or strategy end up magnified the further you proceed with development.
Most people aren't precise when initially describing their problem.
Jumping straight into implementation skips the part where we refine and better define what it is we're trying to do, and think through the implications of those changes.
I suspect "trusting the model" doesn't really work at scale with finite resources.
Also this is a way less removed process that I want nothing to do with. The more removed I am from the process the more I hate my job, get burned out and genuinely wish that Anthropic never existed.
Even if it could "just know" or infer my intent. It wouldnt be desirable.
Edit: Oh yeah its Boris, hes one or the most disengenous shovel sellers on earth right now.
The Anthropic employee literally told you plan mode is a "we're still planning!!!" at the end of the prompt. Plan mode is useless because you can literally type "dont write code yet" and get the same effect, not because you should never plan in general.
* Scoping discussion - do research and figure out approach (auto mode)
* Planning - take the scoping and convert it to a concrete plan for review (plan mode)
* Implementation - put the plan into action (auto mode)
I find this works really well for my workflow, and it is really easy to trigger each phase because the model has clean boundaries. (This workflow is articulated in my user CLAUDE.md) Plan mode is still useful to me as it forces the model to double-check its plans (I find even with Opus 5.5 it still discovers gaps), and it gives me an opportunity to clear context at a really good spot.
So I would still consider plan mode to be useful. It would make me sad to see it go.
PS- I have a Claude Marketplace directory submission for an MCP server that has been sitting in review hell for six(!) months. I've never received any outcome other than "In Review". I hope I'm not asking too much but would it be possible to put me in touch with someone who might be able to help here. Nobody has ever replied to messages sent to mcp-review@anthropic or the "Anthony at Platform Operations" inquiring about status, and we are getting frustrated
I tried plan mode when we first added it to github copilot and it didn’t stick for me, until very recently, when I used it the way you describe. I was just putting it too early in the process, turns out I have to do a bit of exploration and discovery on my own and most of the time I can skip plan but now I have an intuition for when to engage it so it can interview me to clarify the last few things it needs before implementation. This also made autopilot mode work a lot better for me.
Historically, plan mode served two different roles:
1. making the agent’s instructions precise enough to execute 2. helping the human understand what was about to happen
I think #1 is less necessary as agents get better. #2 is going the other direction, it becomes more important as the model is able to do more on its own because larger chunks of work are happening with increasing complexity.
Where I’ve changed my mind is the interface for #2. I increasingly think an interactive, iterative workflow is closer to how people actually build understanding than being handed a long generated document, especially one they didn’t author themselves.
The human-understanding problem is very real though
Also I hope your delivery goes well. My wife (and co-founder) had a challenging delivery and it really put life in to perspective for both of us on a range of issues (how much women's pain is minimized in the health system requiring stronger personal advocacy than I would ever have expected).
As far as plan mode, I still find it essential in keeping agents on track. I build propelcode.app and have a variation on plan mode I still find useful, happy to trade notes on agentic coding if youre interested.
Propelcode looks awesome! So cool to see different people and perspectives shaping this space.
And thank you for the article - it was a good read. I still see folks in my org playing "throw spaghetti at the wall and see what works" and getting frustrated so plan mode (mostly point 2) has been their guardrails almost as much as for the AI.
Can't help but think "Doesn't matter if a machine or a human with (even slightly) different background wrote it", maximizing information flow is maximizing common assumptions and "culture" to only have to communicate a small set of current information for the task at hand. Being a team means having built a joint context so to say. This has always been the purpose of design documents and they always were too big or too small. Because you did not write them, but the others. If you only produce code you think they are the past and useless. If you iterate and your team grows, you start seeing the value in always current docs that are containing just what is not in your everyday culture.
All the best for you and your growing family. I had a similar experience recalibrating my values...
In case anyone else is interested, the skill is public: https://github.com/Mudlet/Mudlet/blob/development/docs/demo-...
