[Integrated Practice]AI Agent Office Orchestration

Summary: Use AI Agent frameworks to chain multiple agents into an automated office pipeline — getting the weekly-report scenario fully working end-to-end.

Key Terms You Should Know First

This article is the "hands-on integrated practice" piece of the series, so every term is defined in plain English the first time it appears. After that, we treat them as shared vocabulary.

AI Agent = an AI assistant that can autonomously perceive, plan, and execute multi-step tasks. Unlike a regular GPT, an Agent doesn't just "answer your question" — it decides on its own what to do next, which tools to call, and how to report back when it's done.
MCP (Model Context Protocol) = a standardized, open-source protocol led by Anthropic, designed specifically to let Agents call external tools and data sources. The short version: previously, every time an Agent wanted to query a database, read a file, or hit an API, you had to write a custom adapter for that tool. With MCP, any tool can be "plugged in" as long as it exposes an MCP interface. ⚠️ Major frameworks vary in how completely they support MCP — status notes appear at the end of this article.
Coze / 扣子 = ByteDance's AI Agent platform (coze.com for overseas, coze.cn / 扣子 for China). Zero-code bot building with built-in workflows, plugins, knowledge bases, and multi-agent collaboration. The first choice for users in mainland China.
Dify = an open-source LLM application platform supporting Agents, workflows, and knowledge bases. Self-hostable; popular with engineering teams.
n8n = open-source workflow automation (like Zapier, but more powerful). Its AI Agent node launched in May 2024; the official MCP nodes (Server Trigger + Client Tool) shipped in v1.88.0 on 2025-04-10. As of 2024, MCP on n8n was still experimental. n8n natively connects to dozens of services including LLMs, HTTP endpoints, and databases.
Power Automate / Copilot Studio = the Microsoft stack. Power Automate handles process flows, AI Builder provides AI capabilities, and Copilot Studio lets you customize enterprise-grade Copilot Agents, deeply integrated with Microsoft 365.
Workflow vs. Multi-Agent = a workflow is "draw a fixed pipeline; data flows node by node," where each step is hardcoded. Multi-agent is "recruit specialized Agents and let a main Agent orchestrate them" — flexible, but easier to drift. Each has its sweet spot; we'll compare them below.
Plugin / Tool = external capabilities an Agent can call, e.g., "send-email plugin," "SQL query plugin," "Feishu message plugin." An Agent's ability to "actually do things" comes entirely from its tools.
Orchestration = chaining multiple Agents, tools, and workflows in a deliberate order so they cooperate like a team. Orchestration is not just piling up features — it has a clear "controller → division of labor → coordination" hierarchy.

1. The Pain Point

You've lived through this Friday afternoon:

Open your inbox, scan the past 7 days of email, pick out "customer feedback / project progress / risk flags," and manually copy-paste them into a weekly report draft;
Open the BI platform, export this week's sales numbers, new customers, and conversion rate, build a table, generate a chart;
Open Word, stitch the above together, tidy the formatting, and email it to your boss;
Mid-way, you get bounced back with "Can you make this chart more granular?" or "Double-check that number" — and you start over from scratch.

The whole thing eats 3–4 hours. But the part that actually needs thinking (summarizing, insight, recommendations) is only about 20%. The other 80% is mechanical data-shuffling.

That's exactly what an AI Agent pipeline is meant to solve: let the 80% of data-shuffling run automatically, so you only handle the 20% of judgment.

Going further: many companies are already using ChatGPT or Gemini. The real-world feeling is "great for chatting, but ask it to do a real chain of tasks and it falls apart" — because a single chatbot is "one question, one answer." It can't chain multiple steps, can't actually send email, can't query a database, can't write a file, can't push a Feishu message.

So this article isn't "introduce another chatbot." It's about really wiring multiple AI Agents together to build an automated office pipeline — using the weekly-report scenario as the example, walking you from zero to fully running.

2. What You'll Get Out of This

By the end of this article, you'll be able to:

Understand the real difference between an AI Agent and an ordinary chatbot, and know when it's useful vs. when it's hype;
Build a 3-Agent weekly-report pipeline on Coze / 扣子 (Email Agent + Data Query Agent + Report Generation Agent), with a minimal viable version running within 30 minutes;
Take away 3 ready-to-copy prompt templates (email digest / data insight / report generation) plus tool-call configuration;
See the orchestration differences across five major platforms (Coze / Dify / n8n / Power Automate / Copilot Studio), so you know which one fits your organization;
Grasp what the MCP protocol actually solves, so vendor marketing can't fool you.

