I need more mechanisms for controlling long-running sessions and dynamically injecting my thoughts, correction, and nudges rather than faster ways to burn through my tokens without knowing if the results are going to be correct.

Rewriting Bun with dynamic workflows

An example of what dynamic workflows can unlock at scale is the recent rewrite of Bun. Jarred Sumner used dynamic workflows to port Bun from Zig to Rust with 99.8% of the existing test suite passing, roughly 750,000 lines of Rust, and eleven days from first commit to merge. One workflow mapped the right Rust lifetime for every struct field in the Zig codebase. The next wrote every .rs file as a behavior-identical port of its .zig counterpart, hundreds of agents working in parallel with two reviewers on each file. A fix loop then drove the build and test suite until both ran clean. After the port landed, an overnight workflow addressed unnecessary data copies and opened a PR for each for final review. While not yet in production, all of this was handled by dynamic workflows. Jarred will be writing about this more in the future.

Don't make me think.

All these knobs are also exposed in ChatGPT, which I am more familiar when chatting. Which one of the models? Do I go Instant, Thinking, Pro? Extended Pro? Oh no, maybe I need Deep Research.

Sometimes I think it's on purpose. I fear if I try a lowest knob, it will miss something. So turn everything up. And token usage goes up.

It's telling that they used "rewrite Bun in Rust" as the proof point here. It's cool! But the vast majority of software engineering doesn't start with tens of thousands of tests, where making them pass is the whole job.

In my experience, AI still drifts from what I meant it to do on anything bigger than building a widget. My time is spent suspiciously reviewing output for changes the agent snuck in, or invariants it broke. I talked with a friend recently where the agent broke the test harness badly enough that none of the tests mattered for 3 weeks. They did pass, though, so CI never complained.

There's something at the intersection of context engineering, managing that sloppy pile of markdown plans, and good old fashioning system understanding that's the real bottleneck.

I feel like there are more efficient ways to tackle the issues given.

You can achieve a similar result manually prompting to use subagents, yes. But the TUI for in flight dynamic workflows is really nice - great visibility into exactly what's happening.

Honesty, for anything larger than a 1 shot PR, it's worth firing off a workflow for better automatic context management alone (more work done in the first 20% sweet spot)

Like 90 agents ran to do a code review of a fairly small package I have.

They're really looking for us to increase token usage aren't they?

Is this a way to increase token burn?

I thought we covered this with Claude's C compiler. What changed?

I’m at the point where deciding what we should and should not do takes a lot more time than actually doing it. More agents just means running faster in potentially the wrong direction

Here is the solution to it. Built on a SQLite DB and MCP, blocking until the question is answered, supporting all possible question types, with a CLI or web interface for answers, `ask_human_question` fills the gap in efficient subagent management.

https://news.ycombinator.com/item?id=48320233

IMO, this style of workflow/agentics is how all SWE'll look like long term. Automate everything into a big pipe-y thing. How it's gonna be modelled is up in the air though. lots of different approaches:

mine: https://github.com/portpowered/you-agent-factory

https://github.com/ComposioHQ/agent-orchestrator

https://github.com/gastownhall/gastown

https://github.com/openai/symphony

I did find it uses tokens like crazy, i migrated Pixel Dungeon (java) to C# as a experiment, and it used almost 2 billion tokens. It was just 20 bucks due to deepseek flash, but i shudder thinking of how much money this uses when run on the real claude API pricing.

There ya go, the rewrite was for marketing.

Is this equivalent of DAGs for sub agents inside claude code? Can i pause and resume/retry workflows? How stateful are they?

Really appreciate it someone claude code can throw more light on above. I’m trying to see if I can get langgraph equivalent DAGs here.

https://tsz.dev

So far Codex /goal has been amazing but Claude Code /goal or even /loop does not work hard enough and gives up. I have observed it just claiming it’s “iterating” in a broken loop or simply giving up.

I am diffing Claude Code with them, I tend to agree with the analysis.

So far, versus my system, there are tradeoffs, but the dynamic workflows are over tuned to use way more agents that I have ever found add value.

It used 8 to diff our systems. I would have used 4, for example.

"the model sucks a bit so we just have best-of-4 & adversarial reviewing agents; surely one more agent will do the trick"

So, is this like a skill the LLM should follow, or an actual "workflow" in the deterministic sense?

If it's the former, is it even reliable for long running tasks? If it's the latter, can users interact with it?

Are we sure this is a good "success story" example?

https://blog.cloudflare.com/dynamic-workflows/

Also isn’t all of this already easy to do on any of the platforms (include Claude before this and OpenAI too).