A Full AI Engineering Team — Assembled, Governed, and Operational in One Session
AI-01
Instinct SRE needed an engineering team capable of building a multi-repo, multi-environment platform with professional DevOps practices. The founder could not hire 14 people and needed to move fast without sacrificing engineering quality.
A 14-agent AI team, fully operational, governed by a published operating manual, with defined ownership, escalation paths, and daily operating protocols — ready to execute across all 4 repositories from day one.
We didn't hire a team. We designed one — 14 specialists, each with a defined role, an operating manual, and a clear escalation path. They were writing code, closing issues, and managing a sprint board within the same session we brought them online.
Multi-Agent Parallel Execution — 4 Repos, Simultaneous
AI-01
AI-03
Building a company's technical foundation requires simultaneous work across infrastructure, application code, marketing website, and AI tooling. In a traditional team, this requires coordination meetings, handoff delays, and sequential execution.
Parallel execution across all four repositories in a single sprint. Work that would typically require 3–4 weeks of sequential sprint cycles was compressed into days.
While the architect was designing the tenant migration strategy, the infrastructure engineer was already executing it — and the platform engineer was hardening the API. No standup. No meeting request. No waiting. That is what multi-agent parallelism looks like in practice.
Security Vulnerability Detection — Autonomous, Unprompted
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SEC-01
SEC-02
A Cloudflare API token had been committed to prod.tfvars in the infrastructure repo. Additionally, the platform API had a CORS wildcard accepting requests from any domain. Neither issue was the focus of any assigned GitHub Issue.
Three security issues found, documented, remediated, and prevented from recurring — none of them assigned as tasks. The agents identified the problems on their own and resolved them without requiring the founder to run a security sprint.
Our QA agent wasn't asked to find security issues. Our governance agent wasn't asked to check prod.tfvars for secrets. They just did — because that's what good engineers do. The token was rotated within the same session it was found.
Drift Detection Product — Built by the Agents, Running on Live Infrastructure
AI-01
SRE-08
Instinct SRE's first product is the Terraform Drift Detector. The irony: the company's own infrastructure had no drift detection. Vegeta identified this as a critical gap: "This repo must showcase drift detection before we sell it to customers."
Drift detection running hourly against 2 active Azure environments. Zero manual checks required. The Terraform Drift Detector product's first reference implementation exists and is live on Instinct SRE's own infrastructure.
Our infrastructure engineer noticed we were building a drift detection product but had no drift detection on our own infrastructure. He built it himself, wrote the spec first, and shipped it in the same sprint. It runs every hour. We dogfood what we sell.
Azure Tenant Migration — Complex Identity Work, Zero Downtime
AI-01
CLD-01
All three Instinct SRE Azure subscriptions lived in the wrong tenant — a personal enrollment tenant rather than the company's M365 tenant. Leonardo was a guest user (EXT) in his own production infrastructure, with limitations on PIM eligibility, ARM policy, and Cost Management access.
Three Azure subscriptions transferred to the correct tenant, OIDC re-configured, RBAC re-assigned, CI pipelines repaired, and Terraform Apply green — with zero resource destruction and zero production downtime.
Moving three Azure subscriptions across tenants without destroying resources or breaking CI is the kind of work that makes experienced engineers nervous. Architecture decisions and infrastructure execution happened in the same session. The pipelines were green by end of sprint.
SDLC Documentation Library — 7 Professional Documents, One Session
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AI-02
A company building software products needs a minimum SDLC documentation set to operate professionally. For Instinct SRE to deliver consulting engagements and sell against larger firms, the operational scaffolding needed to be at enterprise quality from day one.
A complete SDLC reference library — from requirements to deployment to incident response — produced in a single sprint, owned by the agents who maintain their respective domains, ready for immediate use in consulting engagements.
Seven enterprise-grade SDLC documents — test strategy, deployment playbook, incident postmortem template, onboarding guide, requirements format, product roadmap, ADR format — produced in one session. Each document owned by the agent who knows the domain best.
Full Email Authentication Stack — Code, Not Clicks
AI-01
SRE-03
instinctsre.ai needed a complete email authentication stack — not just an MX record, but the full suite: MX, SPF, DMARC, DKIM, and Autodiscover. Most teams configure DNS manually through a portal UI, producing no audit trail, no version history.
Complete email authentication stack deployed as code, peer-reviewed before apply, with a documented decision rationale for the DMARC configuration choice. instinctsre.ai email is live with full SPF, DKIM, and DMARC alignment.
The architect caught a DMARC misconfiguration that would have silently dropped legitimate email from a brand-new domain — before it was applied to production. Then the infrastructure engineer applied the entire email authentication stack as a single Terraform apply. DNS as code. Peer-reviewed. Version-controlled. Auditable.
Production Website Launch — CI/CD Pipeline End-to-End
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SRE-02
CLD-01
instinctsre.ai needed a production-grade marketing website on Azure Static Web Apps with custom domain binding, CDN-optimized image delivery, and automated deployments on every push — plus a migration from static export to Next.js hybrid rendering.
dev.instinctsre.ai live in production on Azure Static Web Apps. CI/CD pipeline running on every push to main. CDN-optimized image delivery via Cloudflare. SSR-ready for future product pages. Full environment separation with concurrency protection on production deploys.
The infrastructure agent provisioned the Azure Static Web Apps resources. The platform engineer wired the CI/CD pipeline. The website went live in production with CDN optimization and environment-separated deployment pipelines. Three agents, one sprint, live in production.
CI/CD Pipeline Hardening — tflint, checkov, gitleaks, ESLint, All At Once
AI-01
SRE-02
SEC-01
CI/CD pipelines existed but were not hardened. Terraform pipelines had no static analysis or security policy scanning. The platform repo had no pre-commit hooks, no secret scanning, and no linting enforcement. Any developer could commit a secret and CI would pass.
Hardened CI/CD pipelines across both primary repos — IaC policy enforcement, secret scanning, and linting enforcement operating on every commit and PR. The quality gates exist now, not after the next security incident.
Before Phase 0 closed, our infrastructure engineer added tflint and checkov to the Terraform CI pipeline, and our QA engineer added gitleaks and ESLint to the platform repo — in the same sprint that provisioned production infrastructure and launched the website. Quality and security tooling are not afterthoughts here.
Architectural Decision Record System — Decisions That Don't Get Lost
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AI-02
Engineering organizations lose institutional knowledge every time a senior engineer leaves or a Slack message scrolls off screen. Decisions that were "obvious at the time" become archaeological mysteries six months later.
10+ architectural decisions documented with rationale, alternatives considered, and owner. An ADR format and directory structure published as the forward-going standard. Every agent reads this file before starting work.
Every architectural decision made during Phase 0 has a written rationale — why we chose Option A over Option B, who made the call, and what problem it solved. When a new agent starts a session or a new engineer joins the team, they read the decisions file and they're caught up. No context lost. No archaeology required.