The only solution I've seen on a Mac is doing it on a separate monitor.
I couldn't find a solution here and have built similar things in the past so I took a crack at it using CGVirtualDisplay.
Ended up adding a lot of productivity features and polished until it felt good.
Curious if there are similar solutions out there I just haven't seen.
- Base Claude Code (released)
- Extensive, self-orchestrated, local specs & documentation; ie waterfall for many features/longer term project goals (summer)
- Base Claude Code (today)
Claude Code is getting better at orchestrating it's own subagents for divide/conquer type work.
My problem with these extensive self-orchestrated multi-agent / spec modes is the type of drift and rot of all the changes and then integrated parts of an application that a lot of the time end up in merge conflicts. Aside from my own decision cognitive space, it's also a lot to just generally orchestrate and review. I spent a ton of type enforcing Claude to use the system I put in place including documentation updates and continuous logging of work.
I feel extremely productive with a single Claude Code for a project. Maybe for minor features, I'll launch Claude Code in the web so that it can operate in an isolated space to knock them out and create a PR.
I will plan and annotate extensively for large features, but not many features or broad project specs all at the same time. Annotation and better planning UX, I think, are going to be increasingly important for now. The only augment of Claude Code I have is a hook for plan mode review: https://github.com/backnotprop/plannotator
I have between 3 and 6 hours per day where I can sit in front of a laptop and work directly with the code. The more of the actual technical planning/coding/testing/bug fixing loop I can get done in that time the better. If I can send out multiple agents to implement a plan I wrote yesterday, while another agent fixes lint and type errors, a third agent or two or three are working with me on either brainstorming or new plans, that's great! I'll go out for a walk in the park and think deeply during the rest of the day.
When people hear about all of these agent - working on three plans at once, really? - it sounds overwhelming. But realistically there's a lot of downtime from both sides. I ask the agent a question, it spends 5-10 minutes exploring. During that time I check on another agent or read some code that has been generated, or do some research of my own. Then I'll switch back to that terminal when I'm ready and ask a follow up question, mark the plan as ready, or whatever.
The worst thing I did when I was first getting excited about how agents were good now, a whole two months ago, was set things up so I could run a terminal on my phone and start sessions there. That really did destroy my deep thinking time, and lasted for about 3 days before I deleted termux.
This seems like it'd be great for solo projects but starts to fall apart for a team with a lot more PRs and distributed state. Heck, I run almost everything in a worktree, so even there the state is distributed. Maybe moving some of the state/plans/etc to Linear et al solves that though.
The spec file helps, but we found we also needed a short shared "ground truth" file the agents could read before taking any action - basically a live snapshot of what's actually done vs what the spec says. Without it, two agents would sometimes solve the same problem in incompatible ways.
Has anyone found a clean way to sync context across parallel sessions without just dumping everything into one massive file?
I only run a couple of agents at a time, but with Beads you can create issues, then agents can assign them to themselves, etc. Agents or the human driver can also add context in epics, and I think you can have perpetual issues which contain context too. Or could make them as a type of issue yourself, it’s a very flexible system.
What does your spec file look like when you kick off a new agent? Curious if you start from scratch each time or carry over context from previous sessions on the same project.
The routing layer uses tmux: `agent-doc claim`, `route`, `focus`, `layout` commands manage which pane owns which document, scoped to tmux windows. A JetBrains plugin lets you submit from the IDE with a hotkey — it finds the right pane and sends the skill command.
For context sync across agents, the key insight was: don't sync. Each agent owns one document with its own conversation history. The orchestration doc (plan.md) references feature docs but doesn't duplicate their content. When an agent finishes a feature, its key decisions get extracted into SPEC.md. The documents ARE the shared context — any agent can read any document.
It's been working well for running 4-6 parallel sessions across corky (email client), agent-doc itself, and a JetBrains plugin — all from one tmux window with window-scoped routing.
The SPEC.md as the extraction target after a feature is done is a nice touch. In our case the tricky part is that browser automation state is partly external - you have sessions, cookies, proxy assignments that live outside the codebase. So the "ground truth" we needed wasn't just about code decisions but about runtime state too. Ended up logging that separately.
Checking out agent-doc, the snapshot-based diff to avoid re-triggering on comments is clever. Does it handle cases where two agents edit the same doc around the same time, or is the ownership model strict enough that this doesn't come up?
[1] https://cas.dev
https://open.substack.com/pub/sluongng/p/stages-of-coding-ag...
I think we need much different toolings to go beyond 1 human - 10 agents ratio. And much much different tooling to achieve a higher ratio than that
Imagine a superhuman agent who does not need to run in endless loops. It could generate 100k line code-base in a few minutes or solve smaller features in seconds.
In a way, the inefficiency is what leads people to parallelism. There is only room for it because the agents are slow, perhaps the more inefficient and slower the individual agents are, the more parallel we can be.
So we are just now getting agents which can reliably loop themselves for medium size tasks. This generation opens a new door towards agent-managing-agents chain of thoughts data. I think we would only get multi-agents with high reliability sometimes by the mid to end of 2026, assuming no major geopolitical disruption.
1. We discuss every question with opus, and we ask for second opinion from codex (just a skill that teaches claude how to call codex) where even I'm not sure what's the right approach 2. When context window reaches ~120k tokens, I ask opus to update the relevant spec files. 3. Repeat until all 3 of us - me, opus and codex are happy or are starting to discuss nitpicks, YAGNIs. Whichever earlier.
Then it's fully autonomous until all agents are happy.
