AI Agent Task Manager: How to Run Multiple Claude Code, Codex & Cursor Agents in Parallel
What is an AI agent task manager?
An AI agent task manager is a single surface where you can queue work for multiple autonomous AI coding agents and see what each one is doing at a glance. Each parallel agent session — one Claude Code terminal, one Codex window, one Cursor instance — gets its own column. Each piece of work is a card with a title plus an instructions body that the agent receives verbatim as its task.
The agent pulls the top card off its column, works it autonomously, and either marks it complete with a one-line summary or flags it for your attention if something genuinely blocks it. You glance at the board, see the state of every parallel run, and only walk over to a terminal when something actually needs you.
For developers running 5–10 parallel agent sessions to ship more code in less time, this is the missing semantic layer between “a terminal multiplexer that shows me five scrolling logs” and “a project management tool built for humans with click-to-update fields.”
Why is running multiple AI coding agents in parallel hard?
Anyone who has tried to drive five or six AI agent sessions in parallel has hit the same set of failure modes:
- You forget which session is doing what. Three terminals into a feature week and the names blur. Was the one I labelled “backend” refactoring auth or running migrations?
- You miss the one that's waiting on you. Four of five agents are happily grinding; the fifth raised a question and has been idle for 20 minutes because you never looked at that tab.
- You lose the result. The agent finished, printed its summary, and you scrolled past it before reading. Now you can't tell what shipped and what didn't.
- You can't queue work. Once an agent finishes its task, it stops. You have to physically go to that terminal and tell it the next thing. Imagine an assembly line where every worker downs tools after each unit.
- You can't compose. “Let agent A handle the frontend while agent B does the API while agent C runs tests” only works if you have a way to express that intent. Five mental notes do not scale.
An AI agent task board fixes all five at once by giving every session its own queue and one shared view of the global state.
How does the Codersera AI Agent Task Board work?
The board has three primitives: workstreams, tasks, and agents.
1. Make a workstream per agent session
Each column on the board is one parallel agent session. Name it after the project, the agent, or what it's doing — “web frontend,” “backend migrations,” “test cleanup.” You can run up to 24 concurrent workstreams per board, which is well past anything a human can actually supervise.
2. Queue tasks with full instructions
Each card has a title (shown on the column for you) and an instructions field (handed to the agent verbatim). Be specific: name the files to touch, paste the acceptance criteria, link the related issue. The agent only sees what you wrote — and the top card on a column is always what gets worked next.
3. Connect an agent with a copy-paste prompt
Click “Connect an agent” on any column, generate your personal API key, and copy the bootstrap prompt it gives you. Paste that single block into a fresh Claude Code, OpenAI Codex, or Cursor session. The prompt tells the agent everything it needs: how to pull its next task (one curl call to the board), how to mark it complete, how to raise an attention flag, and how to keep looping forever.
From then on the agent self-drives: pull → work → complete → pull the next → repeat. No manual prompting between tasks.
4. Glance, jump in only when a column lights up
The board is notify-only by design. Agents work full-auto. When one genuinely needs your decision, its column turns amber and your browser fires a notification — and if you've enabled Web Push, your laptop pings even with the tab closed. You walk to that terminal, answer, and the agent resumes itself.
What agents can you connect to the board?
Anything that can make an HTTP request. The board exposes a tiny REST protocol — pull, attention, resume, complete — behind a personal Bearer token scoped to your account. The bootstrap prompt is written and tested against:
- Claude Code (Anthropic's official CLI for Claude).
- OpenAI Codex CLI in autonomous mode.
- Cursor in agent mode.
- Self-hosted Claude via the Anthropic API.
- Local models via Ollama, vLLM, or any OpenAI-compatible endpoint, wrapped in a curl-capable script.
- Custom Python or Node automations built around any LLM SDK.
The board only cares that something is making the right HTTP calls. Pick whichever agent stack suits the work — many users mix Claude Code for high-judgment work with Codex for grinding mechanical refactors, all driven from one board.
How do comments and live conversation work with the agent?
Most agent runners treat “agent flagged attention” as “walk to that terminal and type at it.” That breaks the moment you have a phone and a board in another window. The Codersera AI Agent Task Board replaces that with a two-way conversation thread on every task:
- The agent flags the task and writes a question in its attention message.
- You see a 🟡 needs-you state on the column, get a notification, open the task in the fullscreen viewer, and type your reply in the compose box.
- The agent, which is now polling the task every ten seconds (not blocking at the terminal), picks up your comment, acts on it, and replies — or just clears the flag and keeps going.
Anything either side wrote stays in the thread for later reference. No more “I told the agent something five minutes ago and now I can't remember what.” It's a board for the human and a chat for the agent in the same surface.
