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Agentic Software Development Life Cycle

The HyperShift team operates an Agentic Software Development Life Cycle (ASDLC). A framework that decouples distributed consumption of reusable agentic building blocks (skills, slash commands, sub-agents, knowledge bases) from the systems that orchestrate agent execution and LLM inference at scale.

This separation enables flexible implementation choices: the same building blocks power local Claude Code (or other agents) sessions that can run on a developer's laptop, centralized Prow CI jobs, ambient agents, long-running cluster workloads, etc.

graph TB subgraph "Building Blocks Layer" direction LR SK["Skills & Slash Commands — /jira:solve, /code-review:pr, ..."] AG["Sub-agents — HCP SMEs, Explore, Plan"] KB["Knowledge Bases — CLAUDE.md, AGENTS.md, Rules"] HK["Hooks & Plugins — Pre-commit, CI integration"] end subgraph "Orchestration / Execution Layer" subgraph Local["Local (Dev Laptop)"] direction LR L_CC["Claude Code CLI"] L_BM["Bot Minter"] end subgraph Centralized["Centralized (Shared Infra)"] direction LR C_PJ["Prow Jobs"] C_AM["Ambient"] end end SK --> Local AG --> Local KB --> Local HK --> Local SK --> Centralized AG --> Centralized KB --> Centralized HK --> Centralized style SK fill:#e8f4fd,stroke:#1a73e8 style AG fill:#e8f4fd,stroke:#1a73e8 style KB fill:#e8f4fd,stroke:#1a73e8 style HK fill:#e8f4fd,stroke:#1a73e8 style L_CC fill:#fce8e6,stroke:#d93025 style L_BM fill:#fce8e6,stroke:#d93025 style C_PJ fill:#fce8e6,stroke:#d93025 style C_AM fill:#fce8e6,stroke:#d93025

Phases of ASDLC

Phase 1 — Discovery

The discovery phase focuses on understanding the problem space, designing the solution, and refining scope into actionable work items.

flowchart LR subgraph "Architecture Design" A1["Interactive Claude session: discuss design and implementation"] --> A2["HCP SME sub-agents: cloud-provider, control-plane, data-plane, api, architect"] A2 --> A3["Create RFC or enhancement proposal"] end subgraph "Scope Refinement" B1["/jira:create — Create Jira issues"] --> B2["/jira:generate-test-plan — Generate test plan from Jira"] B1 --> B3["/utils:generate-test-plan — Generate test plan from PRs"] B1 --> B4["Obra superpowers"] end A3 --> B1

Architecture Design

Activity Building Block Description
Design discussion Claude Code / Other Agents interactive session Discuss design and implementation plans with Agents in a conversational session
Domain expertise HCP SME sub-agents Leverage specialized agents: cloud-provider-sme, control-plane-sme, data-plane-sme, api-sme, hcp-architect-sme
Proposal creation RFC / Enhancement Create a formal enhancement or RFC document capturing the agreed design

Scope Refinement

Activity Building Block Description
Issue creation /jira:create Create well-structured Jira issues (stories, bugs, tasks, epics)
Test planning (Jira) /jira:generate-test-plan Generate test steps from a Jira issue
Test planning (PRs) /utils:generate-test-plan Generate test steps for one or more related PRs

Phase 2 — Delivery

The delivery phase covers implementation, review, and quality assurance — each supported by agentic workflows.

flowchart LR subgraph "Implementation" C1["/jira:solve"] C2["Bot minter"] end subgraph "Code Review" D1["CodeRabbit — automated review"] --> D3["Address feedback"] D2["/code-review:pr — /code-review:pre-commit-review"] --> D3 D3 --> D4["/utils:address-reviews"] end subgraph "QE & Testing" E2["/ci:analyze-prow-job-test-failure"] E3["/agentic-qe test-plan"] end C1 --> D1 C2 --> D1 D4 --> E2

Implementation

Activity Building Block Description
Code generation /jira:solve Analyze a Jira issue and create a pull request with a proposed fix
Code generation Bot minter GitHub App-based agents that create and manage PRs programmatically

Code Review

Activity Building Block Description
Automated review CodeRabbit AI-powered code review bot running on every PR
On-demand review /code-review:pr Trigger a full PR review with language-aware analysis
Pre-commit review /code-review:pre-commit-review Review staged changes before committing
Address feedback /utils:address-reviews Automatically address PR review comments

QE & Testing

Activity Building Block Description
CI failure analysis /ci:analyze-prow-job-test-failure Analyze test failures from Prow CI job artifacts
Agentic QE /agentic-qe test-plan Execute test plans with agentic workflows
Presubmit auto-analysis Prow presubmits CI jobs automatically trigger failure analysis on test failures

Modes of Agentic Workflows

A mode describes where/how execution happens. Building blocks can be executed in different modes depending on the use case:

graph LR subgraph "Local" L1["Developer laptop — Claude Code CLI"] end subgraph "Centralized" P1["Prow periodic jobs"] P2["Prow presubmit jobs"] AM1["Ambient agents"] LR1["Long-running agents — prebuilt binaries as cluster workloads"] end L1 -.- BB["Shared — Building Blocks"] P1 -.- BB P2 -.- BB AM1 -.- BB LR1 -.- BB style BB fill:#e8f5e9,stroke:#34a853

Local

Building blocks executed by developers on their laptops using Claude Code CLI, Bot-Minter, others... This is the most interactive mode where removing ambiguity might need several iterations. Ideal for design discussions, exploratory work, and ad-hoc tasks.

Before publishing a code artifact for human review, developers are expected to levereage building blocks for local code review and agentic qe

Centralized

Building blocks run and driven by central infrastructure tools. These run on schedule, in response to events, or continuously as cluster workloads: Prow Jobs, Ambient, Cluster workloads... Ideal for repetitive tasks or concise work items.

Hands-off Delivery

flowchart TD A["Jira item labeled with issue-for-agent"] --> B["Periodic Job picks from backlog"] B --> C["Agent generates code, reviews, addresses and creates PR"] C --> D["Agent review on published PR"] D --> E["Human review"] E --> F["/address-pr-review"] F --> E E --> G["Human lgtm"] G --> H["/agentic-qe"] H --> I["Presubmit e2e runs"] I --> J["Auto-analysis on failures"] J -->|"Failures found"| F I -->|"Pass"| K["Merge"]

For details on the current centralized jobs, see AI-Assisted CI Jobs.

Monitoring

Dashboards

Agent execution metrics, token usage, and cost tracking

Slack Integration

Channel Purpose
@ship-help AI-assisted triage and routing of help requests
#project-hypershift General team channel

Building blocks integrate with Slack for notifications, status updates, and interactive assistance.

Principles

  • Reuse drives self-improvement — Every execution of a building block generates signal (successes, failures, review feedback) that feeds back into refining the building blocks themselves.
  • Raise the quality floor — Agentic workflows must enforce consistent code patterns, code review, test generation, and CI analysis on every change, raising the baseline quality across all contributions.
  • Compounding returns — Better building block specifications produce better output, which produces better input data for the next iteration. The system improves itself over time.

Getting Started

  1. Install Claude Code CLI
  2. Clone the HyperShift repository
  3. Install plugins:
/plugin marketplace add openshift-eng/ai-helpers
/plugin install jira@ai-helpers
/plugin install utils@ai-helpers
/plugin install ci@ai-helpers
/plugin install code-review@ai-helpers
  1. See AI-Assisted CI Jobs for existing Prow job setup
  2. Label Jira issues with issue-for-agent to submit them for processing