Use Cases

See how engineering teams use Agentic Dev Flow to solve real problems across the development lifecycle.

Team Onboarding

Engineering Manager

The Problem

Onboarding a new developer takes days of manual provisioning across 6+ tools -- creating accounts, setting permissions, sharing links, and verifying access.

The Solution

A single /onboard command provisions the new team member across Grafana, Slack, Jira, GitLab, Confluence, and Google Calendar. The self-service wizard handles the rest.

Commands Used

/onboard/help

Results

  • Onboarding time reduced from days to minutes
  • Zero manual account provisioning
  • Consistent permissions across all tools

Sprint Management

Scrum Master / Tech Lead

The Problem

Sprint ceremonies require pulling data from Jira, GitLab, and monitoring tools separately. Stand-ups waste time on status updates that could be automated.

The Solution

The /digest command aggregates Jira issues, GitLab MRs, pipeline status, and calendar events into one summary. Grafana dashboards show sprint velocity, completion rates, and DORA metrics in real time.

Commands Used

/digest/dashboard/issue

Results

  • Daily digest replaces manual status collection
  • Sprint metrics visible in Grafana without configuration
  • DORA metrics tracked automatically from CI/CD data

CI/CD Operations

DevOps Engineer

The Problem

When a pipeline fails, engineers dig through GitLab logs, identify the failing test, find the relevant code, and then context-switch to fix it. This takes 30+ minutes per failure.

The Solution

The /fix-ci command downloads the job trace, identifies the root cause, pinpoints affected files, and suggests fixes. The /pipeline command triggers new runs directly from Slack.

Commands Used

/fix-ci/pipeline/mr

Results

  • CI failure diagnosis in seconds, not minutes
  • Pipeline triggers without leaving Slack
  • MR status and diff stats at a glance

AI-Powered Development

Software Developer

The Problem

Implementing a Jira story requires reading acceptance criteria, writing code, running tests, creating commits, updating the issue, and documenting changes -- all manually.

The Solution

The /story command reads the Jira story, decomposes it into tasks, implements each one with atomic commits, runs tests, and updates Jira status. The /research command provides deep, multi-step research on any technical topic.

Commands Used

/story/research/confluence

Results

  • Stories implemented end-to-end by AI agents
  • Deep research with structured reports and sources
  • Automatic documentation and Jira updates

Observability & Metrics

Platform / SRE Team

The Problem

Setting up dashboards, defining metrics, and configuring alerts takes weeks. Teams fly blind during early development phases.

The Solution

Connecting Grafana provisions 6 dashboards with 117 panels automatically -- sprint analytics, service reliability, runtime health, release readiness, historical trends, and team performance. 37 Prometheus metrics are collected and pushed every 5 minutes.

Commands Used

/dashboard

Results

  • Zero-config dashboard provisioning
  • 117 panels covering sprints, DORA, CI/CD, and runtime
  • Historical trends stored in DynamoDB with 90-day retention

Documentation & Knowledge

Technical Writer / Full Team

The Problem

Documentation lives in Confluence but nobody remembers to update it. Changelogs are written manually. API docs go stale after every release.

The Solution

The CI/CD pipeline auto-generates TypeDoc API docs and Doxygen call graphs on every merge. The /confluence command creates and links pages from Slack. The lifecycle framework keeps Confluence pages in sync with Jira stories.

Commands Used

/confluence

Results

  • API docs regenerated on every pipeline run
  • Confluence pages linked to Jira stories automatically
  • Changelogs with clickable GitLab changeset URLs

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