[athrowaway3z]

I've tried most form of planning - from the basic AGENTS.md guide to keeping ./dev/ plan files, todo list tools, sqlite db with both minimal and extensive tracking, etc.

None of them have been worth it. A year ago the models needed to be reminded. Today they can follow a plan from text alone. This is my experience from working on a project alone - in teams ... i actually think the same lesson holds in the new AI paradigm.

My current scheme is basically this - in order of the task's complexity:

- Tell an agent to do something

- Tell an agent to make a plan then tell it to execute on it.

- Tell an agent to make a plan, write to a file, have a subagent review it, then execute it.

- Do the above, but instead tell the agent they're in a supervise mode and to have subagents implement as many phases and rollover with a handoff.md while they, as the supervisor agent, keeps driving the task to completion.

The latter two i have under a sigil so they're prepared prompts i can inject with a few keystrokes.

If i feel very fancy i'll tell them to update the plan with a checklist and add checkboxes, but it just doesn't pay enough to have 'init-prompt' level planning feature or tools if in the same context you already have files/read/write.

[bensyverson]

A while back I created a tool called Jobs [0] to help with this workflow. The pattern is:

1. Have a conversation with a smart model (Opus/Fable) about what you're building. Go back & forth until you've ironed out the important architectural choices (deciding what to build).

2. Ask the model to write up its plan in a Markdown doc, including a structured plan in YAML format (telling it to consult `job schema`).

3. Clear the context and tell a leaner model (Sonnet/Opus) to read the plan doc and then pick up the task via `job status`.

From there, the CLI helps the agent take the next step. I designed the `job` CLI through extensive iteration with agents, conducting user-centered design with the agents to make it as smooth and intuitive to them as possible.

When context gets full, you can pause, clear, and pick right back up. Using Jobs (or other tools like it), you can take on large, ambitious plans and keep the agents on-task the entire time.

  [0]: https://github.com/bensyverson/jobs

[syspec]

Did you read the article?

It's not about enhancing Claude. This article is about creating your own agent, and giving it the ability to create plans and tasks list for its or.

The way Claude code creates plans and tasks list for itself.

The article is about creating that in your own harness for things not using claude code, like say a custom LLM integration in your own web app.

[manishsharan]

Please don't take offense to this very dumb question:

Why can't you do the planning ? Figure out what needs to be done , break it down into small tasks and then ask the agent to execute those small tasks?

When we executed projects in the past, this is what I would do as a lead: figure out the overall software architecture and delegate the tasks to developers.

This way I always knew how the system worked and could extend it as needed. I am not in development role anymore but I am trying to understand why we are delegating planning and software architecture to coding agents?

[nl]

The kinds of detailed (and excellent) plans Opus or Fable can generate on our large code base would take me maybe 1-2 days to work through and they do in 10-20 minutes.

Maybe I spent 2-4 hours reviewing it, checking things with colleagues etc.

Then I press "go" and maybe an hour later I have a tested system ready for manual review.

It's plans are at least as good as any I've seen. Their weakness is if there are unstated assumptions I have about how things need to be done, so most of my time is now getting those assumptions stated properly and then reviewing.

Why wouldn't I use this? It's the best tool I've used in my 30 years of professional programming.

[DenisM]

Did you manage to setup a discussion with the agent to reveal such assumptions? Sometimes the shave wrong unstated assumptions when contradicted by evidence, but if we’re taking about a plan for the future the evidence is thin.

[nl]

> Did you manage to setup a discussion with the agent to reveal such assumptions?

This is what the plan review is for.

Usually it will have something like "modify abc.ts to update the widget number in the wnx collection" and I'm "hang on - why does that need to be updated when XYZ" and that subsequent discussion will reveal assumptions that are not shared.

[deadbabe]

Cognitive debt

[nl]

Non sequitur

[panarky]

I could do the planning but I don't, for the same reason that I could write the source code but I don't, for the same reason that I could write the machine code but I don't.

[evilturnip]

This is more or less what I do. Then again, I work on a small parts of the codebase at a time, so maybe the autonomous agent works better when you're doing larger refactors over large codebases.

Even in that situation, I think I would still only feel comfortable approaching the task as I would do it without AI, and using the AI to accelerate the parts that would be time-consuming. E.g. finding where/how feature X is implemented, how it would affect the overall system if I were to change it this way, etc.

[nnnnico]

whatever you delegated in the past probably also required planning by the engineer that went down and got it done, most planning done by agents is at this same level, agent explores the codebase, understands where to touch, tradeoffs, code-level architecture, and ask the user for more context or balance with assumptions and other patterns already present in code

[noodletheworld]

People get defensive when you ask this, because the they think you’re saying they’re being lazy.

…but it’s than just that (in most cases; I am just lazy sometimes); but fundamentally there’s a limit to how much complexity people can comprehend.

We are good at working at high level abstractions, modules with clear apis that can be sprung to together into some kind of feature.

You don’t need to look inside the black box of the module if you trust the implementer; Ive never opened up the internals of a calendar be like “how does this work?”. I just don’t care. It’s a calendar. I use the api.

I think most people are using these tools in this way; very few people are having an agent write a plan, then a sub agent review it, no human in the loop. Those are for prototypes and are yolo cowboys using open claw and playing with the phones instead of working; we have a few at work, but their PRs are regularly rejected as slop.

…but, realistically; many people aren’t software architects. They may not even know coding patterns, forget architecture patterns.

Having an agent spit out generic software architecture is probably better than what they were producing before.

Writing a module / feature using generic architecture and planning is probably better than random code spaghetti right?

It’s easy to lament the loss of craft here, but at the end of the day, the models today do an ok job of this. The models of tomorrow will probably be better at it than many people.

Architecture is easy composed to actually implementing things. You just wave your hands from your ivory tower and say “more event sourcing”.

[evilturnip]

"Having an agent spit out generic software architecture is probably better than what they were producing before."

If they were a poor programmer/architect, I don't think the AI would make the end result any better. It would amplify their lack of skill. Sure, the low-level code might be more airtight and idiomatic, but that's not even where poor skill really manifests itself. It's at the higher level of thinking in terms of the system and understanding the proper context of the business/technology, etc.

[noodletheworld]

This simply isn't true anymore.

High level generic advice from agents is often, in my experience significantly better, unmodified, than doing nothing.

Obviously its better to do it properly, but you know… opus 4.8 is a pretty great model.

You might be surprised at the quality of the planning, architecture and task breakdown that a simple prompt with some context hints can give you.

…at the end of the day, if I’m working with someone and they give me 6/10 plans based on AI instead of stupid/10 plans they dreamed up, or 0/10 plans they didn't even bother (or in too much of a hurry) to write; Ill take it.

Tragedy of the commons? /shrug

You gotta be pragmatic. It turns subpar contributors into useful contributors.