Claude Code agents: Everything teams need to know before choosing

Davis Christenhuis

-March 27, 2026

Claude Code ships with a built-in agent system that lets developers delegate isolated tasks like test runs, codebase searches, and code reviews to specialized workers, all without cluttering the main session. But how do these agents actually work, and when does your team need something different? This guide breaks it down.

📌 TL;DR

In a rush? Here's what this guide covers:

Claude Code includes subagents: isolated AI workers that handle tasks like test runs, code exploration, and reviews in separate context windows, keeping your main coding session clean.
They automate terminal workflows like running tests, exploring codebases, and enforcing read-only code reviews for developers.
The core benefit is context isolation. Each agent keeps high-volume output separate from your main coding session, preventing context degradation.
Workflow agents serve teams working outside terminals by connecting to business platforms like Slack, Notion, and CRMs for cross-team automation.
Dust is a platform where teams across entire organizations, from engineering to sales to support, deploy agents without writing code, with plans starting from individual teams to enterprise-wide deployments.

What are Claude Code agents?

Claude Code agents (officially called "subagents" in Anthropic's documentation) are task-specific AI workers built into the Claude Code development tool. Each subagent runs in its own context window, handling focused jobs like codebase exploration, test execution, or code review, then reporting results back to your main session without overloading it with lengthy output.

Claude Code ships with several built-in subagent types, including:

The Explore subagent handles read-only file discovery and codebase search using the faster Haiku model
The Plan subagent gathers context before presenting implementation strategies
The general-purpose subagent tackles complex tasks that require both exploration and code modification
The Bash subagent executes terminal commands in an isolated context

Developers can also define custom agents as Markdown files with YAML frontmatter, specifying which tools each agent can access and which AI model it should use.

Each agent operates independently with its own context window, keeping high-volume output isolated from your main session. When you spawn an agent to run your test suite or search for function references across a large codebase, all the output stays inside that agent's context.

What Claude Code agents are built for

Most developers hit the same wall: long sessions accumulate so much output that the main context becomes noise. Claude Code's agent system is built specifically to prevent that. Here's what it actually solves.

Isolating high-volume operations: Running test suites, processing log files, or fetching documentation produces massive outputs that would consume your main context window. Agents handle these operations separately and return only actionable summaries.
Enforcing tool restrictions: A code review agent can be configured with read-only tool access, restricting it to file reading and search operations without the ability to modify files through the permitted tool set.
Routing tasks to cheaper models: Exploratory work like "find all files importing this package" doesn't need Opus-level reasoning. Agents let you route simple tasks to Haiku at a fraction of the cost.
Managing context across long sessions: Developer sessions often span hours across dozens of files. Agents prevent context degradation by keeping research, exploration, and verification work in separate windows.
Automating terminal commands: Agents execute bash commands, run build scripts, and interact with developer tools directly from the command line.

When teams need something different

Claude Code agents work for developers writing code in terminals. Teams outside that specific workflow encounter different problems:

They're optimized for developer workflows: While Claude Code now runs in terminals, VS Code, JetBrains IDEs, desktop apps, and web browsers, and can connect to external services via MCP, it remains a developer-first tool. Non-technical team members would still need technical knowledge to configure agents, define tool permissions, and work within a development-oriented interface.
Advanced integrations still require technical setup: While Claude Code offers one-click OAuth connectors for common tools like Slack, Jira, and Google Drive, custom MCP server configurations, advanced permission policies, and organization-wide deployment still require developer-level configuration. The setup gap widens as integration complexity increases.
Technical setup required: Building a custom agent means creating a Markdown file with YAML frontmatter and understanding tool restrictions. That limits who can create and deploy agents to developers only, even if the end users would be non-technical colleagues.
Limited organizational visibility: Claude Code’s Teams and Enterprise plans offer usage analytics dashboards for tracking code contributions and adoption metrics. However, they provide limited visibility into which agents are deployed across the organization. They also offer less clarity on what data those agents can access and how permissions are configured, especially compared with purpose-built enterprise agent platforms.

💡 Need AI agents that work across your entire team? See how Dust works →

Different types of AI agents for teams

AI agents split into separate categories based on where they run and what problems they solve. For teams evaluating tools like Claude Code, the most important comparison is between coding agents and workflow agents.

