Interview Question: How to Leverage Claude Code’s Dynamic Workflows While Controlling Token Usage

What Is a Dynamic Workflow?

Dynamic Workflow enables an LLM to write its own orchestration script, breaking a large task into dozens or hundreds of parallel sub‑agents, executing them, and then validating the results.

The key phrase is “let the AI write itself.” Traditional multi‑agent setups require developers to predefine the number of agents, their responsibilities, and the data flow; Dynamic Workflow generates this plan on the fly based on the user’s goal.

Agent System Classification

Anthropic’s 2024 "Building Effective Agents" paper divides agentic systems into two categories:

Workflow : LLM‑tool collaboration is orchestrated by predefined code.

Agent : The LLM decides dynamically what to do next.

Within the Workflow category, five patterns are defined. Dynamic Workflow corresponds to the fourth pattern, Orchestrator‑Workers , where a central LLM (the orchestrator) dynamically creates and assigns tasks to worker agents.

How Dynamic Workflow Operates

The execution consists of several stages:

Task reception and planning: Claude analyses the input, determines the number of sub‑agents needed, and generates an orchestration plan.

Dispatch and execution: Up to 16 agents run in parallel, with a total limit of 1,000 agents.

Cross‑validation: Independent validator agents check each other’s outputs; adversarial agents may be spawned for high‑risk tasks.

Iterative correction: Failed agents are asked to fix their results until all outputs meet consistency criteria.

Result aggregation and delivery: The final validated output is returned to the user, with progress persisted for resumable execution.

Practical Case Study

Jarred Sumner used Dynamic Workflow to migrate the JavaScript runtime Bun from Zig to Rust in 11 days, generating roughly 750,000 lines of Rust code with a 99.8% test‑suite pass rate.

The migration involved several specialized workflows: one to map Zig structs to Rust lifetimes, another to parallelize file translation with dual review agents, a repair loop that iteratively builds, tests, and fixes errors, and a final optimization workflow that creates pull‑requests for manual review.

Model Capability vs. Platform Capability

Dynamic Workflow relies on both:

Model layer : task planning, code‑base understanding, tool invocation, and self‑correction.

Platform layer : lifecycle management of sub‑agents, concurrency limits, progress persistence, context isolation, and result aggregation.

Think of the model as a technical director’s brain and the platform as the project‑management infrastructure.

Comparison with Multi‑Agent Frameworks

Traditional frameworks (e.g., LangGraph, CrewAI) require developers to define agent roles and orchestration logic manually. Dynamic Workflow flips this: the developer states the goal, and Claude automatically creates roles, splits tasks, writes orchestration code, handles errors, and validates results.

Neither approach is universally superior; fixed pipelines suit repetitive tasks, while Dynamic Workflow shines for large, unpredictable projects such as codebase migrations or security audits.

How to Use Dynamic Workflow

Available via Claude Code CLI, desktop client, and VS Code extension, or through the API. Two activation methods:

Issue a natural‑language command, e.g.,

创建一个工作流，帮我审计整个代码库的安全漏洞

.

Enable the ultracode setting in the effort menu, which raises inference strength and auto‑detects suitable tasks.

The first run shows a plan for confirmation. Token consumption is significantly higher than a normal Claude session because dozens of agents are hired simultaneously; users should start with small tasks and scale up gradually.

Interview Takeaway

When asked “What is the essence of Dynamic Workflow?” answer that it is a platform product built on strong model capabilities—model‑driven planning and decision‑making combined with infrastructure that manages parallel agents, persistence, and result aggregation.

Understanding the five patterns from Anthropic’s "Building Effective Agents" paper is essential for AI‑application development interviews.