#Corporate AI

1 messages · Page 1 of 1 (latest)

hot halo Feb 17, 2026, 12:00 AM

I'd like to be able to have a corporate army of agents that are all purpose built with very specific capabilities they are EXPERTS in. We will have a traditional corporate style hierarchy with a CEO at the top and specialized operators below. The specialized operators will be the heads of their departments. The heads of their departmers can have one to many agents below them as worker agents.

Use native openclaw functionality and code where possible. The capabilities should be written into core and configurable in the openclaws settings appropriately. Honor provider limitations and requirements but make sure to document what they are.

Do thorough analysis of best practices around fo this protocol and how we can apply it to openclaw.

Should all be followed up in discord.

quick windBOT Feb 17, 2026, 12:00 AM

To help others find answers, you can mark your question as solved via Right click solution message -> Apps -> ✅ Mark Solution

hot halo Feb 17, 2026, 12:03 AM

@tidal moat

tidal moatBOT Feb 17, 2026, 12:04 AM

Hey — what do you need help with?

If this is an OpenClaw setup/troubleshooting thing, paste:

openclaw --version
what you were trying to do
the exact error + the last ~30 lines of gateway logs (or openclaw gateway status output)

If it’s more of a “corporate AI / architecture / policy” question, tell me your constraints (data residency, SSO, audit logs, allowed model providers) and what you’re aiming to ship.

hot halo Feb 17, 2026, 12:05 AM

Do thorough analysis of best practices around fo this protocol and how we can apply it to openclaw.

Should all be followed up in discord.

tidal moatBOT Feb 17, 2026, 12:09 AM

You can get pretty close to that “CEO → dept heads → workers” hierarchy using native OpenClaw primitives today — but the best practice is to treat it as an orchestration pattern (routing + tool policy + sandboxing + sub-agents), not as “100 always-on agents.” The latter gets expensive + fragile fast.

Below is a concrete protocol + how it maps onto OpenClaw, plus provider/tool limitations you’ll want to design around.

1) Map your org chart to OpenClaw concepts

A. Persistent “brains” = multi-agent routing

Use one Gateway hosting many isolated agents (agents.list[]). Each agent has its own:

workspace (files / prompts / skills)
auth profiles
session store

Docs: https://docs.openclaw.ai/concepts/multi-agent

This is how you model:

CEO agent (orchestrator)
Department head agents (sales, finance, eng, legal, ops, etc.)

B. Ephemeral “workers” = sub-agents

Use sessions_spawn to spin up background workers for research, drafting, triage, analysis, etc.

Docs: https://docs.openclaw.ai/tools/subagents

Best practice: workers are usually cheap + parallel + disposable. Dept heads integrate outputs.

C. “Capabilities” = tool policy + sandbox policy + skills

In OpenClaw, what an agent can do is enforced mainly by:

per-agent tool allow/deny
per-agent sandboxing (and workspace mount mode)
per-skill enable/disable (global config), plus which skills exist in that agent’s workspace

Docs:

Multi-agent sandbox + tools (referenced from multi-agent doc)
Security (threat model + guardrails): https://docs.openclaw.ai/gateway/security
Skills config: https://docs.openclaw.ai/tools/skills-config

If you want “capabilities” as a first-class named object (e.g. capabilities: ["read_only", "jira_writer"]) that’s a core feature proposal (totally reasonable); today you express it via the policies above.

2) Recommended hierarchy protocol (works well in practice)

CEO (Orchestrator) responsibilities

intake + clarify goals
choose which dept head(s) own the task
set constraints (budget, compliance, time)
request structured deliverables
final synthesis + decision log

Dept head responsibilities

turn CEO intent into an execution plan
spawn workers for subtasks
validate + merge worker results
escalate back to CEO with options + tradeoffs

Worker responsibilities

do one bounded task
produce output in a strict format (so it’s easy to merge)
never “go rogue” (no broad exploration, no extra tool use)

Strong pattern: force every handoff to be structured, e.g.

Task Contract (markdown template)

Objective
Inputs (links/files)
Constraints (policy/compliance, time, budget)

Allowed actions/tools
Output format (bullets/table/JSON)
Definition of done

This one change (structured handoffs) matters more than the exact org chart.

3) Best practices applying this to OpenClaw

3.1 Keep the number of persistent agents small

Make dept heads persistent; make workers sub-agents. You’ll get:

lower token burn
less context drift
less “agent coordination overhead”

OpenClaw’s own FAQ basically frames “CEO + many agents” as feasible but token-heavy; sub-agents are usually the efficient version. (See the FAQ section “one CEO and many agents”.)

