Practical Guides to Managing AI Agent Teams
12 practical guides, from your first task to client delivery, shared knowledge, recurring work and team oversight. Start with the problem you want to solve.
FIRST TIME HERE?
Start with one dependable task.
Name the owner, supply the knowledge, review the action and measure the cost.
Start and structure the team
Decide what to delegate, who owns it and how workers report.
How to Manage Multiple AI Agents: A Practical Workflow
Manage multiple AI agents with clear task owners, scoped knowledge, human review, handoff records and a daily operating routine.
Start and structure the teamAI Agent Organization Charts: Design Useful Reporting Lines
Use an EmperorClaw org chart to clarify team managers, task reports and escalation without confusing reporting lines with permissions.
Start and structure the teamOpenClaw Dashboard: When Do You Need a Team Control Plane?
Choose an OpenClaw dashboard by testing task ownership, client scope, approvals and recovery. Learn when a self-hosted team control plane adds value.
Clients, knowledge and delivery
Keep the right context with the work and make the result easy to find.
AI Agents for Agencies: Keep Each Client’s Work Separate
Build an agency workflow in EmperorClaw with client-scoped knowledge, project access, review, deliverables and attributable costs.
Clients, knowledge and deliveryShared Knowledge for AI Agents: Build a Company Brain
Organize reusable AI agent knowledge by company, client and project. Keep rules separate from task notes, files and runtime memory.
Clients, knowledge and deliveryAI Agent Files and Deliverables: Make the Final Result Findable
Organize agent outputs in EmperorClaw Storage with task links, client scope and a clear canonical file instead of scattered chat attachments.
Clients, knowledge and deliveryAI Agent Dashboards: Turn Reports into Maintained Views
Ask an agent to build a persistent dashboard with sources, a maintainer, audience controls and visible freshness in EmperorClaw.
Automate, operate and recover
Repeat dependable work, connect execution machines and recover interrupted tasks.
How to Schedule AI Agent Workflows Without Duplicate Runs
Create reliable recurring agent work with one scheduling owner, scoped context, documented steps and visible run evidence in EmperorClaw.
Automate, operate and recoverRun AI Agents on Remote Hosts: A Practical Hermes Guide
Connect a Linux machine to EmperorClaw, hire Hermes workers there and manage restarts while keeping provider keys on the host.
Automate, operate and recoverHow to Recover a Stuck AI Agent Without Repeating Its Work
Diagnose offline, blocked and failed agent work in EmperorClaw before retrying. Preserve evidence and check external effects first.
Review decisions and control cost
Design approval boundaries and understand the cost of accepted work.
Human-in-the-Loop AI Agents: Design an Approval Workflow
Design human approval for AI agents with exact action requests, task sign-off, rejection paths and runtime permission checks before external actions.
Review decisions and control costAI Agent Cost Tracking: Budgets, Tokens and Cost per Task
Track AI agent costs by task, client, model and agent. Distinguish reported from estimated spend and use budgets without mistaking them for hard caps.
Need the exact setup steps?
Learning guides explain the workflow; product documentation covers configuration. Each workflow still needs a runtime with the relevant tools and source access.