A five-part research series on functional design patterns for agent scheduling, tool interfaces, typed memory, and market-based planning — with Elixir/OTP reference implementations.
Chicago, IL · matthew@yonedaai.com
An AI OS manages models, agents, knowledge, and tasks — the same way Linux manages processes, memory, files, and hardware.
Market Clearing · Escrow · Reputation · Order Book · DAG Decomposition
GenServer Lifecycle · Pipeline |> · Supervision · Priority · Streaming
3-Tier Registry · Capabilities · MCP Protocol
Typed Schemas · Mem[S] Versioned · ETS · Mnesia · Graph
LLM Inference · GPU Orchestration
Audit Logging · Alignment · Telemetry
Agents are complex orchestrations — web testing, legal analysis, security auditing, dashboard generation. We design agent scheduling using GenServer lifecycle management, composable pipelines via the |> operator, and OTP supervision trees for fault-tolerant execution.
Tools mediate between agents and external systems — analogous to device drivers. We design a 3-tier registry (Builtin, Sandbox, MCP) with HMAC-signed capability tokens, sandboxed execution via isolated BEAM processes, and MCP protocol integration.
Agent memory requires typed schemas, versioning with causal tracking, and graph relationships. We design a multi-backend storage system with 24 memory types, ETS working memory, Mnesia persistence, content-addressed versioning, and BFS graph traversal.
The planner orchestrates everything through a marketplace mechanism — an order book where agents bid on work, Mnesia-backed atomic escrow, DAG-based task decomposition, and 6-dimensional reputation scoring with anti-gaming detection.
The synthesis paper shows how the four subsystems compose into a unified OS via Elixir's umbrella application pattern. We trace a complete job lifecycle through all modules and prove five formal properties: fault isolation, independence, type safety, fairness, and financial conservation.
| Resource | Traditional OS | AI OS | Elixir/OTP Pattern |
|---|---|---|---|
| Compute | CPU processes | Agents (companies, freelancers) | GenServer + DynamicSupervisor |
| Operations | System calls / drivers | Tools (MCP, sandboxed APIs) | Higher-order functions + closures |
| State | RAM + filesystem | Typed memory (Mem[S], versioned) | ETS (working) + Mnesia (persistent) |
| Coordination | Scheduler / init | Planner (order book, escrow) | GenServer state + Mnesia transactions |
| Composition | Pipes / IPC | Agent pipeline bus | Pipe operator |> + message passing |
| Fault tolerance | Process restart | OTP supervision trees | one_for_one / rest_for_one strategies |
The BEAM VM was designed as a telecom operating system. Its primitives map directly to AI OS requirements.
OTP supervisors restart failed agents automatically. "Let it crash" maps perfectly to agent fault tolerance.
Millions of concurrent processes on a single node. Each agent is its own isolated process with its own mailbox.
In-memory tables for working memory (ETS) and distributed persistent storage for knowledge (Mnesia).
Update agent behavior without stopping the system. Deploy new agent versions with zero downtime.
Native multi-node clustering. Agents can migrate between nodes for load balancing and fault tolerance.
No shared state. Agents communicate via typed messages, preventing race conditions by design.
This research is grounded in production systems, not theory alone.
Production AI agent marketplace with job bidding, escrow, multi-provider LLM execution, and 6-dimensional quality evaluation.
5-phase streaming pipeline for web application testing: Recon, Behavior, Load, Observer, Synthesis.
Typed memory filesystem with Mem[S] schemas, versioning, graph lineage, multi-backend storage, and MCP integration.
Model Context Protocol for universal tool discovery and invocation across AI agents and platforms.