I was quite surprised when I learned this (when Claude Code edited a file despite being in plan mode). I had previously assumed "plan mode" was a harness level concern, and restricted what tools could be used. I didn't expect that it was simply an addendum to the prompt.
But I think it gave me some good insight into where the heads are at of Anthropic employees building this. Basically, leaning on pushing everything to the model. That's why alignment is so important: a "sufficiently advanced" model doesn't require any tooling infrastructure around it, and I suppose Claude Code devs are targeting that future. I had previously thought there was more to a harness, but with "auto" mode these days, it seems like there's no desire to build in that direction.
Basically, Claude Design is primarily a huge prompt.
That's all.
There's no "magic sauce", and with the right tools to call, even your local LLM can implement "Qwen Design".
Also if you are working in a heavily vibe-coded codebase, as Claude Code reportedly is, it's not that surprising if the human doesn't really understand it or have anything useful to add in a collaborative context.
I haven't reached for Plan Mode in awhile--maybe, on some blank folder/canvas and I just want that cute "questionnaire" DX to get me going...
But on the whole, CC is smart enough to know when to "rush off and act" and when to "pushback", which is great--and it's no big deal to tell it to pause/stop by adding "what's your thoughts?" or "feel free to pushback" etc to my prompts to make it start a back-and-forth with me (the fact that you say that Plan Mode was really nothing more than a prompt anyways is reassuring).
And yeah, when you're deep in the weeds, you could (and can!) have multiple threads of thought/work going on in the same convo, that stopping and starting a plan in Plan Mode seems like a regression.
(happily using Claude Code Opus 5.5 on High rn)
For the record, this isn’t unique to Claude. ChatGPT and Gemini do the same, each with its own quirks. ChatGPT got extra credit for being the only one who allowed the function to also take a CA file for server authentication.
Don’t get me wrong: LLMs are the future (maybe even the present) of software development but I think there’s some way to go before they can be entirely hands-off in some areas. I still find myself having to course correct designs and plan mode helps me with that.
And of course, thank you for your work on Claude. :)
But there is still a ceiling above which it is necessary to "preload" the context window before starting to call tools and get into the meat of the work. You want to establish domain language (especially with Claude models which otherwise will invent their own, and it will be inscrutable) and key requirements and assumptions. You want to do a Q&A iteration cycle with the LLM. You definitely should do a sanity check that the LLM actually "understands" what you were trying to achieve, and then make sure that understanding is coherently and plainly stated in the prompt. All of that seems to be necessary still for just about any serious task, if you actually care about the quality of the results and/or don't want to burn hundreds of thousands of tokens on flailing around to get to a good quality result.
So no, you don't "need" plan mode. But you do still need to do all of the things you would do with plan mode.
That's when I need to learn about implied patterns, do's and don'ts; not from theory but in the context of my own project. Unfortunately, the model tends to keep implicit knowledge implicit. But I can ask during planning.
The feature did get less useful over time when the model started babbling in newspeak more and more. When it threw a thousand words at me even in concise mode.
So I don't want to let the toolmakers off the hook here. There's a lot to win that would make plan mode much much better without changing plan mode itself.
> In Claude Code, all plan mode does is add a little reminder to every user message along the lines of “you’re in plan mode, please don’t code yet"
Can't help but think if plan mode isn't useful as you say because it's implementation is lacking in claude code, hypothetically speaking.
What I can say is that with other harnesses plan mode helps stabilize my workflows. Actually synthesizing code is only part of the process, lots involved in taking a work-item to production end-to-end and plan mode helps give this flow structure. More than that it's an opportunity to regroup before committing to changes. It slows down the process to a rythim that's sustainable and smooth, which ends up speeding up the process.
So if plan mode in claude was designed to speed up code churn, while oh my pi for instance designed plan mode to be strategic, that might account for the different perceptions here.
And it's not to say you should force yourself to use it, but if you are planning on cutting this mode off the loop just beware of the possible side effects.
I would really like a dry run mode that just disables all external commands from the outside so Claude doesn't proactively go about changing things.
