Maxxwell by Rindler
Writing

Herded Attention Across Many Coding Agents

2026-10-24

Herded attention is the practice of directing a developer’s focus across many AI coding agents using explicit, shared signals in one view, so the human only.

Herded attention is the practice of directing a developer’s focus across many AI coding agents using explicit, shared signals in one view, so the human only looks at sessions where a real decision or intervention is needed.

How Herded Attention Works

In Maxxwell, herded attention starts with the fleet view: every agent session sits in a lane with a readable state label like working, idle, waiting on you, blocked, needs sign-in, done, dead, not heard from, or not started.

These states are derived from real signals - process status, tool calls, timer thresholds - not guesses. When the app cannot confirm what a session is doing, it says not heard from instead of pretending it is still working.

On top of the lanes sits the orchestrator seat, which is itself a real coding-agent session created from a written brief. Instead of talking to eight terminals, you talk to the orchestrator in natural language. It reads state from all the lanes and responds with:

The core UI mechanic is simple: one window, many lanes, explicit state. You can click into any lane to attach to the underlying terminal session - Claude Code, Codex, Cursor-agent or your own harness - and take over live. Sessions outlive the app; quitting Maxxwell detaches instead of killing them.

Herded attention does not run your fleet autonomously. Controls that would change the fleet - start a new task, re-aim a worker, stop a job - are drafts rather than acts: Maxxwell writes a fully formed, unsent sentence into the composer for the orchestrator or worker, and you decide whether to send it. You stay the one who presses enter.

Why Herded Attention Matters When You Already Use Claude Code or Cursor

By 2026, multi-agent work is normal. JetBrains reports 90% of professional developers use AI coding agents at least weekly and 68% use them daily, with Claude Code at 39% global adoption and 47% in the U.S. Stack Overflow’s 2025 survey found over 50.6% of professional developers use AI tools daily, and Anthropic’s 2026 report says developers use AI in roughly 60% of their work but can fully delegate only 0-20% of tasks.

That last number is the reason herded attention matters. You are still in the loop; you just have more loops than before. Once you have three or more sessions open - say Claude Code on a backend, Cursor in the front-end repo, Codex in a throwaway sandbox - the bottleneck is not output. The bottleneck is you deciding where to look next and which question to answer.

Without an attention layer, you end up with:

just slow

Herded attention changes that by giving you a single surface where every lane carries a clear state and context pressure readout. You can scan the fleet and see, at a glance, which sessions are not heard from, which are actively working, and which are waiting on you. Maxxwell shows live estimates of context pressure with tiered warnings and a one-click compact when a lane is close to the limit.

Instead of babysitting Claude Code and Cursor one by one, you spend your time on the handful of junctions where your judgment actually matters: goal changes, trade-offs, and merges. Maxxwell does not replace those agents; it runs them as unmodified workers inside real terminal sessions, and its herded attention UI keeps your focus on the lanes that need it.

How Herded Attention Differs from Notifications

Generic notifications are a reactive, fragmented pattern: each tool pings you in its own way (toast, email, terminal log) when something happens. They do not share state across tools, they do not encode meaning beyond “an event occurred”, and they do not help you choose which interruption to handle first.

Herded attention is proactive and shared:

You see all sessions’ states at once.

You do not parse logs to realise a build failed.

The orchestrator summarizes what changed since you last spoke, instead of a stream of pings.

In Maxxwell, the orchestrator seat is the attention concentrator. When you ask “where are we?”, it does a return report across lanes: which tasks completed, which are mid-flight, which need a decision. Notifications tell you that something happened; herded attention tells you where your next decision is.

Competitors like Herd, Helmor, Addy, Conductor and Alera also tackle multi-agent coordination. Many do this with dashboards and timelines. Maxxwell’s line in the sand is the explicit, conservative state model and draft-only fleet controls - it will say not heard from instead of guessing, and nothing acts without you sending the command.

Example: Multi-Agent Refactor with Herded Attention

Take a concrete workflow: you are refactoring a legacy service into separate modules. You want API surface cleanup, test coverage, and a migration script, and you already trust Claude Code and Cursor for day-to-day work.

You start in Maxxwell with a brief to the orchestrator seat:

Refactor legacy-service into api, domain, and persistence modules.

Claude Code: plan and execute backend refactor.

Cursor: update front-end calls.

Codex: draft a migration script and sanity checks.

Keep tests passing and call out any ambiguous behaviour changes before editing.

From that brief, you have three worker lanes:

All three start in working state. You go back to your day - code review, standups, whatever. Ten minutes later, you flip back to Maxxwell. The fleet view shows:

You do not read three scrollbacks. You talk to the orchestrator seat:

Summarise the refactor status and what needs my decision.

It replies with a return report along the lines of:

No decisions needed yet.

Cursor paused and is waiting on you.

No progress since last check.

Your attention is now herded to the two real junctions: the API ambiguity and the broken sign-in. You attach to Lane B, decide how to handle getUserData, and approve or edit the orchestrator’s drafted message back to Cursor. You then fix auth for Lane C and restart the session.

At no point do you mentally track which tab is the backend or wonder whether Codex is still running. States tell you what is happening; the orchestrator tells you where you are needed. The agents you already trust - Claude Code, Cursor, Codex - do the work they are good at, and herded attention keeps your limited focus aimed at the few decisions only you can make.

That is herded attention in practice: the same agents, the same repos, but an operating layer that treats your focus as the scarce resource and routes it deliberately instead of leaving it to chance.