Both are autonomous AI. They differ in who defines them, how broad their remit is, and what you do with their output.
- ›What each one is for
- ›Why managers work out of the box and agents need tuning
- ›How they divide labor
- ›Which to start with
AI Managers: standing supervision
An AI manager watches an entire operating area continuously: product, delivery, accounts, or the company as a whole. It ships pre-built, because the concerns are the same at every company. Sprints slip. Accounts go quiet. Requirements sit without owners. Goals drift from the work.
You do not build a manager; you configure how much authority it has and how often it reports. Its output is findings and proposals about its domain: "velocity is 20% under pace and here are the two items causing it", "this account's health dropped after two missed check-ins".
AI Agents: defined workers
An agent is one you create for a job specific to your company. It has instructions you wrote, tools you granted, and a trigger you chose. "Every Friday, summarize deals that changed stage and flag any that skipped validation." No vendor ships that, because it encodes how you work.
Agents need tuning precisely because they are specific. Expect to iterate on the instructions for a couple of weeks before an agent is genuinely good.
The division of labor
| AI Manager | AI Agent | |
|---|---|---|
| Scope | An entire domain | One defined job |
| Origin | Built in | You create or install it |
| Setup | Configure authority and cadence | Write instructions, grant tools |
| Output | Findings and proposals about the domain | The specific work product |
| When to use | Standing concerns every company has | Workflows unique to you |
The rule that keeps them from overlapping: if a manager already watches it, do not build an agent for it. Tune the manager's autonomy instead. Duplicating supervision produces two sets of proposals about the same problem, which trains people to ignore both.
Where the Fleet fits
Neither is the run log. In Brainis, the Cortex Fleet records every run by every agent, manager, and scheduled play, with step-by-step replay. It is how you answer "what has the AI been doing" without asking each worker separately.
Tip: Start with managers. They work immediately, they cover the concerns you already worry about, and watching their proposals for a fortnight teaches you what a custom agent would need to do differently.
FAQ
Can a manager and an agent conflict?
They can propose overlapping actions if you duplicate scope, which is the argument for the rule above. Authority contracts prevent either from exceeding its bounds regardless.
Do managers cost credits too?
Yes, per run, like any AI work. Their cadence is configurable, which is your main cost control.
Which should a small team use?
Managers plus the Employee Copilot, and no custom agents for the first month. Small teams get more from supervision than from bespoke automation. See AI agents for business.
Brainis ships AI managers per operating area and an agent builder for everything else, both under one governance model. See the AI layer.
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