Dynamic Workflows
动态工作流（Dynamic Workflows）是一种计算与任务编排范式，指在运行时根据实际需求动态生成、调整和调度任务执行路径，而非依赖预先固定的流程结构。其核心特征包括：支持多个子任务或子代理的并行协调、能够根据中间结果自适应地改变执行逻辑，以及灵活应对不确定性或复杂依赖关系。广泛应用于AI代理系统、自动化流程和分布式计算场景中。
Timeline (last 90 days)
The record-replay mechanism behind resumability is consistent with the idempotent replay design approach in workflow engines (such as Temporal and Airflow): write the outputs of completed steps into state storage, and during replay, reuse the cached result and skip execution when a record is hit.
The official guidance gives the criteria: tasks suitable for workflows include tasks with wide fan-out plus a merge step, staged work, and mechanical batch modifications; what suits ordinary sessions is situations that require only a few sessions and where the tasks are tightly coupled.
Pipeline mode draws on the pipeline concept in software engineering, sets no global barrier between stages, and is better suited to batch jobs where there are clear sequential dependencies within each project but the projects are independent of one another, which can improve resource utilization and overall throughput.
Parallel mode is a barrier-style synchronization: all parallel branches must complete before the merge step can start, and any branch slowing down holds back the whole, making it suitable for scenarios requiring horizontal alignment, deduplication, and unified sorting.
Devin launched a feature called Dynamic Workflows, which uses Python scripts to orchestrate multiple Devin sessions collaborating in parallel to handle complex tasks.
Dynamic Workflows provides two core orchestration modes: Parallel mode (waiting for all parallel results to return before entering the merge step) and Pipeline mode (each project independently flows through its own stages).
Every agent call in Dynamic Workflows is recorded, and a run after an interruption replays completed agents and only re-executes unfinished work, thereby supporting resumability.
In the official demo, for an accessibility audit task with 12 components, Devin planned 12 review agents plus one consolidation step, adopted Parallel mode, and finally merged them into a report containing 10 deduplicated findings sorted by severity.
Dynamic Workflows work by having a lead agent first write a plan, then a server schedules multiple agents to execute it in stages, with a budget cap controlling the total cost of each run
Dynamic Workflows are designed for large-scale batch processing tasks; for single tasks that a single agent can complete, the traditional approach is simpler and more straightforward
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All Facts (10)
The record-replay mechanism behind resumability is consistent with the idempotent replay design approach in workflow engines (such as Temporal and Airflow): write the outputs of completed steps into state storage, and during replay, reuse the cached result and skip execution when a record is hit.
50%UnverifiedEvery agent call in Dynamic Workflows is recorded, and a run after an interruption replays completed agents and only re-executes unfinished work, thereby supporting resumability.
50%UnverifiedThe official guidance gives the criteria: tasks suitable for workflows include tasks with wide fan-out plus a merge step, staged work, and mechanical batch modifications; what suits ordinary sessions is situations that require only a few sessions and where the tasks are tightly coupled.
50%UnverifiedPipeline mode draws on the pipeline concept in software engineering, sets no global barrier between stages, and is better suited to batch jobs where there are clear sequential dependencies within each project but the projects are independent of one another, which can improve resource utilization and overall throughput.
50%UnverifiedParallel mode is a barrier-style synchronization: all parallel branches must complete before the merge step can start, and any branch slowing down holds back the whole, making it suitable for scenarios requiring horizontal alignment, deduplication, and unified sorting.
50%UnverifiedDynamic Workflows provides two core orchestration modes: Parallel mode (waiting for all parallel results to return before entering the merge step) and Pipeline mode (each project independently flows through its own stages).
50%UnverifiedDynamic Workflows work by having a lead agent first write a plan, then a server schedules multiple agents to execute it in stages, with a budget cap controlling the total cost of each run
50%UnverifiedDynamic Workflows are designed for large-scale batch processing tasks; for single tasks that a single agent can complete, the traditional approach is simpler and more straightforward
50%UnverifiedDevin launched a feature called Dynamic Workflows, which uses Python scripts to orchestrate multiple Devin sessions collaborating in parallel to handle complex tasks.
50%UnverifiedAnthropic's Managed Agents has launched a public beta of Dynamic Workflows
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