AI Agent Ecosystem
The SRE team that
never sleeps.
13 specialized AI agents coordinating across 4 repositories — monitoring, building, shipping, and improving your platform continuously.
AI Workflow Pipelines — Live
How It Works
Not a chatbot. A coordinated team.
Role-Based Agents
Each agent has a defined domain, tools, and escalation path. No overlap, no gaps — clear ownership across every function.
Coordinated Execution
Agents pass work to each other through structured handoffs. Architecture, engineering, QA, and delivery in a single continuous loop.
Built for Your Stack
Deployed into your repositories, your CI/CD, and your cloud — not a black box SaaS you can't inspect or control.
The Squad
13 agents. One coordinated system.
Each agent owns a domain, escalates on threshold, and delivers complete work — not suggestions.
Whis
Lead Architect
Architecture, ADRs, cross-repo coordination
Broly
Project Manager
Backlog, sprint board, milestone tracking
Goku
Platform Engineer
TypeScript API, Next.js frontend
Vegeta
Infrastructure Engineer
Terraform, Azure IaC, CI/CD
Frieza
QA Engineer
Test coverage, security scanning, tech debt
Beerus
SRE Operations
SLOs, alerting, incident response, chaos engineering
Gohan
Consulting Delivery
Runbooks, architecture docs, client deliverables
Piccolo
Process & Governance
SOPs, compliance frameworks, operational standards
Jiren
Full-Stack & UI/UX
Frontend, UI/UX design, brand enforcement
Shenron
R&D / POC Master
POC builds, R&D prototypes, lab execution
Scribe
Session Logger
Memory management, session logs, knowledge promotion
Ralph
Work Monitor
Issue queue patrol, backlog health
Android 17
Business Analyst
Requirements, user stories, backlog refinement
13 Agents · 4 Repositories · 1 Coordinated System
Phase 0 Results
We built our own AI team before selling it to anyone.
Phase 0 closed 31 issues, merged 10 PRs, and generated verifiable consultant-equivalent value — in a single sprint.
166 hrs
Autonomous agent work logged in Phase 0
$29,050
Equivalent consultant value generated
13 agents
Coordinating across 4 repositories
4 repos
Platform, web, infra, and AI agents
21 services
Mapped and operational in Phase 0
10 cases
Validated business use cases documented
The Process
How we deploy your AI team.
Discover
Audit your stack, map operational pain points, and define agent roles.
Design
Architect the agent ecosystem, define handoffs, and scope each domain.
Build
Implement agents in your repositories with full test coverage and documentation.
Deploy
Wire agents into your CI/CD, monitoring, and communication channels.
Optimize
Continuous improvement loop — agents learn from outcomes and improve over time.
Use Cases
What the squad handles automatically.
Incident Response
Before
On-call engineer paged at 3am, manually runs through checklist, searches logs, escalates after 45 minutes of triage.
After
SRE Agent runs automated triage, generates incident timeline, routes to the right on-call with context. Engineer wakes up to a briefing, not a blank terminal.
Infrastructure Drift
Before
Drift discovered during a post-mortem, weeks after it caused the incident. Compliance team scrambling for audit trail.
After
Drift Detector catches the change within minutes, opens a remediation PR, and alerts the team before it reaches production.
Deployment Safety
Before
Deploys reviewed manually. Risky changes slip through because reviewers are context-switching between 8 open PRs.
After
Deployment Agent runs automated checks, flags risk patterns, and blocks merges that violate deployment policy — every time.
Knowledge Management
Before
Runbooks live in Confluence, go stale in 3 months, and nobody updates them. New engineers are lost on day one.
After
Scribe and Gohan maintain living documentation, updated after every significant change. New engineers are operational from day one.