Multiple Claudes: Delegation and Agents Explained

At some point you will hear about agents, and it will sound like the future arriving. Not one Claude answering your questions, but several of them working at once, taking on whole jobs on their own while you do something else. It is a real thing and it is genuinely powerful. It is also where more people overreach and get burned than anywhere else in this whole space, so it is worth understanding before you chase it.

This is the thirteenth post in a beginner's series on Claude. The first one mapped out the whole tool. This one is the honest version of the agent story, in plain language, including the part where most people should not start here yet.

What an Agent Actually Is

So far in this series, Claude has mostly answered you or done a single clear task. An agent is a step past that. It is Claude given a goal and the freedom to take several steps toward it on its own, deciding what to do next, using tools, checking its own work, without you approving each move. Delegation is the same idea one level up. Instead of doing everything itself, one Claude hands a whole sub-job to another Claude and gets the result back. A researcher, a writer, a checker, each a separate instance working its own part.

That is the dream that gets people excited. A little team of tireless workers, running in parallel, while you supervise from above.

Why Most Attempts Fall Apart

Here is the honest part. The large majority of ambitious agent setups fail before they are ever useful, and it is almost never because the model was not smart enough. It is because people point a team of agents at a vague goal, give them no shared understanding of the situation, and put no checks between them and the real world. Then they are surprised when the thing wanders off, does confident nonsense, and multiplies one misunderstanding across five workers at once. Agents do not fix a fuzzy plan. They execute it faster and in more places, which is worse.

The Three Things That Make It Work

The setups that actually hold up tend to share three habits. The first is keeping the number small. A few agents with clear roles beat a swarm every time, because coordination gets harder faster than the extra hands help. The second is shared context. Agents working on the same job need the same background, the same facts, the same picture of what done looks like, or they drift apart and produce work that does not fit together. The third, and the one people skip, is a human at the gates. Before anything irreversible happens, sending a message, spending money, deleting or publishing something, a person looks. Agents can do the work. They should not be the last check on the work.

Treat It Like a Capable Junior

The most useful way to think about an agent is as a talented junior on their first month. Give it a clear, well-scoped task and it will do the work well, often faster than you would. Ask it to decide what the work should be, or to run unsupervised on something that matters, and you are trusting judgment it has not earned. The skill is not building the most autonomous system possible. It is knowing which tasks are clear enough to hand off and which ones still need you to decide the shape before anyone, human or agent, starts executing.

You Probably Do Not Need This Yet

Here is the advice almost nobody gives. If you are still early with Claude, you do not need agents, and reaching for them now will mostly teach you frustration. Almost everything people want from a team of agents, they can get first from a single Claude that has good context, a real memory of their work, and the right tools connected. That is far more capable than most people ever push it to be. Agents are what you reach for when one well-set-up Claude is genuinely not enough anymore, which is much later than the excitement suggests. Master the one before you multiply it.

Where to Start This Week

Do not build an agent team. Instead, take the most capable single setup you can, a Claude with your context in a Project, your memory in place, and one tool connected, and hand it a task that has two or three steps. Watch how far a single well-equipped Claude gets on its own before you ever think about a second one. That experience will tell you honestly whether you have a problem that actually needs agents, or whether you just needed to use one properly.

Agents are the real frontier, and they will matter more over time, but the fastest way to get good at them is to get very good at the thing underneath them first. If you want a structured path all the way up to multi-agent systems, our free StudioMeyer Academy covers it. The series ends next time with the question everyone asks first and should ask last, which Claude model you should actually use.