Multi-agent AI systems for real business work

I design, build, and operate AI agent systems — orchestrated teams of specialized agents with documented skills, verified output, and live integrations into CRM, calendar, voice, and content platforms. Not demos. Working infrastructure that executes daily.

AI SYSTEMS · DESIGNED, BUILT, OPERATED

Meet { the system } LIVE

Orchestrated AI agents with documented skills, verified output, and live integrations into CRM, calendar, voice, and content platforms. Working infrastructure that executes daily.

AI agent orchestration: structured delegation and verified output

AI agent orchestration is the layer that receives objectives, breaks them into tasks, delegates each one to a specialist agent, and verifies the output before it reaches a human. I designed and operate this architecture myself — a chief-of-staff agent that routes work across marketing, voice, and administrative specialists, with quality-assurance gates and error logging at every handoff. The result is delegation that behaves like a managed team: work gets checked, failures get documented, and nothing ships unreviewed.

ORCHESTRATOR VERIFY SPECIALISTS

AI agent skills: documented playbooks for consistent output

AI agent skills are reusable, documented procedures that tell an agent exactly how to perform a task — encoding brand rules, compliance guardrails, formatting standards, and step-by-step workflows. Every agent in my system runs on skills I authored, from content sourcing standards to lead-qualification criteria, so output is consistent and auditable rather than improvised. When something needs correcting, the skill is fixed once and every future task inherits the fix automatically.

SKILL ARCHITECTURE DEFINE ONCE 01 / BRAND RULES 02 / GUARDRAILS 03 / WORKFLOWS INHERITS CV / SYS.02 SKILLS

AI task automation: scheduled and event-driven execution

AI task automation runs business work on two triggers: schedules for recurring tasks and events for real-time ones. In my system, content pipelines, lead reviews, and reporting execute on cron schedules, while webhooks fire agents into action the moment something happens — a form submission, an inbound call, a booked meeting. Humans set direction and approve; agents execute around the clock.

EXECUTION MODES 01 / SCHEDULED CRON · RECURRING 02 / EVENT-DRIVEN TRIGGER WEBHOOK · ON EVENT EXECUTES CV / SYS.03 TASK AUTOMATION

AI agent integrations: CRM, calendar, voice, and API connections

AI agent integrations connect agents to the systems where business actually happens — CRM, calendars, phones, and content platforms — through APIs and automation layers. My builds log leads and call dispositions in HubSpot, book meetings directly to Google Calendar, run inbound and outbound calls through Retell, and publish through Webflow, wired together with Make.com scenarios and custom Cloudflare Workers. An agent that can't touch real systems is a chatbot; these do the work end to end.

AI Integration

Multi-agent AI system structure

A multi-agent AI system is organized like a company: an orchestrator at the top, specialists below it, and shared tools underneath. In my system, Arthur serves as chief of staff — delegating, verifying, and logging — while Emily runs marketing and content, Maya handles voice calls, booking, and intake, and Rita manages scheduling, inbox, and operations. Every agent has a defined role, a documented skill set, and a QA gate before anything reaches the outside world.

SYSTEM STRUCTURE ATLAS ORCHESTRATOR VERA QA / AUDIT NOVA CONTENT SLOAN VOICE REESE OUTREACH QUINN OPERATIONS CV / SYS.05 MULTI-AGENT SYSTEM