Lesson 1 · Module 4: Agentic Systems: Manager and Worker Agents
From one agent to a team: when a multi-agent system is worth it
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Key points from the video
One assistant, too many jobs
- Maya runs an online candle shop
- One AI assistant answers messages, books calls, sends invoices
- It starts mixing things up
What you will learn
- Spot where a single agent starts to strain
- Describe the manager and worker team shape
- Decide with a simple checklist if a team is worth it
A single agent, quick recap
- An agent = a language model + instructions + tools
- It loops: read, plan, use a tool, check, repeat
- All jobs share one prompt and one memory
Where one agent strains
- Inbox: tone and return policy
- Calendar: time zones, hours, no double bookings
- Invoices: numbers, tax, current prices
- One long prompt, a dozen tools
The team shape
- A manager agent reads the request and routes it
- Worker agents each own one job and its tools
- Each worker gets a short, focused prompt
Worked example: one message, two jobs
- "Can I book a call Thursday at 3, and resend my September invoice?"
- Manager splits it, two workers act, one reply goes back
layout: wide
direction: LR
components:
user: {kind: user, label: Customer}
mgr: {kind: llm, label: Manager agent}
book: {kind: worker, label: Booking worker}
bill: {kind: worker, label: Billing worker}
cal: {kind: database, label: Calendar}
inv: {kind: storage, label: Invoices}
links: [user->mgr, mgr->book, mgr->bill, book->cal, bill->inv]
steps:
- at: "a customer writes in"
show: [user, mgr]
- at: "splits the message into two"
show: all
- at: "the booking worker checks"
flow: ["user>mgr: message", "mgr>book: book Thu 3pm", "book>cal: free?", "cal>book: yes", "book>mgr: booked"]
- at: "the billing worker finds"
flow: ["mgr>bill: Sept invoice", "bill>inv: find", "inv>bill: PDF", "bill>mgr: link", "mgr>user: one reply"] The price of a team
- More agents means more model calls
- More calls means more cost and slower replies
- More handoffs means more places to fail
Four signs a team is worth it
- The jobs need different skills or tone
- Each job uses its own tools
- Mistakes are costly, so each job needs focus
- One prompt keeps growing and breaking
A quick decision check
- Different skills or tone: yes
- Its own tools per job: yes
- Costly mistakes: yes
- One prompt keeps breaking: yes
- Score 4 of 4, two or more means a team
Common mistake: too many agents
- One agent per tiny step: "greet", "read", "reply"
- Each step adds cost, delay and handoffs
- Split by job, not by step
Your exercise
- Pick a real small business workflow
- List its jobs, tools and costly mistakes
- Score it on the four signs and decide: one agent or a team
Recap
- One agent strains when rules and tools pile up
- A manager routes, focused workers each own a job
- Build a team only when the signals outweigh the cost
Transcript
One assistant, too many jobs. Meet Maya. She runs a small online candle shop, and she built one AI assistant to help. It answers customer messages, books video calls for custom orders, and sends invoices. For a few weeks it's great. Then things go wrong. A customer asks about a refund and gets a booking link. An invoice goes out with last month's prices. So what happened? The assistant isn't broken. It's overloaded, the way a person would be if they did three jobs at one desk with one notebook. In this lesson we'll work out when splitting one agent into a small team fixes that, and when it just adds cost.
What you will learn. Here's the plan for today. First, you'll learn to spot the moments where a single agent starts to strain, so you know what problem you're actually solving. Second, you'll see the basic shape of a multi-agent system, which simply means several agents working together: one manager agent and a few worker agents. Third, and most useful for your clients, you'll get a simple checklist that tells you whether a team is worth building or whether one well-built agent is the better deal. By the end you'll be able to look at any small business workflow and make that call with confidence.
A single agent, quick recap. Let's recap what one agent is. An agent is a language model, the AI that reads and writes text, plus instructions telling it how to behave, plus tools, which are actions it can take, like reading a calendar or creating an invoice. It works in a loop. It reads the message, plans a step, uses a tool, checks the result, and repeats until the job is done. The key detail is this. Every job it does shares one set of instructions, which we call the prompt, and one memory of the conversation. That sharing is fine for one job. It gets crowded when there are many.