3.2 Enforce least-privilege per department head

Example policy approach:

CEO: can spawn, can read/write internal docs, but no exec
Finance: no browser automation, no outbound messaging except via CEO
Ops: exec allowed but sandboxed, with strict approvals
Legal/Compliance: read-only + web_fetch/web_search off (or a “reader” mode)

This is OpenClaw-native: per-agent tools.allow/deny + per-agent sandbox.

3.3 Use sandboxing as your default safety boundary

Especially if workers touch web/email/docs. Prompt injection is real; tool gating is what prevents damage.

Docs: https://docs.openclaw.ai/gateway/security and https://docs.openclaw.ai/gateway/sandboxing

3.4 Avoid credential sharing between departments

Multi-agent mode stores auth per agent. That’s good.

Don’t reuse agentDir across agents (collisions)

If you must share creds, copy only what’s required — but for corporate separation, prefer not to.

Docs: https://docs.openclaw.ai/concepts/multi-agent

3.5 Build “department memory” as files, not chat history

Have each dept head maintain:

department-charter.md
policies.md
glossary.md
decision-log.md

Then workers read those files as needed. It’s more auditable than relying on long chat context.

4) Provider / system limitations you need to design around (important)

Sub-agents limitations (native behavior)

No nested spawning (workers can’t fan out more workers)
Announce/results are best-effort (gateway restart can drop pending announcements)
Default tool policy is restricted for sub-agents (session-management/admin tools denied)
Auth is not perfectly isolated: sub-agent auth resolves by agent id with some fallback behavior (see subagents doc)

Docs: https://docs.openclaw.ai/tools/subagents

Model/provider limitations (general guidance)

Without naming specific models: across providers you’ll hit combinations of:

rate limits / 429s → need fallback + retry strategy
tool-calling reliability differences → keep “tool-heavy” roles on tool-reliable models
context-window constraints → force summarization into files, use /new + /compact deliberately

Docs to anchor this: https://docs.openclaw.ai/concepts/models and https://docs.openclaw.ai/help/faq (rate limits section)

5) The “native OpenClaw” implementation plan (high-level)

Create agents: ceo, sales, eng, ops, etc. (multi-agent)
Give each agent:
- its own workspace (department docs + skills)
- tool allow/deny policy
- sandbox policy
Let CEO spawn sub-agents under specific dept heads (cross-agent spawning) for worker tasks
(Optional) Route different inbound channels/rooms to different agents via bindings

Docs:

Multi-agent routing: https://docs.openclaw.ai/concepts/multi-agent
Sub-agents: https://docs.openclaw.ai/tools/subagents

6) To proceed: a few context questions (so we don’t design the wrong “army”)

What’s the primary surface: Slack, Discord, Teams, email triage, something else?
Do you need hard isolation between departments (separate credentials + separate tool access), or mostly “role clarity”?
What are the allowed actions for agents: create tickets, edit docs, run code, message customers, approve spend?
Any compliance constraints (PII, SOC2, data residency, “no web browsing”)?
Rough scale: how many departments + how many “workers” concurrently?

If you want, I can turn this into a proper RFC/spec in a structured format (Problem → Goals → Architecture → Policy → Limitations → Rollout plan) and we iterate on it here in this Discord thread.

hot halo Feb 17, 2026, 12:11 AM

give me example implementations for openclaw

tidal moatBOT Feb 17, 2026, 12:14 AM

Here are a few copy/paste-able OpenClaw example implementations (all native config) for a “CEO → dept heads → workers” setup.

Docs that match these patterns:

Multi-agent routing: https://docs.openclaw.ai/concepts/multi-agent
Channel routing / session keys: https://docs.openclaw.ai/channels/channel-routing
Sub-agents: https://docs.openclaw.ai/tools/subagents
Skills config: https://docs.openclaw.ai/tools/skills-config
Security: https://docs.openclaw.ai/gateway/security

Example 1) One public interface (CEO), dept heads only via delegation

Everything routes to ceo. The CEO can spawn sub-agent runs under other agent ids (dept heads), who can then spawn cheap worker sub-agents under themselves.

