Multi-Agent Task Automation
Part of the AI Engineer Roadmap
In short
Build a system where an orchestrator reads incoming requests and hands each one to a specialist agent: email, calendar or spreadsheet. It's how real office automation is built.
A system where multiple coordinating agents automate an end-to-end workflow (e.g. research → draft → review → finalize) — demonstrates orchestration beyond a single-agent design.
Think of it like this
The bank token counter
At the bank, the token desk asks what you need and sends you to counter 2 for deposits or counter 5 for loans. Anything odd goes to the manager. Your orchestrator is the token desk, the specialist agents are counters, and you are the manager.
Watch it happen
How the parts talk
Book lab for Fri 3pm
1/6A new request lands in the queue.
The big ideas
What you need to know
Router first
A small classifier picks the specialist. Unknown or risky labels always go to a human.
Tiny toolsets
Each specialist owns only its tools. The email agent can't touch the calendar.
A shared task board
Track every task's status, owner agent and result in one table. It's your audit log and your retry list.
Retries without repeats
If a step fails, retry it safely. Make sure 'send invite' can't run twice for one task.
See it in code
Walk through the code
1AGENTS = {2 "email": lambda t: f"drafted reply for: {t}",3 "calendar": lambda t: f"booked slot for: {t}",4 "sheet": lambda t: f"updated sheet with: {t}",5}6 7def orchestrate(tasks, classify):8 board = []9 for task in tasks:10 kind = classify(task) # an LLM returns one AGENTS key11 if kind not in AGENTS:12 board.append((task, "needs human"))13 continue14 board.append((task, AGENTS[kind](task)))15 return board1/3The list of specialists. Adding a new one means adding one entry here.
The orchestrator skeleton. Each lambda stands in for a real specialist agent with its own tools.
Save yourself the pain
Common mistakes
Oops
Letting the router make up new agent names.
Do this instead
Constrain the classifier to a fixed list of labels and treat anything else as 'needs human'.
Oops
No record of which agent did what.
Do this instead
Write every task, label, result and timestamp to the board. Debugging without it is guesswork.
Your mission
First milestone: route ten requests
Quick check
The classifier returns 'payments' but there is no payments agent. What should happen?
Go deeper
- 1Design a workflow with at least two agents playing distinct roles.
- 2Add a clear handoff protocol so agents pass structured output between each other reliably.
- 3Measure and report where the multi-agent version outperforms a single-agent baseline.
More projects at this stop
Autonomous Research Agent
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