The problem with most AI coding agents isn't that they fail outright - it's that they succeed at the wrong plan. Anthropic introduced dynamic workflows in Claude Code to address exactly this: the coding agent can now revise its approach mid-task instead of locking in a fixed sequence of steps at the start.
For real projects involving multiple files, unclear dependencies, or steps whose scope only becomes clear after earlier steps complete, rigid task sequences break down. With dynamic workflows, Claudee Code](/tools/claude-code/) can adapt - if it discovers something unexpected partway through a task, it adjusts what comes next rather than pushing ahead with an outdated plan.
This brings Claude Code closer to how developers actually work. Outlining an approach, hitting something unexpected, and revising is the normal workflow for any non-trivial coding task. An agent that does the same is more useful on large codebases and ambiguous problems - not just the small, well-scoped ones where current coding agents already perform reliably.
The feature is live now. For developers using Claude Code on multi-step tasks, the main benefit is fewer cases where the agent completes every step correctly but produces the wrong result because its original plan was wrong from the start.