# Agile SDLC Workflow for HITL + AI Agents

> 🎯 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 🧰

%[https://youtu.be/B75p1-x_DdI] 

## 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**.

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## 2\. Spec-Driven Workflow

We adopt **Spec-Driven Development** with 3-phase workflow: **Specify** → **Plan** → **Tasks** using with [GitHub Spec Kit](https://github.com/github/spec-kit) / [BMAD method](https://github.com/bmad-code-org/BMAD-METHOD) 🆓 / [AWS Kiro](https://kiro.dev/) 💰.

1. **Specify**: PO (or HITL) writes the “what / why / acceptance criteria / risk metadata” spec
    
2. **Plan**: Architect designs architecture, module boundaries, interfaces
    
3. **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

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1759981217251/ad95b49c-6225-428c-b746-79419604bb90.png align="center")

### 🔄 MCP Integration Summary

| **Automation** | **Connection** |
| --- | --- |
| Agent → [**Spec-Kit**](https://github.com/github/spec-kit) | AI Agents consume spec files, generate implementation |
| Remediation → [Runbooks API](https://pypi.org/project/runbooks/) | All snapshots and actions logged |
| [**AWS MCP Servers**](https://awslabs.github.io/mcp/) | 🏗️ Infrastructure 💰 Cost & Operations Monitor: optimize & manage AWS infra and costs |
| [Atlassian Jira / Confluence](https://www.atlassian.com/platform/remote-mcp-server) | Tickets & exceptions |
| Metrics → [Vizro Analytics](https://vizro.readthedocs.io/projects/vizro-mcp) | Compliance trends, drift latency |
| Slack / Teams | Notifications / alerts |

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### 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.

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## 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
    

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## 5\. References

* 📚 [Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI](https://www.amazon.com.au/Rewired-McKinsey-Guide-Outcompeting-Digital/dp/1394207115)
    
* 📚 [AI Engineering: Building Applications with Foundation Models](https://www.amazon.com.au/AI-Engineering-Building-Applications-Foundation/dp/1098166302)
    
* 📚 [AWS for Solutions Architects: Design and scale secure AWS architectures with GenAI strategies and real-world patterns](https://www.amazon.com.au/AWS-Solutions-Architects-definitive-Architecture/dp/1836641931)
    

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