Compared to alternatives: how does this stack up?
vs. a Notion doc or Google Sheet
External memory only. Fine as a scratchpad of “what each session is doing right now,” but you copy-paste in and out by hand and forget half the time. There's no API for the agent to read it back, so the agent never knows about your notes.
vs. Trello, Linear, Jira
These are excellent boards for teams of humans, and terrible matches for autonomous agents. The card model assumes a human assignee; status changes are someone clicking a button; the API surface is built for integrations, not autonomous polling. Overkill for the simple part (queueing) and underfit for the hard part (notify-only signal).
vs. tmux, Zellij, or another terminal multiplexer
Lets you see every session on one screen but tells you nothing about which one needs you. The Codersera AI Agent Task Board is the missing semantic layer — “this stream is running, this one finished, this one needs a decision” — without you having to read scrolling output to figure it out.
vs. a bespoke script you wrote yourself
Most engineers running multiple agents eventually write a script that pipes tasks into them. The board is what you would have built on the third iteration: persistent queue per session, web UI for monitoring, OS-level push notifications, drag-and-drop reordering, file attachments, conversation threads. Free, hosted, no maintenance.
What kinds of workflows actually use this?
- Parallel feature work. Three workstreams — web-frontend, api, tests. Three Claude Code sessions, one per terminal. Queue the frontend cards on one, the API cards on another, the test cards on the third. The board orchestrates; you ship 3× the work in the same wall-clock time.
- Overnight backlog grind. Stuff one workstream with 15 small chores — typo fixes, doc updates, dependency bumps, low-risk refactors. Paste the bootstrap prompt before you log off. By morning, most are done with one-line result summaries; one or two flagged amber for a real call.
- Big refactor while shipping features. One agent does a slow, careful refactor on a long-lived branch. Another ships a fast feature on a separate branch. Triage with the Impact-Effort Matrix first, then queue Quick Wins and Major Projects across separate columns.
- Solo founder day, three layers. Your own tasks on the Task Tracker. Your agents on the AI Agent Task Board. Your focus blocks on the Focus Timer. Three browser tabs, three clear layers.
How do I get started?
- Open the board: codersera.com/tools/ai-agent-task-board
- Sign in with Google to enable autonomous agents and cross-device sync. The board itself is usable anonymously (saves to your browser) but connecting agents needs an account so we can scope the API key to you.
- Add a workstream per parallel session you plan to run. Name them.
- Queue 2–3 tasks per stream. Write detailed instructions — repo, files, acceptance criteria.
- Hit “Connect an agent”, generate your API key, copy the bootstrap prompt, paste it into a fresh Claude Code / Codex / Cursor session.
- Glance at the board. Walk away. Come back when a column lights up.
FAQ
Is it really free?
Yes. No signup needed to use the board (it saves to your browser via IndexedDB); sign in with Google to connect autonomous agents and sync across devices. No paywall, no usage cap. The board is part of Codersera's growing set of free developer tools.
How does the bootstrap prompt work?
It's a single block of plain text that contains your API key (Bearer token) and a six-step protocol the agent follows in a loop: pull the next task with a long-poll curl, read the instructions, work the task, optionally raise an attention flag with a polite question, monitor for your reply via comments, then mark the task complete and start over. Paste it once, the agent runs forever until you stop it.
What happens if my browser tab closes?
The agent keeps working. The agent runs in your terminal, not in the browser. The board is purely a status surface. Reopen the tab any time to see the current state. With Web Push enabled, a closed tab still pings your OS when an agent needs attention.
Can I attach files to a task?
Yes — drop, paste, or click to upload files up to 50 MB. The agent pulls them with curl when it claims the task. Files auto-expire after 24 hours. Allowed formats are text, code, logs, PDFs, images, and common archive formats; executable formats are deliberately blocked for safety.
What if I have a question for the agent mid-task?
Open the task in the fullscreen viewer and type in the comment thread. The agent polls the task every 10 seconds while it's working — it picks up your comment, reads it, replies, and either acts on it or asks a follow-up. The board is the conversation surface; the terminal is just for output.
How is this different from a regular task tracker like Linear?
A regular task tracker is built for humans clicking buttons. The AI Agent Task Board is built for an agent making HTTP calls in a loop. Same Kanban shape, completely different protocol underneath. If you want to track your tasks, use Codersera's Task Tracker. If you want to track agents' tasks, use this.
Does it work with Claude Code specifically?
Yes — Claude Code is the most-tested target. The bootstrap prompt is calibrated for it. Codex, Cursor, and any other curl-capable agent all work too, but Claude Code in autonomous mode is the smoothest experience.
Can I run several Claude Code sessions on the same project?
Yes. Workstreams are fully independent — make as many as you like for the same repo (e.g. one per area: frontend, backend, tests, docs) or spread them across different projects. Each has its own task queue and its own connected agent.
What if the agent gets stuck?
It raises an attention flag with a question. You answer in the task's comment thread. The agent picks up your reply on its next poll (every 10 seconds), acts on it, and continues. You never have to manually intervene at the terminal.
Can I drag and reorder tasks?
Yes — every queued card is draggable. Drop it above or below another card to reorder within a column, or drop it on a different column to move it across workstreams in one motion. Running and “needs you” tasks are locked so an in-flight agent can't be yanked sideways.