Everything below is real, runnable, step-by-step. Every prompt can be copied as-is and used.

3. Case Study

Preparation: Picking a Platform and a Scenario

Why Coze / 扣子 as the main example?

Fast access from mainland China, strong Chinese prompt support;
The personal free tier grants 500 resource points per account per day (resets daily, cannot be topped up); the personal pro tier is ¥9.9/month with 30,000 resource points/month — the free tier is sufficient for this case study;
Both Multi-Agent mode and Workflow mode are available, so we can compare them side-by-side;
Rich built-in plugins (Feishu, email, databases, APIs, etc.);
Visual drag-and-drop, see it take shape as you build.

Comparison scenario: For the same weekly-report pipeline, I'll show you how it would be built on Dify / n8n / Power Automate and where the differences lie.

First, lock down your business scenario (we'll use the most common one: "automatic weekly reports"):

Every Friday at 17:00, automatically collect this week's key email items → automatically query the BI database for this week's numbers → automatically assemble a Markdown weekly report → push it to Feishu or email it to the boss.

Three Agents are involved:

Agent	Responsibility	Key Tools
Email Agent	Pull the past 7 days of email, filter out noise, extract key items	Coze's official generic SMTP/IMAP integration (configure the account once); community plugins such as Gmail / QQ Mail Assistant are alternatives
Data Query Agent	Query sales / customer data for this week's date range, do initial aggregation	HTTP node calling BI API / self-built database plugin (Coze has no generic SQL plugin; the built-in database only supports PostgreSQL)
Report Generation Agent	Combine email items + data into a Markdown weekly-report draft	LLM (writing model) + Feishu message plugin

Plus a main controller Agent / workflow to handle "scheduling."

Step 1: Sign Up and Set Up the Environment

Open 扣子 (coze.cn) (China version) and register with a phone number; overseas users go to coze.com;
Go to "Workspace," click "Create Bot," and choose "Multi-Agent mode" (Note: not "Single Agent" — Multi-Agent mode is what gives you the "controller + sub-agents" hierarchy);
Name it Weekly Report Assistant, description: automatically aggregate email + data + generate the weekly report every week.

💡 Key point: Coze has both "Single Agent mode" and "Multi-Agent mode." Multi-Agent mode is what this article uses. A Single Agent can call tools too, but it does everything by itself. Multi-Agent is "a main Agent orchestrating + multiple sub-Agents specializing" — far more extensible.

Step 2: Build the "Email Agent" — Sub-Agent 1

Goal: Let the Agent automatically pull the past 7 days of email, filter out notifications and ads, and extract key items.

2.1 Create the Sub-Agent

In the Multi-Agent orchestration canvas, add a sub-Agent and name it "Email Digest Agent."

2.2 Configure the Prompt (Persona & Task)

A sub-Agent's prompt has two parts: Persona + Task.

text
# Persona
You are a senior executive assistant, skilled at extracting key items from a large volume of email and filtering out irrelevant noise.

# Task
1. Call the "Email Query Plugin" to fetch all emails from the past 7 days (the date window is computed by the workflow's "Get Today's Date" node plus `date.subtract(N,'day').format('YYYY-MM-DD')`);
2. Filtering rules: discard "auto-notifications / ads / subscriptions / internal weekly-meeting CCs," and keep:
   - Customer feedback (inquiry, complaint, praise)
   - Partner communications (quote, contract, technical alignment)
   - Internal important items (approval, decision, risk warning)
3. Sort by time descending, and for each email extract "subject + one-sentence summary + key action item (who, when, what)";
4. Output a Markdown table with columns: Date / Sender / Subject / Summary / Action Item.

# Output Format
Return only the Markdown table, no explanatory text.

# Boundary
- Do not fabricate email content; if there is no email on a given day, write "None."
- Do not output full email bodies; summarize only.