When a test case fails, the relevant part of the plan is surfaced in the error. I find this helps Claude stay on track for longer - I've been able to do 12h most times and even up to 48h unattended (11h of API time) with good results.
Then whenever I do check in, I ask it to update the HTML with current state in an append only fashion (sort of like it's writing a blog), and then based on that, we iterate on the end to end test (I think of it as a "test harness") - update the test cases and error messages.
I've been able to build some truly large projects this way, both greenfield and up to spec (for example, a video game I've always wanted to play), and brownfield while staying within the conventions and design of the codebase, and with very little attention required on my part.
seems like plan mode could turn off some tools, even if it doesn't change the set offered to the model, the ones that they have which would mutate your codebase could just not work with an error message, and plan mode could change permissions in the security approval prompt for "auto"
anyway, isnt the right way to know if plan mode helps or not, to run an experiment? we're all guessing unless we have data
read only agent mode sounds straightforward and useful to me
1. I dont want to have to accept every time Claude touches our DB
2. I'm scared out of my mind it might do something bad to the DB
Plan mode gives me enough confidence that it wont do (2) --> allowing me to give it enough permissions to do (1)
For my small-scale sqlite dB, it gets read access, and I encourage it to test modifications by copying it somewhere and writing into that.
Scale-dependent, but I hope to not have to work at a scale where it gets write access to the production DB. That just seems like asking for trouble.
I was experimenting with a rather complicated backfill operation, were I had a validation script I understand and have Clod come up with the backfill script. I was running against a local prod copy, and it proposed running the actual (unfinished) backfill script against prod.
It didn't have access to the secrets and I also caught the command, but a good reminder that this stuff needs guardrails.
Hallucination not a big deal when it's on the surface layer. But I can't imagine the damage it could do if it hallucinated while building/validating a "load-bearing" component and then continued down that path
we DO daily snapshotting, so the risk is limited... but still spooks me
What could possibly go wrong with building a data-focused company on a foundation of violating the most fundamental precepts of data management
I miss that. It worked really well, and it kept the context clean.
Plus, I usually plan with a more expensive model and guide implementation with a cheaper model (with smaller validation calls back to a more expensive model)
I ended up building out tool an MCP server that I use as a bit of a psuedo harness for Claude. I have a variety of multi-step workflows that are basically micro-skills stacked on top of each other. This helps me make sure that I can get Claude to think in a repeatable and reliable manner.
For coding, I've found that I have a few specific steps that Claude needs to do before I'm comfortable letting it loose:
* It must extensively explore the code base (including certain areas that it misses)
* It must think about what it doesn't know or is making assumptions about
* It MUST scaffold out it's intentions. Essentially, it can write comments, classes, and method stubs - but no actual content. Very much like a spec, but since it's in and alongside other code, it's much easier to identify problems.
* It must spike and validate key assumptions. This, plus the prior step, are the only way I've figured out how to avoid it ending up in a confusion loop. Too often it looks at poor-quality code it's written and thinks it's a long-term solution. By avoiding writing code as much as possible, it knows that it's draft content.
* Only, then can I review it and send it it.
Said MCP server (missing the actual ops): https://github.com/clops-mcp/clops-mcp
Now that I’m frequently designing and delegating day/week scale features, the flow has to change; having the agent go off and build a spike can be a quicker way of us understanding the design space and constraints (especially in a huge codebase). I still have the agent write and update a spec doc as I go, but it’s not waterfall anymore.
At least for my kinesthetic learning mode a rough code PR stack is usually way better than a plan doc anyway, and tokens are cheap enough (vs my time) that going further than just a plan is often cost-effective overall.
The dream of course is (say it with me) loops, but that doesn’t tend to work for me on new features often.
1. I want to know whats going to happen, at least at a high level, before changes are actually made. 2. Plan mode helps me flesh out the missing details of my plan before being mid-execution 3. In situations where I have a limited budget for AI usage I will often times use a high powered model like Opus 5.5 or Fable to make a detailed plan, then scale down to a cheaper model for implementation. I feel like this saves cost in the end.
