GitHub has introduced Dynamic Workflows in Copilot CLI, the GitHub Copilot app and the Copilot SDK. The feature lets developers define an orchestration in code while AI agents handle the parts of a workflow that require analysis or judgment.
The important change is that the workflow itself becomes a programmable object. Instead of asking an agent to decide everything from start to finish, developers can define stages, dependencies, parallel work, checkpoints and structured handoffs, then use agents where reasoning is actually useful.
What a Dynamic Workflow is
GitHub describes a dynamic workflow as a program that defines how a task is carried out. Some steps can be deterministic commands or service calls. Other steps can invoke one or more agents. The workflow can run steps sequentially, in parallel, or through a mixture of both.
This creates a useful separation of responsibilities. Code controls the process. Agents handle the parts that need interpretation, investigation or judgment.
For example, an incident investigation could collect logs and telemetry using deterministic tools, send separate questions to different agents, then combine the structured findings into a timeline and root-cause report.
Why this is different from free-form agent delegation
A general-purpose agent can be told to solve a large task and decide what to do next. That flexibility is useful, but it can also make long workflows difficult to observe and reproduce.
Dynamic Workflows move some of that control back into code. Developers can specify what must happen, where agents are allowed to participate, what information is passed between stages, and where a person can review the result.
The result is closer to a software workflow with AI components than a chatbot with a long prompt.
Where Dynamic Workflows can help
- Release checks: run deterministic tests, ask an agent to investigate failures, then pause for review.
- Pull-request review: inspect many changed files in parallel and combine structured findings.
- Codebase sweeps: search a large repository for a pattern and have agents assess the results.
- Research and implementation: gather evidence, create a plan, then move into implementation.
- Long-running jobs: start an expensive workflow, pause it, inspect the output and resume later.
These examples have one thing in common: the process has recognizable stages and benefits from repeatability.
Why structured handoffs matter
Multi-agent systems often become difficult to debug when one agent produces free-form text for another agent. A downstream model may interpret the text differently on every run.
A workflow can instead define structured results. One stage might return a list of failing tests, another might return suspected causes, and a later stage might receive only those fields.
That makes the system easier to test. Developers can validate whether each stage returned the expected structure before allowing the next stage to continue.
Checkpoints add human control
GitHub says Dynamic Workflows can pause at a checkpoint so a user can review results and resume when ready. This is important for workflows that can create external side effects.
A good design should not require human approval for every harmless step. But actions such as merging code, deleting data, changing production configuration or sending external communications may deserve a checkpoint.
The workflow can therefore separate analysis from authorization. An agent can recommend an action without automatically receiving permission to perform it.
Dynamic Workflows and AI coding agents
Coding agents are increasingly capable of editing files, running commands and coordinating multiple tasks. The engineering challenge is shifting from “Can the agent write the code?” to “Can the team control the complete process around the agent?”
Dynamic Workflows are one answer. A team can define a repeatable sequence such as inspect issue, collect repository context, implement change, run tests, review the diff, and prepare a pull request.
The agent can perform reasoning-intensive steps, while the workflow ensures that important deterministic checks are not skipped.
How to design a safe workflow
- Start with a deterministic skeleton. Define the stages that should always happen.
- Use agents selectively. Invoke them where interpretation or reasoning adds value.
- Define structured outputs. Make downstream inputs predictable.
- Add validation after important stages. Do not assume an agent returned a correct result.
- Limit permissions. Give each step only the tools it needs.
- Add checkpoints for high-impact actions. Keep human approval where it matters.
- Log the workflow. Capture enough information to reproduce failures.
When a normal prompt is better
Not every task needs an orchestrated workflow. GitHub explicitly positions Dynamic Workflows for processes that are reusable or have clear stages, checks or limits.
If someone wants a quick explanation, a small code change or a simple answer, a normal Copilot interaction is usually simpler. Building a workflow around a one-minute task can create more engineering overhead than value.
What developers should measure
Teams adopting agent workflows should measure more than completion rate. Useful metrics include time to completion, number of failed stages, human intervention, retry frequency, tool-call errors, escaped defects and compute cost.
Also measure observability. A workflow that finishes quickly but cannot explain why it made a wrong decision is difficult to operate at scale.
The bigger change
Dynamic Workflows reflect a broader shift in AI software engineering. Developers are moving from writing every individual step to designing systems in which deterministic software and probabilistic agents work together.
The strongest architecture is unlikely to be fully autonomous or fully scripted. It will use code for the parts that should be predictable and agents for the parts that genuinely benefit from flexible reasoning.