2.3 Add Tools

In the sub-Agent's "Plugins" area, search for and add:

Email-related plugins: Coze officially provides a generic SMTP/IMAP integration (configure once, send and receive). The community marketplace also has finer-grained plugins like Gmail Assistant and QQ Mail Assistant — pick whichever fits your company's email.
If your email provider has no ready-made plugin, use the "HTTP Request" plugin and call your email provider's REST API yourself (WeCom Mail and Alibaba Mail both expose REST APIs);
Date tool: Add a "Get Today's Date" node in the workflow (or import a community plugin such as yesterday_date), and inject the date as a variable into the Agent. ⚠️ Coze Bot prompts do not have built-in date variables like {{yesterday}} — any {{}} placeholder content must first be defined in a workflow node or in the Bot's "Variables."

⚠️ Important: principle of least privilege — for the email plugin's token, grant only read access. Absolutely do not grant delete or send permissions. Even if the Agent drifts off-track, it can at most read, never write. This is one of the ironclad rules for putting Agents into production.

Step 3: Build the "Data Query Agent" — Sub-Agent 2

Goal: Query BI / database, pull this week's sales numbers, and do initial aggregation.

3.1 Create the Sub-Agent

Add another sub-Agent, named "Data Query Agent."

3.2 Configure the Prompt

text
# Persona
You are a data analyst, skilled at pulling data with SQL / APIs and producing initial aggregations.

# Task
1. Call the "Database Plugin" or "HTTP Node"; the database connection is in variable {{DB_CONN}} (defined and injected by the upstream workflow node);
2. Query data for this week (`{{week_start}}` to `{{week_end}}`); the SQL template is below. Both dates come from the workflow's "Get Today's Date" node plus `start_of_week` / `end_of_week` outputs;
3. Return: this week's sales / new customers / conversion rate / Top 5 customers / anomaly orders (amount over 100,000);
4. If the query fails, return the error message verbatim. Do not make up data.

# SQL Template (replace with your own business tables)
SELECT
  SUM(amount) AS week_sales,
  COUNT(DISTINCT customer_id) AS new_customers,
  AVG(conversion_rate) AS avg_conversion
FROM sales_dashboard
WHERE date BETWEEN '{{week_start}}' AND '{{week_end}}';
  -- {{week_start}} / {{week_end}} are injected by workflow nodes, not built-in Bot variables

# Output Format
Return JSON only, fixed fields:
{
  "period": "{{week_start}} ~ {{week_end}}",
  -- the two {{}} above are defined and injected by upstream workflow nodes
  "week_sales": number,
  "new_customers": number,
  "avg_conversion": number,
  "top5_customers": [{"name":"...","amount":number}],
  "anomalies": [{"order_id":"...","amount":number,"reason":"..."}]
}

# Boundary
- Never execute INSERT/UPDATE/DELETE; only SELECT.
- If nothing is found, return an empty array. Do not fabricate.

3.3 Add Tools and a "Safety Lock"

Database plugin: Coze has no generic SQL plugin. For external databases, go through an HTTP node calling your BI platform's query API (Metabase, Tableau, Quick BI, Sensors Data all have query interfaces), or build your own database plugin in the plugin marketplace (Coze's built-in database only supports PostgreSQL). Either way, the database connection string ({{DB_CONN}}) must be defined in a workflow node and injected into the sub-Agent — it must not be written as a "Coze built-in variable."
HTTP plugin: If your data lives in a BI platform, use HTTP to call its query API.
In Coze's "Code Node" or "Pre-check," add a guard: IF SQL contains INSERT|UPDATE|DELETE|ALTER|DROP THEN REJECT — that's your Agent's safety lock.

💡 Key point: Agents running SQL is the riskiest area. In production, you absolutely need three layers of protection — read-only account + template SQL + keyword blacklist. Skip any one of them at your peril.

Step 4: Build the "Report Generation Agent" — Sub-Agent 3

Goal: Take the outputs of the first two Agents and generate a Markdown weekly report.

4.1 Create the Sub-Agent

Add a third sub-Agent, named "Weekly Report Generation Agent." Note: this Agent does not need external tools; it only uses an LLM for writing.

4.2 Configure the Prompt

text
# Persona
You are a senior project manager, skilled at turning scattered information into a clear weekly report.