I get plan mode is basically just a small hidden prompt. I get that I can basically just preface my prompts with "make a plan only, don't make actual changes." Maybe this is just a UX trick, but it works well for my brain.
Aider, Cline and many other agents had plan mode before Claude Code existed.
But with GPT 5.6 Sol, I'm still finding that the model makes conceptual mistakes, or gets edge cases wrong, or assumes incorrectly (making an ass out of both user and model). In many cases, I need to at least refine the proposed approach, or amend, correct, or flat out just stop and start over. Not planning and catching these errors, and just letting the agents code their code, would mean I'd have to rollback and redo many times. What a waste!
For a current project, which is ~33k lines of code, I'm also finding that I know the codebase better than the model, and that's vital at the planning stages too. If I wasn't in the planning loop, the model would have reinvented various wheels a few times over. How much spaghetti do you want with your code?
As always, I may simply be doing this wrong. But I'm personally not convinced that the plan is dead, or that I want the plan to be dead. Planning is also good for me -- it keeps me thinking about the code, prompting better, guiding the model better.
If I'm no longer on top of the codebase, then at some point my prompts will devolve to "Do the thing with the thing, that does thing". And I don't want that.
On the other hand, for a low effort hobby project: just do the thing.
I find myself endlessly ctrl+c ing claude now as it flies off doing deep first principles analysis to work out how to find a thing it isn't sure about but I know the answer. Being able to give it that answer without needing to ctrl c would be a massive improvement
You don't need to actually change the tool schema or break the prompt cache to do that. In the tool itself you could just check if it's in plan mode and reject the tool call...
They’re most useful for broad changes (new features, refactors, etc.) where it’s helpful to avoid breaking changes or unnecessary scope expansion.
The new models are great, but they do more by default, which means I’m finding myself explaining what _not_ to do more often than with previous models (where they’d often end too early).
In my case, the previous plan mode was too ephemeral, and I like having one source of “truth” that sits across context windows without loss/compaction.
Also for session planning, as in when-can-I-walk-away-from-computer, its nice to know the particular rhythm of initial crunch - ask questions - make plan - do it. Especially with a 5 minute cache timeout.
I'm experimenting just like everyone else, but this is my process right now:
- Quick prototype
- Figure out the language of your app (what terms you want to use for things, what your UI design language will be, etc) and spec that, so you can use words consistently with the agent. You need to be able to describe the things you want well and consistently.
- Keep prototyping. Let the agent write unit tests along the way. Lock down behaviour you like, keep track of those things in a document.
- At some point your idea of the real architecture comes into focus, from actual use cases -- avoids the over-abstracting right away trap.
- Refactoring is cheap with tests, so start refactoring into the architecture you want.
- Your architecture won't necessarily be what would be best for a human, but it will be pretty close.
- Keep relentlessly iterating on small work.
- Things that were expensive before aren't that expensive now -- integrating a library, changing from one library to another, trying out a few architectural refactors, trying out different performance optimizations, etc. That stuff is all 'throw it there and see what sticks' now, so don't be afraid to try stuff which felt big before.
I feel like 'front loading' too much is just the wrong approach. You might feel like you're sitting there 'babysitting the agent'; but that's just what the hard part of the work (hard as in 'zjust slogging through it', not as in 'conceptually complex') looks like now. Your code is much more like clay.
Atleast that's how I'm thinking about it so far, but I'm not working on large sprawling systems that I imagine would need more pre-planning.
I find that the code is generally in a better place proportionate to the amount of SDD I actually do. But it's just a matter of where and when I want to spend my time.
But given that running an agent us cheaper than the cost of waiting for a slot to assemble the team to talk about a change (isn’t it always?), why wait with running the agent?
I propose updating the spec then do the implementation. This will most likely show that a few assumptions were wrong forcing some major or minor updates to the spec. Work through those and then let your team review the spec change together with testing the next iteration of what what’s build.
