Case Study

Multi-Agent Operating Systems

We design and deploy multi-agent systems that absorb the senior cognitive load most teams still carry manually: pipeline stewardship, research synthesis, project intake, sprint reporting, knowledge retention, and decision support.

30+
Specialist agents live
8 mo
Continuous production use
Week 1
First agent live
4
Memory layers
Pressure

Senior time gets consumed by repeat cognitive work: pipeline stewardship, research synthesis, intake, reporting, and decision support.

Build

A specialist-agent architecture with explicit scopes, orchestration boundaries, persistent memory, and reusable cognitive modules.

Proof

The reference system has been operated internally for eight months with 30+ specialist agents and persistent project memory.

What It Handles

An operating layer for the work your team keeps doing manually.

Sales pipeline stewardship

Research synthesis

Project intake and qualification

Sprint reporting

Knowledge retention

Decision support

Stack
Claude Code

Agent execution and specialist cognitive workloads.

n8n

Scheduled and event-driven orchestration across workflows.

Anthropic API

Model layer for reasoning, generation, and structured outputs.

Your Existing Tools

Memory persists into Notion, Linear, Sheets, Slack, and the systems the team already lives in.

Working Demo

Two operating-system deployments, one shared architecture.

Patient Pipeline EngineLive by end of week one

A quiet background system for recalls, no-shows, treatment plan follow-ups, reviews, and new-patient intake.

Friction today
  • Missed recalls leave rebooking revenue on the table.
  • No-shows are discovered too late for the front desk to recover.
  • Treatment plans stall because nobody owns the follow-up loop.
What the system runs
  • Recall reminders triggered on schedule, with escalation when patients do not book.
  • No-show prevention sequence with automated texts and a call prompt for staff only when needed.
  • Review requests sent after positive visits while treatment-plan follow-ups stay active until resolved.
Orchestration view
Recall Agent

Scans due patients and opens follow-up sequences.

Front Desk Copilot

Escalates only the bookings that need a human touch.

Review Agent

Requests reviews after a strong patient experience.

Value POV

Recovered appointments and accepted treatments can cover the setup inside the first quarter.

Week one live: recall + no-show recovery running in production.

Existing tool fit
DentallySoftware of ExcellenceOptix

Memory persists into the stack your team already uses. No new product to learn, no second operating surface to maintain.

Under The Hood

Built like an operating system, not a prompt toy.

Specialist Agents

Single-responsibility agents with explicit input and output contracts so orchestration stays legible.

Fail-Loudly Boundaries

Hub-and-spoke coordination with observable hand-offs, retries, and stop conditions at every boundary.

Persistent Memory

Working, episodic, semantic, and procedural layers persisted into the tools your team already uses.

Reusable Skill Library

Thirty-plus versioned cognitive modules that can be recomposed sprint by sprint into new operating flows.

Memory architecture
Working

Live context from the task at hand, current queues, and unresolved hand-offs.

Episodic

What happened on this client, this sprint, and this decision trail.

Semantic

Canonical knowledge, policies, playbooks, definitions, and naming conventions.

Procedural

The exact steps, skills, and reusable operating routines agents can execute.

Have a system that needs building?

Tell us about it. First response within 4 business hours.

Start the conversation