# Input
You will receive two outputs from upstream Agents (filled in via the main Agent's / workflow node's "global variables"):
- {{input.email_summary}}: the Markdown table returned by the Email Agent
- {{input.data_summary}}: the JSON returned by the Data Agent

# Task
1. Read both inputs and extract this week's "key progress / data highlights / risks and issues / next-week plan";
2. Output the weekly report using the following Markdown template:

# {{input.week_label}} Weekly Report

## 1. Key Progress This Week
- (extract 3–5 items from {{input.email_summary}})

## 2. Data Highlights
- Sales: {{input.data_summary.week_sales}}
- New Customers: {{input.data_summary.new_customers}}
- Conversion Rate: {{input.data_summary.avg_conversion}}%
- Top Customers: ... (list Top 3)

## 3. Risks and Issues
- (extracted from emails tagged "risk / anomaly" in {{input.email_summary}} plus data anomalies)

## 4. Next Week's Plan
- (infer 3 items based on this week's incomplete items + pending customer actions)

# Boundary
- All data must come from the inputs; never invent.
- Keep numbers to 2 decimal places.
- Keep total length under 800 words.

4.3 No External Tools Needed

This Agent is a "pure writing" Agent. Don't give it database or email tools — fewer tools, more stable Agent.

⚠️ Reminder on variable pre-definition: {{input.week_label}}, {{input.email_summary}}, and {{input.data_summary}} in the prompt are all user-defined variables. They must first be declared in the Bot's "Variables," or filled in by the main Agent's / workflow node's "global variables" — Coze Bot prompts do not have a built-in mechanism where "writing {{}} makes it instantly usable."

Step 5: Main Controller Agent / Workflow Orchestration

This is the most critical step — chaining the three sub-Agents into a pipeline.

Option A: Multi-Agent Mode (native to Coze, suited for "routing dispatch," not strict sequential scheduling)

⚠️ Critical clarification: Coze's "Multi-Agent mode" is fundamentally routing dispatch — it is not a main Agent that uses natural language to "sequentially schedule" sub-Agents. The main Agent cannot chat-style say "first call A, then B, take the result and call C." It can only choose routes through a "Global Jump" node with a structured target/context JSON. For strict sequential pipelines (email → data → report → Feishu), use Option B's workflow.

The main Agent's prompt only describes "when to choose whom"; the actual routing is handled by the "Global Jump" node or sub-Agent node configuration:

text
You are the main controller Agent of the Weekly Report Assistant. Rules (route only, do not orchestrate):

- If user input contains keywords "email / inbox / mailbox" → route to [Email Digest Agent];
- If user input contains keywords "data / sales / BI" → route to [Data Query Agent];
- If user input contains keywords "weekly report / generate report / summary" → route to [Weekly Report Generation Agent];
- Other scenarios → answer yourself.

# Sample output of the "Global Jump" node (structured JSON, not natural language)
{
  "target": "email_agent | data_agent | report_agent | self",
  "context": {
    "date": "{{date.format('YYYY-MM-DD')}}",                  // computed by the workflow node
    "yesterday": "{{date.subtract(1,'day').format('YYYY-MM-DD')}}",
    "week_start": "{{date.start_of_week.format('YYYY-MM-DD')}}",
    "week_end":   "{{date.end_of_week.format('YYYY-MM-DD')}}"
  }
}

Real constraints (lessons from the trenches):

Multiple sub-Agents cannot directly pass structured variables to each other; they must rely on "global variables" or a workflow to feed upstream results into downstream Agents;
The main Agent cannot see the intermediate results of upstream Agents, nor can it guarantee a fixed execution order;
Therefore, "strict sequential scheduling" is awkward in this mode — a pipeline like "email → data → report → Feishu" does not fit Option A.

Trigger modes: In Coze you can configure "Scheduled Trigger" (every Friday at 17:00) or "Webhook Trigger" (called by an external system).

Option B: Workflow Mode (the right answer for strict sequential scheduling)

If the dependency between the 3 Agents is clear, use Workflow mode for strict sequential scheduling, drawn out as nodes:

[Start: Scheduled Trigger]
   ↓
[Email Digest Agent Node]
   ↓
[Judgment Node: Was the email step successful?]
   ↓ Yes              ↓ No
[Data Query Agent Node] [Send alert email to admin]
   ↓
[Judgment Node: Was the data step successful?]
   ↓ Yes              ↓ No
[Weekly Report Generation Agent Node] [Send alert email to admin]
   ↓
[Feishu Message Integration (based on Feishu custom bot Webhook): Send weekly report]
   ↓
[End]

Workflow mode's advantage: every step has explicit success/failure branches. If an Agent drifts, alerts fire immediately, and execution strictly follows "email → data → report → Feishu" in order. Multi-Agent mode suits "routing dispatch" — the user says one sentence, the main Agent picks one sub-Agent to respond; it is not suited for strict sequential scheduling.