Where one agent strains. Picture Maya's assistant at its desk. Start with the Inbox. Customer messages arrive about scents, shipping and refunds, and each one needs a friendly tone and the shop's return policy. Now the Calendar. Booking a call needs exact rules: time zones, opening hours, no double bookings. Then the Invoices. Billing needs numbers, tax and the current price list, with zero room for creativity. All three sets of rules sit in one long prompt, and the agent has a dozen tools to choose from. The longer that prompt grows, the more often it grabs the wrong rule or the wrong tool. That's exactly the refund that got a booking link.
The team shape. The fix is to split the work, the same way a growing shop hires people. A manager agent reads each request, works out what's being asked, and routes it, meaning it sends each part to the right worker. Worker agents each own one job. The booking worker only has calendar tools and booking rules. The billing worker only has invoice tools and the price list. The support worker knows the tone and the return policy. Each worker has a short, focused prompt, so there's far less to confuse. The manager then gathers the results into one reply for the customer.
Worked example: one message, two jobs. Let's walk one real message through the team. A customer writes in: can I book a call Thursday at three, and can you resend my September invoice? The manager reads it and splits the message into two tasks. First, the booking worker checks the calendar, sees Thursday at three is free, books it, and reports back. Then the billing worker finds the September invoice, grabs the PDF link, and returns it. The manager now has both answers and sends the customer one tidy reply. Notice that neither worker ever saw the other's rules, so neither could mix them up.
The price of a team. Now the honest part. A team isn't free. Every agent that thinks is a separate call to the model, and you pay per call. Let's use simple, rough numbers. Say one call costs about one cent, and suppose the single agent answers a simple question in one call, so one cent. Suppose the team handles that same message with three calls: the manager, a worker, then the manager again to write the reply. That's three cents. Real agents often loop through several calls, but the extra layer always adds more. Look at the receipt: roughly three times the cost. Then the stopwatch: each call adds a few seconds of waiting. And every handoff between agents is one more place where details can get lost.
Four signs a team is worth it. So when does a team pay for itself? Look for four signs. One, the jobs need different skills or tone, like warm support replies versus exact invoices. Two, each job uses its own tools, so splitting them shrinks the menu each agent picks from. Three, mistakes are costly, like a wrong amount on an invoice, so each job deserves its own focused agent that thinks about nothing else. Four, your single prompt keeps growing, and every fix for one job breaks another. When you see two or more of these, specialists usually beat one generalist. When you see none, keep it simple.
A quick decision check. Let's run that checklist on Maya's shop, the same way you'll do it in a no-code tool. Different skills or tone? Yes: warm support replies and exact invoices. Its own tools per job? Yes: calendar tools for booking, invoice tools for billing. Costly mistakes? Yes: a wrong price on an invoice costs money and trust. One prompt keeps breaking? Yes, that's the refund that got a booking link. Now count the ticks. The rule is simple: two or more means split into a small team. Maya's shop scores four out of four, so the verdict is a team, and the extra calls are worth paying for.
Common mistake: too many agents. The most common mistake is splitting too far. Builders get excited and create an agent for every tiny step: one to greet, one to read the message, one to write the reply, one to format it. Look at the handoffs in this picture. Every pass of the envelope costs a model call, adds delay, and risks losing a detail, so by the end the envelope arrives crumpled. A good rule is to split by job, not by step. Booking is a job. Billing is a job. Saying hello is not. If two steps always happen together, keep them in the same agent.
Your exercise. Now it's your turn. Pick a real workflow from a small business you know, maybe a hair salon booking appointments, a tutor sending invoices, or a shop answering order questions. In your notebook, list every job inside that workflow, the tools each job needs, and the mistakes that would really hurt. Then score it on the four signs from this lesson, and write your verdict: one agent or a small team, and if a team, name the manager and each worker. Keep it to one page, because you'll use this exact page in the next lesson.
Recap. Let's recap. First, a single agent starts to strain when too many rules and tools pile into one prompt, and it begins grabbing the wrong one. Second, the team shape is a manager that reads and routes each request, plus focused workers that each own one job with its own short prompt and tools. Third, a team costs more calls, more time and more handoffs, so build one only when the signals outweigh that cost: different skills, different tools, costly mistakes, or a prompt that keeps breaking. When you do build a team, split by job and keep it small. In the next lesson we'll design the manager agent, using the page you just wrote.
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Lesson summary
- Video watched
- An agent = a language model + instructions + tools
- It loops: read, plan, use a tool, check, repeat
- All jobs share one prompt and one memory