~/.openclaw/openclaw.json (JSON5):

{
  agents: {
    defaults: {
      // Make workers cheap by default (pick a model you have configured)
      subagents: {
        model: "CHEAP_MODEL_ID",
        thinking: "low",
        maxConcurrent: 6,
        archiveAfterMinutes: 60,
      },
    },

    list: [
      {
        id: "ceo",

        default: true,
        name: "CEO / Orchestrator",
        workspace: "~/.openclaw/workspace-ceo",

        // CEO can delegate to dept heads:
        subagents: {
          allowAgents: ["ops", "eng", "finance", "sales", "legal"],
        },

        // CEO should usually NOT have host-level power
        sandbox: { mode: "all", scope: "agent", workspaceAccess: "ro" },
        tools: {
          deny: ["exec", "process", "browser"], // CEO is coordinator, not operator
        },
      },


      {
        id: "ops",
        name: "Head of Ops",
        workspace: "~/.openclaw/workspace-ops",
        sandbox: { mode: "all", scope: "agent", workspaceAccess: "rw" },
        tools: {
          // Ops can actually do things (tune to your comfort level)
          allow: ["read", "write", "edit", "exec", "process", "sessions_spawn"],
          deny: ["browser"], // optional
        },
      },

      {
        id: "finance",

        name: "Head of Finance",
        workspace: "~/.openclaw/workspace-finance",
        sandbox: { mode: "all", scope: "agent", workspaceAccess: "ro" },
        tools: { deny: ["exec", "process", "browser", "write", "edit"] },
      },

      {
        id: "legal",
        name: "Head of Legal",
        workspace: "~/.openclaw/workspace-legal",
        sandbox: { mode: "all", scope: "agent", workspaceAccess: "ro" },
        tools: { deny: ["exec", "process", "browser", "write", "edit"] },
      }
    ],
  },


  // Route all Discord messages in a given guild to CEO (replace with your guild id).
  bindings: [
    { agentId: "ceo", match: { channel: "discord", guildId: "YOUR_DISCORD_GUILD_ID" } },
  ],

  // Multi-user safety: avoid DM context leakage if many employees can DM the bot
  session: {
    dmScope: "per-channel-peer",
  },
}

How you use this: talk to CEO in chat, and CEO uses sub-agents to delegate to department heads and workers (per https://docs.openclaw.ai/tools/subagents).

Example 2) Route specific Discord channels to specific dept heads

This makes “#ops” go to ops head, “#finance” to finance head, etc.

{
  agents: {
    list: [
      { id: "ceo", default: true, workspace: "~/.openclaw/workspace-ceo" },
      { id: "ops", workspace: "~/.openclaw/workspace-ops" },
      { id: "finance", workspace: "~/.openclaw/workspace-finance" },
    ],
  },

  bindings: [
    // Most-specific wins: route specific channels first

    { agentId: "ops", match: { channel: "discord", peer: { kind: "channel", id: "DISCORD_CHANNEL_ID_OPS" } } },
    { agentId: "finance", match: { channel: "discord", peer: { kind: "channel", id: "DISCORD_CHANNEL_ID_FINANCE" } } },

    // Fallback: everything else in the guild goes to CEO
    { agentId: "ceo", match: { channel: "discord", guildId: "YOUR_DISCORD_GUILD_ID" } },
  ],
}

(That peer.kind: "channel" form is documented in the config reference: bindings match.peer supports direct|group|channel.)

Example 3) “Committee mode” (multiple specialists respond) — WhatsApp only today

Broadcast groups are currently WhatsApp-only (Discord/Slack planned). Useful when you want e.g. Security + QA + Docs to all chime in.

{
  agents: {
    list: [
      { id: "code-review", workspace: "~/.openclaw/workspace-code-review" },
      { id: "security", workspace: "~/.openclaw/workspace-security" },
      { id: "docs", workspace: "~/.openclaw/workspace-docs" },
    ],
  },

  broadcast: {
    strategy: "parallel",
    "120363403215116621@g.us": ["code-review", "security", "docs"], // WhatsApp group JID
  },
}

Docs: https://docs.openclaw.ai/channels/broadcast-groups

Example 4) Skills as “capabilities” (config + per-agent skill folders)

Enable/disable skills centrally, and keep department-specific skills in each agent’s workspace skills/ folder.

{
  skills: {
    // Optional: only allow specific bundled skills
    allowBundled: ["peekaboo"],

    // Optional: load an internal skills pack repo
    load: {

      extraDirs: ["~/corp/openclaw-skillpacks/skills"],
      watch: true,
    },

    entries: {
      // Example of enabling a skill and injecting env (host runs only)
      "nano-banana-pro": {
        enabled: true,
        env: { GEMINI_API_KEY: "${GEMINI_API_KEY}" },
      },
    },
  },

  agents: {
    list: [