Which is why I'm exploring optimization strategies. Based on the analysis of where most of the tokens are spent for my workflow, roughly 40% of it is thinking tokens with "hmm not sure, maybe..", 30% is code files.
So two approaches: 1. Have a cheap supervisor agent that detects that claude is unsure about something (which means spec gap) and alerts me so that I can step in 2. "Oracle" agent that keeps relevant parts of codebase in context and can answer questions from builder agents.
And also delegating some work to cheaper models like GLM where top performance isn't necessary.
You'll notice that as soon as you reach a setup you like that actually works, $200 subscription quotas will become a limiting factor.
I also kinda expect that one of the saner parts of agentic development is the skills system, that skills can be completely deterministic, and that after the Trough of Disillusionment people will be using skills a lot more and AI a lot less.
So it's spec (human in the loop) > plan > build. Then it cycles autonomously in plan > build until spec goals are achieved. This orchestration is all managed by a simple shell script.
But even with the implementation plan file, a new agent has to orient itself, load files it may later decide were irrelevant, the plan may have not been completely correct, there could have been gaps, initial assumptions may not hold, etc. It then starts eating tokens.
And it feels like this can be optimized further.
And yes on deterministic tooling as well.
Most of what I'm seeing is AI influencers promoting their shovels.
The jury is still very far out on how agentic development affects mid/long term speed and quality. Those feedback cycles are measured in years, not weeks. If we bother to measure at all.
People in our field generally don't do what they know works, because by and large, nobody really knows, beyond personal experiences, and I guess a critical mass doesn't even really care. We do what we believe works. Programming is a pop culture.
Where does one get started?
How do you manage multiple agents working in parallel on a single project? Surely not the same working directory tree, right? Copies? Different branches / PRs?
You can't use your Claude Code login and have to pay API prices, right? How expensive does it get?
Set an env var and ask to create a team. If you're running in tmux it will take over the session and spawn multiple agents all coordinated through a "manager" agent. Recommend running it sandboxed with skip-dangerous-permissions otherwise it's endless approvals
Churns through tokens extremely quickly, so be mindful of your plan/budget.
1. https://code.claude.com/docs/en/agent-teams
Obv, work on things that don't affect each other, otherwise you'll be asking them to look across PRs and that's messy.
Now these things are being made. I can justify spending 5-10 minutes on something without being upset if AI can't solve the problem yet.
And if not, I'll try again in 6 months. These aren't time sensitive problems to begin with or they wouldn't be rotting on the back burner in the first place.
We have 500+ custom rules that are context sensitive because I work on a large and performance sensitive C++ codebase with cooperative multitasking. Many things that are good are non-intuitive and commercial code review tools don't get 100% coverage of the rules. This took a lot of senior engineering time to review.
Anyways, I set up a massive parallel agent infrastructure in CI that chunks the review guidelines into tickets, adds to a queue, and has agents spit up GitHub code review comments. Then a manager agent validates the comments/suggestions using scripts and posts the review. Since these are coding agents they can autonomously gather context or run code to validate their suggestions.
Instantly reduced mean time to merge by 20% in an A/B test. Assuming 50% of time on review, my org would've needed 285 more review hours a week for the same effect. Super high signal as well, it catches far more than any human can and never gets tired.
Likewise, we can scale this to any arbitrary review task, so I'm looking at adding benchmarking and performance tuning suggestions for menial profiling tasks like "what data structure should I use".
Overall effort was a few days of agentic vibe-coding over a period of about 3 weeks. Would have been faster, but the parallel agents burn though tokens extremely quickly and hit Max plan limits in under an hour.
1. https://github.com/ecliptik/fluxland
Obviously no users will see a benefit directly but I reckon it'll speed up delivery of code a lot.
The long tail of deployable software always strikes at some point, and monetization is not the first thing I think of when I look at my personal backlog.
I also am a tmux+claude enjoyer, highly recommended.
Trying workmux with claude. Really cool combo
I actually had a manager once who would say Done-Done-Done. He’s clearly seen some shit too.
They built the popular compound-engineering plugin and have shipped a set of production grade consumer apps. They offer a monthly subscription and keep adding to that subscription by shipping more tools.
There is a component to this that keeps a lot of the software being built with these tools underground: There are a lot of very vocal people who are quick with downvotes and criticisms about things that have been built with the AI tooling, which wouldn't have been applied to the same result (or even poorer result) if generated by human.
This is largely why I haven't released one of the tools I've built for internal use: an easy status dashboard for operations people.
Things I've done with agent teams: Added a first-class ZFS backend to ganeti, rebuilt our "icebreaker" app that we use internally (largely to add special effects and make it more fun), built a "filesystem swiss army knife" for Ansible, converted a Lambda function that does image manipulation and watermarking from Pillow to pyvips, also had it build versions of it in go, rust, and zig for comparison sake, build tooling for regenerating our cache of watermarked images using new branding, have it connect to a pair of MS SQL test servers and identify why logshipping was broken between them, build an Ansible playbook to deploy a new AWS account, make a web app that does a simple video poker app (demo to show the local users group, someone there was asking how to get started with AI), having it brainstorm and build 3 versions of a crossword-themed daily puzzle (just to see what it'd come up with, my wife and I are enjoying TiledWords and I wanted to see what AI would come up with).
Those are the most memorable things I've used the agent teams to build in the last 3 weeks. Many of those things are internal tools or just toys, as another reply said. Some of those are publicly released or in progress for release. Most of these are in addition to my normal work, rather than as a part of it.
https://git.ceux.org/cashflow.git/