CodeHerder
Command your fleet of coding agents.
What problem does it solve?
Running many AI coding agents at once means blind spots — you can't see what each agent is doing, they collide on the same files, and cost sprawls across machines and repos.
Who is it for?
Engineering teams and studios operating fleets of AI coding agents across multiple machines and repositories.
Why did we build it?
We run AI coding agents on every client engagement, often several at once across different repos. Early on that meant a wall of terminal tabs, no shared view of what any agent was doing, and more than one afternoon lost to two agents editing the same file in parallel without knowing it.
The cost problem showed up just as fast. Agent time isn't free, and once a fleet is running across machines, the spend is scattered exactly like the visibility is — no single place to see what a task actually cost until the invoice landed.
CodeHerder is the tool we built to fix our own operation first: one place to see every agent, on every machine and repo, with tasking that can't collide and cost tracked per task instead of reconstructed after the fact.
How does it work?
- A shared task registry, not shared state
Agents don't need to share a filesystem or process to avoid stepping on each other — they claim tasks against a central registry, so two agents can't be handed the same file at the same time even when they're on different machines.
- Collision detection at the task level
Before an agent starts, CodeHerder checks what else is in flight against that repo and file set. A conflicting claim gets rejected up front instead of surfacing later as a merge conflict or clobbered edit.
- Per-task cost ledger
Every agent run reports its own spend back to the task it was assigned, so cost rolls up by task, repo, or machine instead of arriving as one undifferentiated provider bill.
- One dashboard across every repo and machine
The registry is the same one every agent reports to, regardless of which machine kicked it off — so the fleet view is a query, not a manual roll-up.
What's the hard part?
Collision-free tasking without shared state was the genuinely hard part. Agents run as independent processes, often on different machines, with no lock file or shared memory to coordinate through — so "don't let two agents touch the same file" has to be enforced by a central claim-and-check step that's fast enough not to slow an agent down, and consistent enough that a claim made on one machine is instantly visible to an agent starting on another.
What does it do?
- Real-time visibility into every agent across machines and repos
- Collision-free tasking so agents never clobber each other's work
- Per-task cost tracking from a single dashboard
- Coordinate agents across many repositories at once
What does this prove we can do for you?
The same discipline — a central coordination layer, collision detection, and per-unit cost accounting — is exactly what we build for clients running their own agent fleets or multi-step agentic workflows in production.
Where this shows up in client work
Questions, answered.
The questions we get asked most, answered plainly: no hedging, no marketing copy.
01 What is CodeHerder for?
Coordinating multiple AI coding agents running across different machines and repositories at once — so you can see what every agent is doing, prevent them from colliding on the same files, and track what each task costs from one dashboard.
02 Does it work with any AI coding agent?
CodeHerder coordinates at the task and repo level rather than controlling a specific agent's internals, so it's built to sit alongside whichever coding agents you're already running.
03 Do I need to change how my agents work to use it?
No — agents claim tasks against CodeHerder's registry rather than being rewritten; the coordination happens around them, not inside them.
A coordination platform for running many AI coding agents across machines and repos — real-time visibility, collision-free tasking, and per-task cost tracking from one dashboard.
This is the same discipline, real architecture decisions and an honest account of the hard part, that we bring to client engagements.