Agile Workflow for HITL and AI Agents Integration

🎯 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.

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

Agile SDLC Workflow for HITL + AI Agents

Comments

🥇 Agentic AI

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1. Team Composition & Workflow Setup

2. Spec-Driven Workflow

3. Architecture & Patterns for Cloud Foundation + Cost Optimization

🔄 MCP Integration Summary

OKRs, Metrics & Continuous Improvement

4. Publishing & Feedback Loop

5. References

Command Palette

Comments

🥇 Agentic AI

ADLC Framework: Enterprise AI Agent Governance for Multi-Cloud DevSecOps

More from this blog

1. Team Composition & Workflow Setup

2. Spec-Driven Workflow

3. Architecture & Patterns for Cloud Foundation + Cost Optimization

🔄 MCP Integration Summary

OKRs, Metrics & Continuous Improvement

4. Publishing & Feedback Loop

5. References