What Are Claude Code Agents? And How to Use Many at Once

Claude Code agents explained in plain English — what an agent actually is, how subagents differ, and the leap from one agent to a coordinated team.

Chaitanya Giri 3 min read Concepts
TL;DR

A Claude Code agent is a running Claude session with a goal and tools — it reads files, edits code, and runs commands in a loop. Subagents are short-lived helpers an agent spawns for fan-out. The real leap isn't more agents; it's getting several to act as a coordinated team with roles, shared memory, and messaging.

If you’re new to Claude Code, “agent” gets thrown around a lot. Here’s the plain-English version — what an agent actually is, how subagents fit, and what changes when you run several at once.

What an agent actually is

An agent is just a loop with a goal. A Claude Code agent is a running Claude session that can:

  • read your files and project context,
  • act — edit code, run shell commands, call tools — toward a task, and
  • repeat, observing the results and deciding the next step.

That’s it. Every terminal where you start a claude session is an agent: one goal, one context window, one loop. The magic isn’t the word “agent” — it’s that the loop can use tools and keep going until the job’s done.

Agents vs. subagents

These two get conflated constantly, so let’s separate them:

Agent

A top-level session you drive. It owns a task from start to finish and you interact with it directly.

Subagent

A helper the main agent spawns inside its own run to fan out a chunk of work — “go research these five files in parallel,” for example. Subagents are powerful for parallelism, but they’re short-lived and scoped to that one parent: they don’t persist after the run, and they don’t share memory across your whole workflow. For the deeper trade-offs, the difference between subagents and a full harness is worth understanding before you lean on either.

The short version: subagents help one agent do more at once; they don’t make many agents into a team.

From one agent to many

One agent handles most tasks fine. You start wanting several when work naturally splits — someone writes tests while someone refactors while someone updates docs. The naive way is to open more terminals, and that works… until it doesn’t:

  • agents collide on the same files,
  • each session forgets what the others learned, and
  • you lose track of who’s doing what across a wall of tabs.

That’s the wall every multi-agent setup hits. The fix isn’t more agents — it’s coordination. We wrote the practical playbook in how to run multiple Claude Code agents.

What turns agents into a team

Three additions turn parallel sessions into something that behaves like a team:

  • Roles — each agent gets a job, so “who should do this?” is obvious.
  • Shared memory — a durable, long-term memory every agent reads, so knowledge compounds instead of resetting.
  • Coordination — agents message each other and an orchestrator routes work, so you’re not the bus.

When you wrap your agents in that layer, you’ve got a multi-agent harness — the concept that turns “five chat windows” into “one office.”

Where to go next

Munder Difflin is the team layer for the Claude Code agents you already run — roles, shared memory, messaging, and an orchestrator, all local. Download Munder Difflin to turn your agents into an office; it’s free and open source.

FAQ

What are Claude Code agents?

A Claude Code agent is a running Claude session with a goal and a set of tools — it can read and edit files, run commands, and work toward a task in a loop. Each terminal you start a Claude session in is an agent.

What's the difference between an agent and a subagent in Claude Code?

An agent is a top-level session you drive. A subagent is a helper the main agent spawns inside its own run to fan out a piece of work — short-lived and scoped to that parent. Subagents are great for parallel sub-tasks but don't persist or share memory across your whole workflow.

How do I use multiple Claude Code agents at once?

Open several sessions, give each a clear role, and coordinate them — ideally with shared memory and messaging so they don't collide or forget. A multi-agent harness like Munder Difflin automates that coordination so the sessions act as one team.