An Agent Multiplexer is NOT all you need
We've all encountered this issue while working with agents. Lots of ideas, lots of sessions. And eventually, it all breaks down. So we introduce numerous md files that the agents can reference and extend. This works for a while but breaks down as it's still too clunky and takes up more and more attention and agent chats extend further and further into the evening. Introducing an agent multiplexer into the picture allows you to manage more sessions so you can do more work in parallel. Adding a real issue tracker makes this a pretty slick setup! You now have your swarm of agents taking up tasks, and you think you are making great progress! But your sessions still extend later and later. Sometimes you wonder if you're just burning more tokens for a marginal increase in actual output, and you still have trouble tracking project progress.
You're probably operating like this if you're using one of these multiplexers:
- Herdr: see at a glance which agents are working, done, or waiting on you, and keep sessions running when you close the app.
- amux: run parallel agents from one shared board, where agents claim tasks so two never take the same one.
- Conductor: run parallel Claude Code, Codex, or Cursor agents on your Mac, then review and merge their changes.
- Paradev (formerly Superset): an open-source workspace to "run an army of coding agents", each on its own branch, with review and merge in one window.
...and one of these issue trackers:
- Jira: plan and track work across teams, and assign tasks to coding agents like Claude, Cursor, or Codex.
- Linear: "the product development system for teams and agents", with roadmaps, prioritized issues, and issues you can delegate to an agent.
- GitHub Issues: issues next to your code, with "blocked by" links between them.
- Beads: a graph issue tracker for AI agents, powered by Dolt, that gives your coding agents a persistent memory and finds the tasks that are ready to work on. You've probably only heard of this if you've dug deep into running swarms of agents.
This setup allows scaling up the number of agents you can handle, but you still hit the fundamental limit of time and attention as your agents ask for input to unblock themselves, make puzzling implementation decisions, or decide to take up unrelated tasks on priority. You need a tree of issues to properly hand out tasks. You need a way to focus your attention onto fewer of the actual agents. You need to scale your review process and separate the feature goals and architecture from the mechanics of the review. All of these issues motivated us to build MaximizePM, which is free and open source on GitHub.
What do you get with MaximizePM?
- Multi-project support: Turn your ideas into actionable tasks as they come in and parallelize the tasks across many focused agents
- Manager Agent: Unblock agents, determine if blockers need your attention, create new tasks, projects, and goals, and coordinate the entire system without attention overload
- Project-level prioritization: Create, prioritize, and link tasks to optimize efficiency and increase output without burning tokens on redundant or unnecessary work
- Code review and release: Establish the goals with the Manager Agent and discuss escalated deviations, then agents review the code for bugs and security issues and ship the finished work according to your release policy
- View and track work: Understand the dependencies between tasks visually, and track completed work
Using MaximizePM to build my projects has cut down on excess token usage due to agents no longer taking conflicting work. When I analyzed my agent usage over the last month, 86% of the messages I typed went to the Manager Agent, leading to less agent switching and less attention fatigue. The agents that are running are picking the right task and following the dependencies, reducing conflicting work.
I think you will be surprised and happy with MaximizePM's approach to giving you your time and attention back, and you will definitely not be switching between agent windows as much.