Coding agents automate development workflows inside terminal environments, while workflow agents automate business processes across company tools and platforms.

Comparison table: Coding agents vs Workflow agents

	Coding agents	Workflow agents
Built for	Developers writing, debugging, and reviewing code	Business teams automating knowledge work
Where they run	Terminal and code editor environments	Dedicated platform with integrations into Slack, Notion, CRMs, and other business tools
Who deploys them	Developers	Teams and organizations
User profile	Engineers with technical knowledge	Anyone across sales, support, ops, HR
Primary input	Code, files, terminal commands	Documents, conversations, business data
Output	Modified code, test results, commit suggestions	Summaries, drafted responses, updated records
Collaboration scope	Individual developer sessions or coordinated team workflow	Shared across teams with collective context

Coding agents are built for developers who need to keep development context clean during long terminal sessions. Workflow agents are built for business teams who need to automate work across multiple platforms and data sources.

What is Dust?

Dust is a platform where enterprise teams deploy specialized agents that connect to company data and work alongside employees. The agents understand your organization's context because they're connected to the systems teams already use: Slack, Notion, Salesforce, Google Drive, and more.

The platform acts as an AI operating system for your organization, orchestrating agents across your company's tools. A sales team might deploy an agent that pulls customer data from HubSpot, references past Slack conversations, and drafts deal status updates ready for review in the CRM.

A support team might use an agent that searches the knowledge base, checks ticket history, and drafts responses based on company guidelines. Each agent accesses only the data its assigned team is permitted to see.

Unlike coding agents that require Markdown configuration files with YAML frontmatter or command-line knowledge, Dust provides a no-code interface where teams define what an agent should do, which data sources it can access, and who on the team can use it.

Dust is built for enterprise security requirements. The platform is SOC 2 Type II certified, GDPR compliant, and enables HIPAA compliance, with data encrypted at rest and in transit, and regional hosting options.

💡 Want to deploy AI agents across your team? Start your free 14-day trial →

How companies are using Dust

Watershed achieved 90% adoption by embedding AI champions in every department. Sales teams use agents to research prospects, engineering built design documentation agents, and a third of the company uses a performance review coach agent.
Spendesk reached 93% monthly active users, with over 40% of messages going to custom agents. Their AskProduct agent lets anyone query product documentation instantly, supported by a dozen AI champions creating team-specific workflows.
CMI Strategies achieved 95% adoption across 100 consultants with over 50% time savings on proposals (from 4-5 hours down to roughly 2 hours). The firm deployed 30+ specialized agents for research and qualitative analysis, processing interview transcripts while maintaining attribution.

💡 Looking for other examples? Explore more customer stories →

See Dust in action

Enterprise teams deploy AI agents differently than individual developers. Business teams need agents that connect to their existing tools and work across departments without requiring engineering resources to build and maintain them.

Watch this short product overview to see what Dust looks like in action:

Frequently asked questions (FAQs)

What is the difference between Claude Code agents and Dust?

Claude Code agents are developer tools that automate coding tasks like debugging, testing, and code review across terminal, IDE, and web-based environments. They operate within individual coding sessions and require Markdown configuration files with YAML frontmatter. Dust is an enterprise platform that deploys AI agents across business teams to automate workflows involving Slack, Notion, CRMs, and other company tools. Dust agents are built using a no-code interface and can be deployed to entire departments with role-based access controls across admin, builder, and member roles.

What are workflow agents used for in enterprise?

Workflow agents automate repetitive knowledge work that spans multiple business tools. Common enterprise use cases include customer support agents that search knowledge bases and draft responses based on ticket history, sales agents that pull CRM data and generate account summaries, onboarding agents that guide new employees through company documentation, and research agents that synthesize information from internal databases and external sources. Unlike coding agents that modify files and run tests, workflow agents query databases, send Slack messages, update spreadsheets, and coordinate actions across the tools teams already use daily.

Which type of AI agent is right for my team?

If your team writes code and works primarily in terminal environments, coding agents like Claude Code are built for that workflow. They help developers manage context during long coding sessions, isolate expensive operations, and automate testing and debugging tasks. If your team works in business tools like Slack, Notion, CRMs, or Google Workspace and needs to automate knowledge work across multiple platforms, workflow agents are the better fit.