Claude Dispatch is Anthropic’s orchestration layer for multi-agent workflows. It coordinates how multiple Claude agents work together on tasks that are too long, too complex, or too multi-step for a single agent invocation to handle reliably.
The problem it solves is real. Running a single Claude instance on a ten-step research, analysis, and writing task produces drift. The model loses thread, accumulates errors, or simply hits context limits. Dispatch structures the work differently: breaking it into coordinated agent calls, managing state between them, and routing outputs where they need to go.
A single agent invocation is a sprint. Dispatch is a relay: each agent picks up cleanly where the last one finished.
How Dispatch differs from running agents manually
The manual multi-agent problem
Without Dispatch, developers building multi-agent systems write the orchestration themselves. This means:
- Defining how agent A’s output becomes agent B’s input
- Managing state across agent calls
- Handling failures and retries
- Sequencing parallel vs. sequential steps
- Logging what each agent did for debugging
This is not AI work. It is glue code. And it accumulates quickly on complex pipelines.
What Dispatch handles
Dispatch provides the orchestration infrastructure so developers define the task logic rather than the plumbing. The key components:
Task decomposition: defining a long-horizon task as a set of coordinated subtasks, each handled by an agent with appropriate context and scope.
State management: passing relevant outputs from one agent to the next without requiring the developer to manually serialize and deserialize state between calls.
Parallel execution: running independent subtasks simultaneously rather than sequentially, reducing total task time.
Failure handling: defining retry behavior and fallback paths when an individual agent call fails, without bringing down the entire pipeline.
Orchestration approaches compared
| Approach | What you manage | What the platform manages | Best for |
|---|---|---|---|
| Single agent call | Task prompt | Nothing | Simple, contained tasks |
| Manual multi-agent | State, routing, retries, sequencing | Nothing | Full control, custom needs |
| Claude Dispatch | Task definitions, agent logic | State, routing, retries, coordination | Complex pipelines, long-horizon work |
| Third-party orchestration (LangGraph, CrewAI) | Agent definitions, graph structure | Flow execution | Teams already using these frameworks |
Dispatch sits between full manual orchestration and no orchestration. It handles the plumbing while leaving task logic to the developer.
Use cases where Dispatch is the right tool
Long-horizon research tasks
A task like “research the regulatory landscape for this product category in five markets, summarize each, compare the key differences, and flag the top three compliance risks” is too long for a reliable single-agent run. Dispatch breaks it into coordinated research agents per market, a synthesis agent, and a risk-identification agent. Each receives focused context. The outputs chain correctly.
Multi-step content pipelines
Content pipelines that require research, drafting, editing, SEO optimization, and formatting involve different task types with different quality criteria. Running them as a Dispatch pipeline means each step runs at appropriate depth without the accumulated drift of a single long conversation.
Data processing workflows
Analyzing a large dataset, segmenting it, running category-specific analysis, and producing a consolidated report maps well to a Dispatch architecture. Independent segments run in parallel. The consolidation agent runs on their combined outputs.
Automated report generation
Weekly or monthly reports that draw from multiple data sources, require source-specific summarization, and must be synthesized into a consistent format benefit from coordinated multi-agent execution over a single large prompt.
Current availability status
Note: Dispatch’s specific release status, API availability, and feature set evolve. Verify the current state at docs.anthropic.com before building against it. As of mid-2026, Anthropic has been developing Dispatch as part of its broader agent infrastructure. Check the official documentation for current access options, pricing, and supported workflow types.
Frequently asked questions
Is Claude Dispatch the same as the Claude Agents API?
Dispatch is part of Anthropic’s broader agent infrastructure, which includes the Agents API. The distinction between specific product names and API endpoints evolves as Anthropic’s agent capabilities mature. Treat the official documentation as the authoritative source for current naming and API structure.
How does Dispatch compare to LangGraph or CrewAI?
LangGraph and CrewAI are third-party orchestration frameworks that work with multiple AI providers. Dispatch is Anthropic’s native orchestration layer, tightly integrated with Claude’s capabilities. Teams already invested in LangGraph or CrewAI retain flexibility across providers. Teams building Claude-native pipelines may find Dispatch’s integration advantages outweigh the portability tradeoff.
Does Dispatch require a specific Claude tier?
Dispatch is aimed at API users building production systems, not at Claude.ai subscribers using the chat interface. Verify the current tier requirements and pricing at docs.anthropic.com.
What happens when one agent in a Dispatch pipeline fails?
Dispatch includes failure handling configuration. Developers define retry behavior, fallback paths, and whether a failed subtask halts the pipeline or allows the rest to continue with a flagged gap. The specific failure handling options depend on the current API version.
Ready to build reliable long-horizon AI workflows?
Multi-agent orchestration makes tasks possible that single-agent calls handle poorly. Dispatch provides the infrastructure so the development work stays focused on task logic rather than coordination plumbing.
Path one: explore the API yourself. If you are already building with the Claude API, the agent infrastructure documentation at docs.anthropic.com covers current Dispatch capabilities and quickstart examples. The place to start is a two-agent pipeline on a task you currently run as a single long prompt.
Path two: work with Phos AI Labs. We design and build multi-agent pipelines for operations teams, handling architecture, prompt engineering, failure handling, and integration with your existing data and tools. Talk to us here.