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To build a GitHub Copilot incident-response workflow you can debug, make the workflow code define the sequence, permissions, handoffs, and result formats; use agents only for bounded analysis that needs judgment. Start by collecting authorized logs and telemetry, send separate evidence sets to specialized agents, require structured findings, and consolidate those findings into a timeline and root-cause report. Add visible phases, resource limits, and human checkpoints so an operator can inspect, pause, or resume a run.
GitHub documents service interruption as an example of dynamic workflow use. That example describes a possible workflow pattern, not proof of production deployment or independently measured incident-response performance. Dynamic workflows are in public preview and may change.
What makes a Copilot workflow debuggable?
GitHub Docs defines a dynamic workflow as “a program that defines how a task is carried out.” The workflow author specifies steps, conditions, handoffs, and how results move between steps; agents perform work that requires analysis or judgment. A workflow can run steps sequentially, in parallel, or in a mix of both. See GitHub’s dynamic workflows documentation.
For incident response, this division matters: orchestration should make the investigation legible, while agents should have bounded assignments. If a run fails or produces a weak conclusion, you need to see which stage ran, what evidence it received, and what it returned—not just a final answer from an opaque conversation.
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- Deterministic orchestration: specify evidence sources, stages, conditions, and the expected handoff between stages.
- Bounded agent judgment: assign each agent a distinct system or evidence set and a focused analysis question.
- Inspectable outputs: require a stable structure that later stages and people can interpret.
- Operational controls: expose progress and limits, with a human able to inspect or stop the run.
How to structure an incident-response workflow
GitHub’s documented service-interruption example gathers logs and telemetry, assigns agents to analyze different systems, then combines structured findings into a timeline and root-cause report. The stages below turn that example into a practical starting design. Explicit boundaries and a small initial scope are implementation advice, not a GitHub-mandated incident procedure.
1. Gather authorized evidence
Make the first stage collect the relevant service logs and telemetry through sources the workflow is allowed to access. Keep collection separate from interpretation: this makes it easier to distinguish missing or inaccessible evidence from an agent’s analysis of evidence it actually received.
2. Dispatch focused analysis tasks
Divide the evidence by system or source, then give each agent a bounded question. For example, one agent could examine application logs while another examines infrastructure telemetry. Avoid asking every agent to investigate the entire incident independently; overlapping, unbounded assignments make results harder to reconcile.
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Specify a result format before dispatch. A useful structure can include the system examined, relevant observations, time references, supporting evidence, uncertainties, and any next question. The exact fields are your design choice; the important point is that downstream steps should not have to guess how to interpret each response.
Do not treat a missing field or unsupported conclusion as established fact. Preserve uncertainty in the handoff so the consolidation stage can distinguish evidence from inference.
4. Consolidate findings into an incident record
Have a later stage combine the structured results into a chronological timeline and a root-cause report. The report should make clear which findings support its explanation and where evidence is incomplete or conflicting. A report generated by the workflow is an aid to investigation, not by itself confirmation of a cause.
5. Make progress and stopping points visible
Give phases meaningful names and provide status updates at stage boundaries. Define where a person can inspect results before the workflow proceeds to a consequential next step. GitHub documents monitoring, limits, pausing or canceling runs, and resuming eligible runs; use those controls as part of the operating design rather than relying on a long, uninterrupted run.
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When a result is incomplete or wrong, trace the workflow stage by stage. Check whether evidence collection completed, whether each agent received the intended evidence, whether its output followed the required structure, and whether consolidation preserved conflicts and uncertainty. Meaningful phase reporting and stable handoff formats make these checks practical.
Monitor active subagents and AI-credit use through the run interface or CLI. If OpenTelemetry is configured, GitHub creates an invoke_workflow span for each run or resume and links it to agent activity. Inspect that span and its linked activity to connect overall workflow execution with individual agent work. See GitHub’s instructions for using dynamic workflows.
Limits are optional, but GitHub recommends setting them to control AI-credit use. Start with limited scope when validating a new workflow. A run that reaches a configured limit can be paused or stopped and may be resumed after the limit is increased; eligibility to resume is not guaranteed for every run.
Where custom agents and hooks fit
Custom agents and hooks solve different problems. A custom agent packages a specialized prompt, available tools, and MCP servers for a bounded role. Hooks run shell commands at selected points in an agent workflow, such as around sessions, prompts, tool use, or errors. Use an agent to perform a role-specific analysis; use a hook when a repeatable command or check should run at a defined execution point.
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Hooks can support diagnostics or automated checks around execution. For cloud-agent hook configuration, the configuration must exist on the repository’s default branch. See GitHub’s guide to customizing agent workflows with hooks. For agent configuration concepts, see About custom agents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a workflow client for the way you operate
| Client | Best fit | Setup and operational consideration |
|---|---|---|
| Copilot app | Interactive work in a local session where an operator wants to create and monitor a workflow. | Workflow creation may request approval. The documented run-monitoring feature applies to local sessions. |
| Copilot CLI | Command-line use and programmatic invocation. | Dynamic workflows require experimental features, enabled with --experimental or /experimental on. Programmatic CLI invocation does not show permission-approval prompts, so grant required tool and URL permissions before starting. |
| Copilot SDK | Embedding agent orchestration in a software-driven workflow. | Use the SDK’s documented custom-agent and sub-agent mechanisms when you need isolated roles and lifecycle events in a parent session. |
Dynamic workflows are documented as available in the Copilot app, CLI, and SDK, subject to GitHub’s plan eligibility caveat. Eligibility and billing details can change, so check the current feature documentation before planning around a particular plan.
Share a workflow and manage its permissions
A workflow created in a session is session-local by default. To reuse or share it, put the extension in personal or project extensions. A project workflow becomes available to teammates after the repository change is shared and their extension state is reloaded.
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For interactive creation, be prepared to approve requested permissions. For CLI programmatic runs, arrange the necessary permissions explicitly in advance because the invocation does not present approval prompts. Grant only access needed for the workflow’s evidence sources and tools.
Dynamic workflows versus autopilot and fleet
| Approach | Who defines the process? | What to expect |
|---|---|---|
| Dynamic workflow | The author defines reusable steps and handoffs. | The path can still vary with inputs and findings, but the orchestration is authored and inspectable. |
| Autopilot | The system decides what to do next. | It focuses on autonomous continuation rather than an author-defined sequence. |
| Fleet | Copilot decides how to divide and coordinate delegated work for a run. | It encourages delegation, while a dynamic workflow makes the authored process reusable. |
For a repeatable incident investigation where engineers need to inspect stage boundaries and handoffs, an authored dynamic workflow gives more explicit process control. It does not make agent analysis deterministic or guarantee a correct root-cause conclusion.
Preview status and evidence limits
GitHub documents dynamic workflows as a public preview, so behavior and instructions are subject to change. The service-interruption scenario demonstrates how GitHub describes the feature, but the documentation does not establish that this exact workflow is deployed in production or provide independently tested measures of incident-resolution speed, accuracy, or savings. Build and validate the workflow against your own permissions, evidence sources, and operational requirements.
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