How to Manage AI Coding Agents at Scale

What "managing AI agents at scale" actually means

The phrase covers two different problems, and most of the advice written about it only answers one of them. The first is a developer or a small team running several coding agent sessions at once, maybe five, maybe twenty, and losing track of which one needs attention, which branch it is on, or which key it used. The second is a company scheduling hundreds or thousands of agent jobs through other software, where no human is watching any single session.

If you searched for this expecting the first problem, the fix is mostly about visibility and where your keys live, and the rest of this guide gets to that quickly. If you meant the second, the fix looks like ordinary platform engineering: a job scheduler, short-lived credentials per job, and logging, with an AI agent sitting where a script used to sit.

Knowing which one you have matters because the tools do not transfer. A desktop app that shows you five terminals will not schedule a thousand CI jobs, and a Kubernetes job queue is a strange way to babysit the one agent you are pairing with right now.

• Developer or small-team scale: a handful to a few dozen sessions you run and watch yourself.
• Enterprise fleet scale: hundreds or thousands of agent jobs scheduled by other software, with no one watching any single run.
• The rest of this page answers both, in separate sections, because they need different tools.

Managing Claude Code and other agents in the cloud

Claude Code's model always runs in the cloud. You can point it at the Anthropic API directly, or at Amazon Bedrock or Google Vertex AI if your company wants the usage billed and governed inside its own AWS or GCP account. Switching between them changes authentication and billing, not where your code lives.

Where your code runs is a separate choice. You can run the agent in a terminal on your own machine, or hand it a cloud sandbox built for exactly this: E2B spins up a Firecracker microVM per session with a dedicated kernel and cold starts under 200 milliseconds, aimed at short, disposable agent runs; Daytona uses Docker containers and keeps a workspace alive across sessions, aimed at an agent that needs to pick up where it left off. Both let you run many sessions in parallel without tying up your laptop, at the cost of paying for and operating that infrastructure.

A search like "manage AI agents Claude cloud" is usually asking about both of these at once. You can mix them freely: a cloud-hosted model with local execution, or the reverse, depending on where you want the code and where you want the bill.

• Model host: Anthropic API, Amazon Bedrock, or Google Vertex AI. This changes billing and IAM, not code location.
• Execution host: your own machine, or a cloud sandbox such as E2B (ephemeral, Firecracker microVMs) or Daytona (persistent, Docker-based).
• These two choices are independent. Pick each on its own merits.

Cloud fleet or your own machine: the real trade-off

If you are the enterprise-fleet case, look at what your company already uses to schedule work, because an agent job is still a job. Kubernetes Jobs, self-hosted GitHub Actions runners, or a workflow engine such as Temporal can all run an agent the same way they run any other task, and each job should get its own short-lived credential rather than one key shared across every run.

If you are the developer case, running offline usually means a terminal on your own machine against your own checkout. The code and anything a command touches stay on hardware you control, and you are not sending a copy of your repository into someone else's sandbox. The cost is that you are limited to whatever parallelism your own machine can handle.

Neither option is automatically safer. A cloud sandbox that is destroyed the moment a session ends can be a smaller risk than a laptop that has had three agents running unattended for a week with a key still loaded from Monday.

• Enterprise fleet: reuse your existing job scheduler, with one short-lived credential per job.
• Developer offline: a local terminal keeps code and secrets on your own hardware, limited by your own machine's capacity.
• A cloud sandbox torn down after every run can beat a laptop left running agents for a week.

Keeping keys safe as you add more agents

The more agent sessions you run, the more chances a plaintext key has to leak, because an agent reads a .env file the same way on its first run of the day and its fiftieth. A setup that was fine for one agent becomes a real exposure once you are running a dozen.

