Why Do Claude Code’s Dynamic Workflows Burn So Many Tokens? – Interview Insights

What Is 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 and then aggregating and validating the results.

Agent System Classification

Anthropic classifies agentic systems into two categories: Workflow (pre‑defined code orchestration) and Agent (LLM decides actions at runtime). Within Workflow, five patterns are defined, and Dynamic Workflow corresponds to the fourth pattern, Orchestrator‑Workers , where the orchestrator is generated dynamically by the model.

How Dynamic Workflow Works

The execution proceeds in three stages:

Task reception and planning – Claude analyses the input, determines the number of sub‑agents needed, and creates a real‑time orchestration plan.

Distribution and execution – up to 16 sub‑agents run in parallel (max 1,000 total), each handling its assigned sub‑task.

Cross‑validation – independent validator agents check each other's outputs; adversarial agents may be spawned for high‑risk tasks. Failed checks trigger a repair loop that repeats until consistency criteria are met.

The orchestration runs outside the main conversation context, preventing the main chat window from being overwhelmed by large‑scale tasks.

Practical Example

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

Mapping Zig struct fields to Rust lifetimes.

Parallel translation of .zig files to .rs files, with dual review agents per file.

Iterative build‑test‑repair cycles until the project compiled and passed all tests.

A final workflow that eliminated unnecessary data copies and created pull‑requests for manual review.

Is It Model Capability or Tool Capability?

The answer is both: the model provides intelligence (task planning, context understanding, tool invocation, self‑correction) while the platform supplies infrastructure (agent lifecycle management, concurrency limits, progress persistence, context isolation, result aggregation).

Difference from Multi‑Agent Frameworks

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

Both approaches have trade‑offs: fixed pipelines suit highly standardized tasks, while Dynamic Workflow shines for one‑off, large‑scale, unpredictable workloads such as codebase migrations or security audits.

How to Use

Dynamic Workflow is available via Claude Code’s CLI, desktop app, VS Code extension, and API. It can be triggered by typing a request (e.g., “Create a workflow to audit the entire codebase for security vulnerabilities”) or by enabling the ultracode setting, which raises inference effort and lets Claude decide when to switch to workflow mode.

First‑time runs present a plan for confirmation. Token consumption is significantly higher than normal sessions because many sub‑agents are employed; users are advised to start with small tasks and scale up gradually.

Conclusion

Dynamic Workflow represents a shift from “developers orchestrate agents” to “agents orchestrate agents,” moving the developer’s role toward defining objectives and reviewing outcomes. Understanding the five patterns from Anthropic’s “Building Effective Agents” blog is essential for AI application development interviews.