⚠️ Prerequisite for the Feishu message integration: Coze's "Feishu Message" integration is built on Feishu's custom bot Webhook. You must first open the target group in Feishu → "Group Settings" → "Group Bots" → "Add Bot" → "Custom Bot," grab the Webhook URL, then paste it back into Coze's integration page. For Enterprise Flagship accounts, an organization admin must also enable the "External Integration" toggle first, otherwise saving will fail.

💡 Practical recommendation: use Workflow mode directly for strict sequential scenarios; only use Multi-Agent mode for "routing one sentence to different sub-Agents." Don't try to make Multi-Agent mode "sequentially schedule sub-Agents via natural language" — it can't reliably execute in order, and if any link fails, the main Agent has no reliable "what next" mechanism.

Step 6: Test and Trial Run

In Coze's "Preview & Debug" area, manually type "generate weekly report" and see if the 3 Agents trigger in order;
First run with test data / mock data — don't connect real email and database right away;
Check each step's "Run Log"; Coze shows the input and output of every Agent;
Deliberately induce a failure: deliberately enter the wrong token for the email plugin, and see whether the "Judgment Node" catches the error and fires an alert;
Once it passes, configure the scheduled trigger in "Publish" (every Friday at 17:00).

Step 7: Roll Out in Small Steps

Don't connect the boss's email and the real database on day one. Suggested rollout order:

Week 1: Use it yourself to generate "your own weekly report," save the result as a draft, then send it after manual edits;
Week 2: Use it for yourself + your direct manager; turn the draft into "semi-automatic";
Week 3: Roll it out to the team; connect to a Feishu group;
Week 4: Connect to the boss; fully automatic.

At every step, keep a "human review" gate so Agent errors surface immediately.

Appendix: Side-by-Side Comparison of Five Platforms

Platform	Best For	Orchestration Style	Multi-Agent Capability	MCP Support	Learning Curve
Coze / 扣子	China-domestic business, product / ops teams	Multi-Agent + Workflow dual mode	Strong (native)	Partial (currently indirect via community MCP plugins in the marketplace such as MCP SSE / MCP Agent Strategy; no official native MCP Server / Client)	Low
Dify	Engineering teams, self-hosting needs	Workflow + Agent	Medium (supported, but weaker than Coze)	Supported (native bidirectional since v1.6.0 / 2025-07; v1.8.0 / 2025-08 added OAuth resource discovery)	Medium
n8n	Engineering teams, cross-system integration	Workflow + native AI Agent node	Medium (node-based)	Partial (officially node-based since v1.88.0 / 2025-04, Server Trigger + Client Tool)	Medium-High
Power Automate	Microsoft ecosystem, multinationals	Workflow + AI Builder	Medium (via Copilot Studio)	Weak	Medium
Copilot Studio	Deep M365 users	Agent + topic flows	Strong (M365 linkage)	Supported (announced at Build 2025, GA later that year; can connect remote MCP Servers as tools; MCP Resources preview added from 2025-10)	Medium

One-line selection guide:

Business / ops / boss → Coze / 扣子
Backend / want private deployment → Dify
System integration / complex chains → n8n
Whole company on M365 → Copilot Studio + Power Automate
Want "all of the above" → let n8n orchestrate across systems and call the Coze / Dify APIs as the underlying AI base

⚠️ Platform risk note: The Coze / 扣子 plugin marketplace changes frequently; refer to what you actually see when logged in. Dify's and Copilot Studio's MCP support is evolving rapidly — this article was written in mid-2025, and the situation may have updated since.

4. Principle Summary

An AI Agent orchestration pipeline boils down to three things:

1. Decomposition: Break a fuzzy big task like "write a weekly report" into 3 clearly-scoped small tasks — "pull email / query data / write report" — each handled by its own Agent. Don't let one Agent do everything; it gets slow and messy.

2. Orchestration: The main controller Agent or workflow decides "who goes first, whose result goes to whom, what to do when someone fails." At its core, orchestration is an explicit state machine — you can't do it "by feel."

