Multi-Agent Systems, MCP and Agentic Workflows Explained

Once you've built a single agent, three ideas come up fast: agentic workflows, multi-agent systems and MCP. Here's what each means and when you need it.

Workflow vs agent

In a workflow, your code decides the order of steps — extract, validate, save — and the model does each step. In an agent, the model decides which tools to call and in what order. Workflows are cheaper and more predictable; use an agent only when the steps genuinely can't be scripted in advance.

Multi-agent systems

A multi-agent system splits a big job between agents. The common, effective pattern is an orchestrator and workers: the orchestrator plans and splits the work, each worker handles one piece in its own separate context (often on a cheaper model), and returns a short result. The orchestrator never sees the workers' full transcripts, so its context stays small.

Use it for research across many sources, processing many records, or any "look into N things, then summarise" job. Avoid it when each step needs the full detail of the last one — one agent does that better.

Rules that keep multi-agent systems sane: workers return results, not transcripts; workers get as few tools as possible; irreversible actions stay behind a human; and you budget the whole tree, not just the orchestrator.

MCP: the Model Context Protocol

MCP is a standard way for a service to publish its tools so any agent can use them — instead of you writing a wrapper for every API. Issue trackers, calendars, code hosts and CRMs increasingly offer MCP servers. Your agent connects to the server and gains its tools.

Use MCP when a trusted service already publishes a server and the tools are read-mostly. Write your own tool when the action touches your own data or needs your approval flow.

Staying safe

• Only connect MCP servers you trust, and enable only the tools you need.
• Treat every tool result — MCP or otherwise — as untrusted input.
• Keep irreversible actions as your own gated tools.

Agentic workflow builders

No-code workflow builders are useful for simple automations. When you need custom tools, real guardrails, testing and cost control, building in code gives you the control — and teaches you what the builders do underneath.

Ready to build one properly? The Agentic AI Course takes you from your first tool call to a hosted, production-ready agent in 24 lessons — beginner to advanced. Lesson 1 is free.