Live Oversight: How to Manage Multiple AI Agents Without Losing Track

Two different things people mean by production here

Searches for managing multiple AI agents in production split into two genuinely different problems, and the tools that solve one do nothing for the other.

The first is enterprise agent observability: you've shipped an agentic system to real users, and you need to trace what it did, measure its cost and latency, and catch failures after the fact. LangSmith, built by LangChain, and Datadog's LLM Observability both do this: tracing every model call and tool interaction, tracking token spend, and correlating agent behavior with the rest of your backend.

The second is live developer oversight: you're running several coding agents right now, on your own machine, working on real tasks, and you need to know what each one is doing without tabbing through every terminal. This page is about that second problem.

• Enterprise observability, post-deploy: LangSmith, Datadog LLM Observability, tracing calls and cost over time.
• Live developer oversight, right now: knowing which of several running agents needs you, this page's subject.
• Different tools, different jobs. Neither replaces the other.

What goes wrong without it

Run three or four coding agents at once and the failure mode is always the same shape. One of them finishes a step and sits there waiting for an answer you haven't noticed yet. Two of them end up touching the same file because you forgot which task you'd handed to which pane. You end up tabbing through every terminal just to find the one thing that actually needs you, which defeats the point of running several agents at once.

The fix isn't fewer agents. It's a signal that tells you, across all of them at a glance, which one is actually working, which one is idle, and which one is stuck.

• An agent finishes a step and waits, unnoticed, for an answer.
• Two agents collide on the same file because the task-to-pane mapping got lost.
• Checking every tab to find the one that needs you defeats the point of running several.

What a live signal actually needs to measure

A useful live signal for a coding agent should track whether it's actually working, not how talkative it's been. CPU usage and the rate of new output are a reasonable proxy for busy versus idle; token count is not, because an agent can sit quietly burning tokens on a long single call, or produce a burst of tokens and then go idle waiting on you, and a token counter can't tell those two states apart.

The second thing it needs is a way to interrupt you on purpose. A quiet dashboard you have to go check is barely better than no dashboard; the useful version flags the agent that's actually blocked, with a count, so you look there first.

• Busy vs. idle: CPU and output rate are a better signal than token count.
• A blocked-agent flag, with a count, beats a dashboard you have to go check yourself.

How Forkbench's pulse does this, and what it doesn't measure

Forkbench gives each terminal tab a live pulse that quickens as its agent works harder, driven by CPU usage and output rate. It's explicitly not a token meter; two agents burning the same number of tokens can show very different pulses depending on whether they're actually computing or sitting idle between calls.

When an agent is blocked, a red needs you flag appears with a count, so across a row of tabs you can tell at a glance which one is actually waiting on a decision rather than quietly working. The claimed task sits beside each terminal, so you're not relying on memory for which task belongs to which pane, and the git panel follows whichever tab you're looking at.

None of this is a token meter, a cost tracker, or a trace of what the model actually said at each step. If you need that level of detail after the fact, that's the enterprise observability job from the first section, not this one.

• Pulse: driven by CPU and output rate, explicitly not a token meter.
• Red needs you flag, with a count, for an agent that's actually blocked.
• The claimed task sits beside its terminal; the git panel follows the active tab.

The vault half: a shared secret across several agents

Running several agents at once usually means several of them need the same credential, an API key for a service all your tasks touch. Forkbench's Vault keeps that key in your Mac's Keychain once, and any Thread's agent can use it by name without the value ever reaching the prompt, the command line, or the transcript.

The caveat is the same one that applies everywhere this kind of vault shows up: an unpinned key can still be read by the program that was run with it. The Vault stops the key from sitting in a file an agent can open or from entering its context. It does not stop a command the agent is allowed to run from reading the value it was handed.

• One Vault entry, usable by name from any Thread, instead of copying a key into each agent's context.
• Limit: an unpinned key is still readable by the program it was given to.

What this covers, honestly, and what it doesn't

Today, the live pulse, the blocked-agent flag, and the per-agent Vault access are all free on one Mac, along with unlimited Threads and agents. That covers a single developer running several agents on one machine, which answers the mac manage multiple ai agents and manage multiple ai agents for developers case directly.

Taking this across Macs, or to your phone, is a Pro feature: keys and the squad setting reach every Mac you own, and Talk lets you check in from your phone. Talk's text passes through Forkbench's servers and is not end-to-end encrypted, which matters if enterprise in your search means a compliance requirement.

For an actual team that has to prove what its agents did, the Team plan adds enrolment, an org-wide access log, and its CSV export. That's a real answer to manage multiple ai agents enterprise, but it's an access log, not a full observability platform with traces and cost dashboards; if that's what you need, go back to the first section.

Windows and Linux support is in development, with no date yet, so a search for a desktop manage multiple ai agents system only has a real answer on a Mac today.

• Free, one Mac: unlimited Threads and agents, the pulse, the blocked flag, the Vault.
• Pro: the same signal across every Mac you own, plus Talk from your phone, not end-to-end encrypted.
• Team: enrolment and an org-wide access log, for proving what agents did, not a full observability suite.
• Windows and Linux: in development, no date yet.

Setting it up, in practice

Open one Thread per task, not one Thread per agent type. That's what makes the claimed task next to each terminal mean something: you can glance at a tab and know what it's for, instead of remembering which pane was doing which job.

Watch the row of pulses instead of any single terminal. Let the red flag pull your attention, rather than checking in on a schedule. If you need someone else to see one piece of this without handing over the whole machine, a Live Link opens just that Thread to someone you invite, not your other work.

• One Thread per task, so the claimed task label actually tells you something.
• Watch the pulses, not the panes; let the blocked flag pull your attention.
• Share a single Thread with Live Link instead of your whole machine.