From Monolithic Intelligence to Collective Collaboration: The Core Mechanism of Dynamic Workflows
On June 11, 2026, AI company Anthropic announced a major new feature for its coding model, Claude Code—Dynamic Workflows. Currently in a research preview, this feature is designed to fundamentally change how complex software engineering tasks are handled, shifting the industry’s focus from the capabilities of a single model to the coordinated management of a large number of specialized AI agents.
The core mechanism of Dynamic Workflows is that when faced with a large or complex user goal, Claude no longer attempts to solve it in a single process. Instead, it transitions into a ‘project manager’ role. Based on the task requirements, it dynamically generates a coordination script, breaking down the large task into multiple sub-tasks that can be processed in parallel. The system then assigns these sub-tasks to different specialized sub-agents for execution. Finally, the main system compares, validates, and integrates the results returned by each agent, iterating until the results converge to form a comprehensive and reliable final answer. This approach overcomes issues like context loss and logical interruptions that can occur when a single agent handles tasks that take hours or even days.
Optimized for Complex Engineering Scenarios
The introduction of Dynamic Workflows specifically targets complex scenarios that traditionally require an entire engineering team to spend significant time on coordination and supervision. Its applications include, but are not limited to:
- Large-scale codebase analysis: For instance, investigating a widespread security vulnerability in a massive project or conducting a comprehensive performance review.
- System-level migration and refactoring: Managing a large-scale migration from one technology stack to another, or performing an in-depth analysis and refactoring of a complex software project’s architecture.
- Security and compliance audits: Executing detailed security audits to ensure code complies with specific industry standards or security protocols.
Unlike ‘agent teams’ that rely on manual configuration by developers, the advantage of Dynamic Workflows lies in its on-demand, automated nature. The system manages the entire process from planning, task decomposition, and assignment to final result validation, significantly enhancing efficiency and autonomy in handling large-scale engineering problems.
Activation and Usage Recommendations
Users can enable this feature in two ways: one is through a new setting called “ultracode,” which authorizes Claude to autonomously decide when a workflow approach is the best way to solve a problem; the other is by explicitly instructing Claude to create a workflow in the conversation. A key engineering practice is that the workflow’s execution progress is saved. This means that even if a user’s session is interrupted, they can resume from the point of interruption without starting over, which is crucial for long-running tasks.
Anthropic also notes that because this feature coordinates multiple agents working in parallel, its token consumption can be significantly higher than in a standard Claude Code session. Therefore, it is officially recommended that users start with well-defined, smaller-scale tasks to familiarize themselves with its operational model and resource consumption before applying it to large projects.
Availability and Industry Trends
Currently, the Dynamic Workflows feature is available as a research preview to users on the Claude Code Max and Team plans, as well as some eligible Enterprise users. Developers can also access the feature via the Claude API and through major cloud platform partners like Amazon Bedrock, Google Vertex AI, and Microsoft Foundry.
This release reflects a significant trend in the artificial intelligence field: the industry’s focus is shifting from pursuing the ultimate performance of a single model to building multi-agent systems capable of collaborative work. Through effective agent coordination, the application potential of AI is no longer limited to Q&A or single-step generation but extends to autonomously planning and executing complex, multi-step systemic engineering tasks. This suggests that AI is poised to play a more central and autonomous role in the software development field.
