Agile SDLC Workflow for HITL + AI Agents
🔥 Efficient Agile SDLC Workflow to build & publish Runbooks PyPI 🥇
⛅ Expertise in developing modern cloud-native applications ⚡ and data analytics 🔥
🎯 This outlines how Agile SDLC with your enterprise team (1 HITL + AI Agents) to build and publish the CloudOps & FinOps runbooks automation system iteratively, safely, and with transparent governance 🧰
1. Team Composition & Workflow Setup
| Role | Work Focus | Interaction with Spec / Tasks |
| 👨✈️ HITL Manager | Strategic direction, priorities, stakeholder alignment | Prioritize spec backlog, approve exceptions |
| 🤖:AI Product-Owner | Value definition, backlog grooming | Draft spec proposals, manage spec backlog |
| 🤖:AI Cloud-Architect | High-level design, cross-module alignment | Produce plan layer, approve architectural spec |
| 🤖:AI DevSec Engineer | Policy-as-code, security review, risk scoring | Annotate spec risk, enforce control gates |
| 🤖:AI SRE Automation | Reliability, drift logic, safe automation | Validate detection / remediation specs |
| 🤖:AI Python Engineer | Code implementation, adapters | Generate module code & tests |
| 🤖:AI QA Specialist | Test coverage, regression, negative tests | Write test spec and ensure validation |
| 🤖:AI Data Architect | Metrics, telemetry, cost modeling | Define data contracts and instrumentation |
| 🤖:AI Document Engineer | ADRs, runbooks, PR/FAQ, spec docs | Produce narratives tied to spec docs |
All agents use a shared workspace (Git + Spec Kit + JIRA) and celebrate sprint cadence.
2. Spec-Driven Workflow
We adopt Spec-Driven Development with 3-phase workflow: Specify → Plan → Tasks using with GitHub Spec Kit / BMAD method 🆓 / AWS Kiro 💰.
Specify: PO (or HITL) writes the “what / why / acceptance criteria / risk metadata” spec
Plan: Architect designs architecture, module boundaries, interfaces
Tasks: Break into small, testable units (covered by AI agents)
Gates between phases must pass reviews: spec review, architecture review, test planning.
This ensures alignment, reduces rework, and makes assumptions explicit.
3. Architecture & Patterns for Cloud Foundation + Cost Optimization
🔄 MCP Integration Summary
| Automation | Connection |
| Agent → Spec-Kit | AI Agents consume spec files, generate implementation |
| Remediation → Runbooks API | All snapshots and actions logged |
| AWS MCP Servers | 🏗️ Infrastructure 💰 Cost & Operations Monitor: optimize & manage AWS infra and costs |
| Atlassian Jira / Confluence | Tickets & exceptions |
| Metrics → Vizro Analytics | Compliance trends, drift latency |
| Slack / Teams | Notifications / alerts |
OKRs, Metrics & Continuous Improvement
Each sprint contributes to OKRs (quarterly). Example OKRs for Runbooks:
KR1: Increase inventory coverage to 95% accounts
KR2: Drift remediation latency P95 ≤ 20 minutes
KR3: Cost savings via rightsizing ≥ 15%
KR4: False positives < 5%
Measure velocity, change failure rate, lead time, DORA metrics, module reuse, test coverage.
4. Publishing & Feedback Loop
After stable increment, package new modules, update CLI, publish to PyPI
Version bump, release notes, changelog
External teams adopt and provide feedback (bugs, feature requests)
Those feedback items become new spec proposals
5. References
📚 Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI
📚 AI Engineering: Building Applications with Foundation Models