For who? The more control you hand over to the AI and let it think for you with no supervision, the better it is for Anthropic
Plan mode is a useful shortcut when I want to have an agent do read-only work without having to worry about giving it appropriate stop conditions.
However how about decisions? Do we expect the model to read our minds, just assume the best practice will be followed and that’s what the user want? Plan mode solves those, what is that am I missing?
For example, there are no shortage of web based uis for pi and they are all cool but I wanted a deep integration between artifacts and how I want to collaborate with the agent on them. So i built my own ui that mirrors what the pi tui sees. It’s chat based but has a deep integration with a GitHub style code review UI so I can leave review and comments whenever I want. Every agent message renders nicely in an annotatable markdown viewer so I don’t need an ask question tool and can more naturally get the agent on the same page as me. I want it to feel like I’m working with a colleague.
I don’t think these features are too unique but having full control over the experience is really nice. Flexibility over model provider, can tailor it to my work’s dev stack, and don’t need to worry about anyone breaking it with a million updates everyday.
Your perfect workflow can be realized in a day or so. You just need to go make it happen.
I've been building https://crit.md to keep that back and forth with agents - GitHub-esque interface and have agents respond to my feedback, iterating until I'm happy.
Admittedly like many others, I use it a lot less for actual plans now, models are indeed getting really good at just getting it.
I wodner what this product space will look like a year from now. Reviews are already dying.
It could still make the tools into no-ops or disabled if it actually tries to use them, without changing the context history at all.
I still use plan mode in Astra to come up with a plan that I then feed into Fable. I feel like OpenAI models still do better big picture investigation and planning, while Claude is the better software engineer, if that makes any sense.
Of course this could well come down to my own biases and the specific things I’m working on.
“I want to ...” / “Let’s ...” -> Plan
“Do X” -> Actually Act.
But then again I also have it configured to only ever answer questions instead of inferring them to be instructions (which I’ve seen others do differently).
- Most people suck at planning anyway
- LLM still don't give you a way to verify and understand to iterate.. you have to ask and then formulate and way so people barely do it, they just trust the vibe
IMO the current successor to plan mode should be the harness knowing when to tell the use "ok here's our overall current state in a simple diagram", auromatically
https://innerloop.test/breadcrumb (for reference)
Is this what happens when you vibe code long enough?
https://innerloop.works/breadcrumb
What a rookie mistake!
# YOLO mode
I just start my day writing about 20 queues /goal prompts and then check the work at the end of the day. It’s almost always right!
I have one session define a task, and provide a formal specification plus context in a "cover letter."
The session B, in plan mode, produces the plan back.
Session one reviews the plan and clears it, ratifying portions and often specifying specific changes.
Session one then executes.
What has been striking to me in this approach is that even with two instances of the same model (currently Opus 5.5), there are regularly corrections made. I use "project chat" for session A and Code for session B atm; it is very typical that Code finds and corrects details or oversights in the task spec; it is also typical (though less so with 5.5) that session A (chat) pushes back or clarifies things Code doesn't have the context for.
I have been afraid to open up the potential of negotiation beyond what this is costing as it is. But I am also afraid to simply skip the formalisms, because of the consistent correction that occurs in this back-and-forth.
Each component of the pattern is schematized, generated from a template, and validated, to keep things tight.
Lots of tokens! But I trust this process far more than "just typing" :)
I also tell it to write deviations and rename plans accordingly once done.
That way I keep the codebases I have to or enjoy to work on in my head and don't become too dependant on any provider or on stochastic parrots in general.
For a one-file change I don’t bother. For anything that touches auth, payments, or a shared schema I still want the plan written down first — not because the model can’t figure it out, but because I need a moment where I can still say “no” before it starts editing.
The mode was never really about making the model smarter. It was about making the human stop and look.
I’ve heard “earns its keep” in only two contexts in my life - the intro to the song “Regulate” and terrible Claude docs
So basically you don't know what the fuck you're talking about. Not everyone is employed to burn money.