At developer scale, the practical options are the macOS Keychain for a single machine, or a lightweight secrets tool such as 1Password CLI or Doppler if a small team needs to share keys without emailing them around. At fleet scale, the standard choices are HashiCorp Vault, AWS Secrets Manager, or Google Secret Manager, which can hand a job a credential tied to its own service account instead of a long-lived key baked into an image. HashiCorp Vault's Kubernetes auth method, for example, lets a pod authenticate with its own service account token, so nobody has to bootstrap it with a separate secret.

The pattern is identical at either scale: the agent or the job asks for a secret by name, the vault hands the value to the one command that needs it, and the value never sits in a file, an image, or a prompt.

• Developer scale: macOS Keychain, or 1Password CLI / Doppler for a small team.
• Fleet scale: HashiCorp Vault, AWS Secrets Manager, or Google Secret Manager, with per-job credentials.
• Either way: inject the value into the one command that needs it, never into a file an agent can read.

What live oversight requires once more than one agent is running

Past two or three sessions, you cannot watch every terminal yourself, so what you actually need is a signal that tells you which agent needs you right now, not a transcript of everything every agent typed. A dashboard that just mirrors all the output is more noise than oversight.

That signal is more trustworthy when it comes from something observable, such as CPU use and how recently a process produced output, rather than from the model reporting its own status, because a model can describe itself as working while it is stuck in a loop.

Oversight also means a hard stop before anything irreversible. Whatever tells you an agent is ready, a human should still be the one who accepts a diff, approves a deploy, or confirms a destructive database change, no matter how many agents are running at once.

• A useful signal flags the session that needs you, not a firehose of every agent's output.
• Base the signal on observable activity, not on the model's own report of its status.
• Keep a human approval step before anything irreversible, at any number of agents.

How Forkbench fits, and where it does not

Forkbench is a desktop app for the Mac that runs your coding agents in real terminals. Each tab has a live pulse that quickens with CPU use and output rate, never a token count, so you can tell at a glance which of several sessions is actually working and which has stalled. A red "needs you" flag with a count appears when an agent is blocked, so you check the one that needs you instead of scanning every tab.

Its Vault keeps secrets in your Keychain and lets a command use one by name, so the value never reaches the prompt, the command line, or the transcript. That protection covers what you put in the Vault specifically: an unpinned key can still be read by the program it was handed to once that program runs, and anything still sitting in a plain .env file in the project folder stays just as readable to an agent as it always was. A Thread can also be locked to its folders with the macOS kernel sandbox, so ~/.ssh and your other repositories stay shut; that lock is opt-in and it does not restrict the network.

Be clear about which scale this is built for. Forkbench runs on the Mac or Macs you own, not as a cloud scheduler for hundreds of jobs with no human watching. If you are the enterprise-fleet case from the first section, Forkbench is not that tool, and the right answer there is the job scheduler and secrets manager your company already runs. If you are a developer or small team running several agents in parallel and want to see all of it, with its keys and its boundaries, in one window, that is exactly the case Forkbench is built for.

• Pulse: a live signal from CPU and output, not a token meter, so you can tell stalled from working.
• Vault: keys stay in the Keychain; a program a key was handed to can still read it once it runs.
• Folder lock: opt-in, does not restrict the network, and Forkbench is not a fleet scheduler for a thousand cloud jobs.

A setup you can put in place this week

You do not need to pick between every option above on day one. Start with whichever scale you actually have, fix the keys first, then add oversight.

The order matters because keys left in a .env file are the exposure that is already live, while a better dashboard only helps you see a problem you have already removed the worst case of.

• Decide which scale you have: a handful of sessions you watch, or a fleet scheduled by other software.
• Move every key out of .env files and into the Keychain, a team secrets tool, or your cloud secrets manager.
• Give each job or session the narrowest credential it needs, not one shared key.
• Add a signal based on activity, not self-reported status, so you know which session needs you.
• Keep a human approval step before any deploy, merge, or destructive command.
• Re-check the setup once you double the number of agents running; what worked at five rarely works unchanged at fifty.