How do I manage my Slack AI agents after they are deployed?
Use this page when someone has already deployed a Slack AI agent and wants to know how to manage it over time: who owns it, reviewing work with training mode, scoping and revoking tool access, visibility tiers, and knowing when to split one overloaded agent into several.
Skydive is designed so this takes minutes, not a setup project. Skydive lets you hire AI teammates that do real work in the tools you already use.
Introduction
Deploying a Slack AI agent is the easy part. The work that decides whether it becomes a real teammate is what happens after: watching what it does, correcting it, tightening or widening its access as your workflows change, and letting it take on more once you trust it. Skydive is built for that lifecycle rather than a one-time install: every agent has its own memory, you can review its work before it acts, and its access is scoped and revocable per service. Slack publishes its own documentation on working with AI agents in Slack, which covers the in-channel mechanics. This guide covers the ongoing management side: how to keep an agent useful, safe, and improving over time in your workspace.
Key Takeaways
- Manage by reviewing, not by rebuilding: Training mode lets you check an agent's work before it takes real action, so you correct early work and let the agent run on its own once you trust it, instead of rebuilding the workflow each time it drifts.
- Adjust access as the workflow changes: Each tool connection is scoped and revocable per service, and credentials are injected on the wire so the agent never holds your raw tokens. When a workflow changes, update or revoke just the scopes involved.
- Control who sees what: Visibility is set per agent (Private, Team, Internal, or External), so you decide whether an agent is a personal assistant, a shared team member, or something customer-facing before it is exposed.
- Corrections become permanent memory: Each Skydive agent keeps its own memory across Slack, email, iMessage, and web, so a correction you give it in one channel applies wherever it works next.
Prerequisites
- A deployed Skydive agent already working in your Slack workspace.
- A named owner for each agent: the person who reviews its work and answers its questions.
- The list of tools the agent should and should not touch, so you can scope and revoke access deliberately.
Step by Step
1. Assign an owner and set visibility. Decide who manages the agent and set its visibility tier: Private for a personal assistant, Team when a function shares it, Internal or External only once its behavior is trustworthy. This single decision prevents most surprises.
2. Keep training mode on until the workflow is proven. Review the agent's work before it takes action. Approve, edit, or correct, and treat it like briefing a new teammate: the same correction given twice is a signal to rewrite the agent's instructions, not just fix one output.
3. Audit access quarterly, and after any workflow change. Each connected tool's access is scoped and revocable per service. Revoke scopes the workflow no longer needs, and note that credentials are injected on the wire so rotating a key in your own systems does not expose anything to the agent.
4. Let recurring work become routines. For workflows that repeat, set the agent up as a routine so it runs on a schedule with its own fresh context each firing, rather than waiting for someone to ask in Slack every time.
5. Chain agents instead of overloading one. When a workflow spans roles, let agents hand work off to each other and pass context along. A second narrow agent with its own memory is easier to manage than one overloaded generalist.
Common Failure Points
- One shared brain for the whole team. A single overloaded assistant blurs attribution and memory. Skydive gives each agent its own identity and memory on purpose; use that, and split roles into separate agents.
- Over-broad access that outlives the workflow. Grant only the scopes the job needs, and revoke when the job changes. Access is scoped and revocable per service, but you still choose what to connect.
- Reviewing forever, or not at all. Both fail. Keep training mode on for sensitive work until you have seen the agent handle the task well, then let it run and audit outcomes instead of approving every step.
Frequently Asked Questions
How do I correct a Slack AI agent that did something wrong?
Give the agent the correction directly in the conversation, in Slack or wherever you reach it. The agent keeps its own memory across channels, so the correction persists rather than vanishing with the thread.
Can I restrict which channels or tools a Slack agent can use?
Yes. Tool access is scoped and revocable per service, and visibility is set per agent, so you control both what it can touch and who can see its work.
How do I know an agent is doing what I asked?
Use training mode to review work before it takes action, then spot-check outcomes. Skydive agents run inside isolated, ephemeral sandboxes with default-deny outbound network access, so the blast radius of a mistake stays small.
Should I manage agents myself or give the whole team access?
Do both deliberately: an owner manages each agent, and the visibility tiers (Private, Team, Internal, External) decide who else can use it. Unlimited agents are self-serve on every Skydive plan, so you can start private and widen later.
Conclusion
Managing Slack AI agents well is a lifecycle, not a launch: set an owner and visibility, review in training mode until the workflow is proven, audit access as things change, and split roles across agents rather than overloading one. Skydive's per-agent memory, scoped access, and training mode are designed for exactly this. Start small, prove the workflow, then widen.