Aaron Diemel

Building execution-grade AI systems

Agent architectures, durable workflows, and adversarial safety testing.

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AI Systems R&D · Founder, Hertzfelt Labs

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Aaron Diemel

Aaron Diemel

Founder, Hertzfelt Labs

I spent 15 years as an audio engineer—signal flow, systems thinking, shipping under pressure. In 2022, I made a bet: reskill entirely with AI as my copilot. No bootcamps, no traditional path. Just me and the models, learning to build together.

It worked. The mental models from audio engineering—signal chains, iterative refinement, translating vision into execution—transferred directly to AI systems architecture. I've scaled my capabilities at the same rate the technology has scaled, staying on the frontier while shipping production systems.

In 2023, I founded Hertzfelt Labs as a consulting practice and applied R&D lab. I work with founders and engineering teams on agent architectures, adversarial evaluation, and AI systems that actually execute. This site is built with the same tools and workflows I bring to client work.

How I Work With Agents

Speed comes from front-loaded architecture and domain translation, followed by delegation to coding agents. Here's the methodology behind shipping functional AI systems rapidly.

01

Architecture First

Front-load system design and domain translation before writing code. Define execution graphs, state schemas, and tool interfaces upfront.

System DesignDomain ModelingState Schemas
02

Claude.md & Agents.md

Maintain project context files that give coding agents full system awareness. Persistent memory across sessions for consistent architectural decisions.

CLAUDE.mdAGENTS.mdContext Files
03

MCP Servers

Custom and OSS Model Context Protocol servers for tool integration. Extend agent capabilities with workspace tools, database access, and external APIs.

Custom MCPSupabase MCPWorkspace Tools
04

Agent Delegation

Delegate implementation to coding agents after architecture is set. Claude Code for complex refactoring, Cursor for iteration, v0 for UI prototyping.

Claude CodeCursorCodexv0
05

Structured Outputs

Type-safe tool calling with Zod schemas and JSON Schema. Reliable structured extraction and function execution with validation at every step.

ZodJSON SchemaTool Calling
06

Evaluation & Iteration

Continuous evaluation against defined success criteria. Rapid iteration cycles with automated testing and human review gates.

Eval HarnessesTest SuitesReview Gates

Agent Tooling & Harness

Vercel AI SDKPrimary SDK
Claude Agent SDKAgent Framework
OpenAI Agents SDKAgent Framework
LangGraphOrchestration
MCP ServersTool Integration
Structured OutputsType Safety
CLAUDE.md
# Project Context

## Architecture
- Durable agent execution with checkpoint-based state
- Tool calling via Vercel AI SDK with Zod schemas
- MCP servers for workspace and database integration

## Conventions
- All tools must return structured outputs
- Human approval gates for destructive operations
- State persisted to Supabase between sessions

## Current Focus
- Implementing document extraction pipeline
- Adding multi-model fallback for reliability

Stack

Tools and platforms in active use.

Languages & Frameworks

TypeScriptJavaScriptPythonNext.jsReactPHPCSS

Agent Tooling

Vercel AI SDKClaude Agent SDKOpenAI Agents SDKLangGraphTool CallingStructured Schema (Zod)MCP Servers

Model Inference

AnthropicOpenAIGoogle GeminiVercel AI GatewayOpenRouterAWS BedrockNous Research

Development

CursorClaude CodeCodexWarp TerminalGitHubReplit

Deployment & Infrastructure

VercelAWSSageMakerSupabaseConvex

ML Ops

Modal LabsHugging FaceWeights & BiasesLangChainLangSmith

Multimodal - Voice

LiveKitOpenAI Realtime APIEleven LabsCartesia

Multimodal - Image

Flux ModelsFal AIStability AIHiggsField AI