3. Tools: An Agent is not "a chatbot that can think"; what makes it actually productive is real tools behind it — email APIs, SQL databases, the Feishu SDK. The essence of the MCP protocol is standardizing "tool integration": previously, every new tool required custom adapter code; with MCP, any tool exposing an MCP interface can be used by an Agent directly. MCP is still early; Coze / Dify only support it partially — but the direction is set, and it will likely become the de facto standard within the next 1–2 years.

Above all, remember: AI Agents are not omnipotent. They can handle "step-by-step work with clear data sources," but they cannot handle tasks requiring deep business judgment, cross-team negotiation, or political sensitivity. Treat an Agent as "a capable intern," not "a manager who can make decisions."

5. Pitfall Guide

1. Treating the Agent as an "all-purpose assistant," then asking it to do things beyond its capability Pitfall: Letting the Agent write SQL on its own, delete data, or send external email on its own. Fix: Give the Agent's tools a whitelist + least privilege. Use SQL templates. The email plugin gets read-only. Any outbound message requires manual confirmation.

2. Writing vague, open-ended prompts so the Agent free-styles off course Pitfall: "Help me organize this week's email" — and the Agent drags in ads and spam. Fix: Every prompt should have the trio of "Boundary + Filtering Rules + Output Format." Look at the 3 templates in this article and copy the "Boundary" section pattern.

3. No layered security — once the Agent flies off, there's no way back Pitfall: The Agent mistakenly calls the "delete email" API and the boss's email is gone. Fix: Read-only account + keyword blacklist (INSERT/DELETE/DROP) + explicit judgment node at every step. All three layers are required.

4. Connecting real production data right away — one mistake takes the business down Pitfall: The Agent's SQL is buggy, the production database throws errors, and operations stop. Fix: Run it through with mock data / a test database first, then switch to the real account only after it passes, keeping operation logs for the entire transition.

5. Ignoring cost — Agent calls blow up and the bill is shocking Pitfall: The Agent is configured to "have the LLM summarize every email," 500 emails land on Monday, and token costs explode. Fix: Add input-length truncation + call-frequency limits to the Agent. Use cheaper models for small tasks (GPT-4o-mini, Doubao Lite (doubao-lite-32k), etc.), and reserve the flagship models for big tasks.

6. Advanced Extensions

After you've got the "3-Agent weekly-report pipeline" from this article running, you can branch out in these directions:

Use n8n as the cross-system orchestrator: Coze handles AI reasoning, n8n handles cross-SaaS triggers (Jira, Slack, Notion, WeCom, DingTalk all connected); the two talk to each other over HTTP / Webhooks;
Use Dify for enterprise self-hosting: Coze data lives in the cloud; some companies (finance, healthcare, government) need private deployment, so migrate the Agents to a self-hosted Dify;
Use Copilot Studio to bridge M365: If your whole company runs on Teams / Outlook / SharePoint, building Agents in Copilot Studio lets them consume M365 data natively;
Use the MCP protocol to plug in standardized tools: Anthropic's official MCP Server list includes databases, filesystem, GitHub, Slack, and more — follow the docs and Agents can extend their capabilities "like plugging in a USB device";
Upgrade "workflow" to "agent team": Scale from 3 Agents to 5, then 10; add a "Weekly Report Review Agent" that critiques the others' output, add a "Customer Profile Agent" for personalized suggestions;
Learn LangChain / LangGraph to build your own Agent framework: When you find Coze / Dify can't handle "super complex business logic," drop down to LangChain / LangGraph for code-level Agents — maximum freedom, but the highest bar to entry.

One sentence to remember: Agent orchestration isn't "install Coze and you're done." It's a combined engineering effort of "tool selection + prompt engineering + permission governance + trial-run iteration." Once you've gotten it running once, the rest is "copy, paste, and tweak."

📌 Next steps: If you want to learn "how to bring Excel / VBA into the Agent pipeline," see B20 Automated Pipelines and B25 Batch Excel in the series. For enterprise-grade rollout (permissions / audit / compliance), see A5 Security Red Lines and C5 Low-Code Approval Flows.

🎓 CTA: vba.net's "AI Office Bootcamp" (L4 Integrated Practice track) has turned "Agent Orchestration" into a 4-week check-in assignment, walking alongside companies through real rollout. If you're interested, check the syllabus at vba.net/c6-agent.

📦 AI Agent Office Orchestration