Building an AI Agent Runtime with WebAssembly
A 14-part technical series on designing and implementing a sandboxed AI agent runtime from scratch.
You have an LLM that can reason, plan, and generate code. You want it to do things. The obvious approach — exec() the output — is a terrible idea. This series builds the right answer from scratch.
Starting from the threat model, you'll design and implement a sandboxed runtime where an LLM reasons about a task, generates WebAssembly programs to execute it, observes the results, and iterates. By the end, you'll have a WASM engine, a capability plugin system, a preprocessor, an LLM-driven ReAct loop, an HTTP server with SSE streaming, and a React frontend — defended by seven concentric layers of security.
What you'll build
A complete agentic runtime: WASM sandbox, wire protocol, capability traits, a custom preprocessor, ReAct orchestration loop, HTTP + SSE server, and a frontend. Each chapter adds a real, working layer.
Chapter Guide
I. Foundations
The problem space and why WebAssembly is the answer.
II. Core Runtime
The engine, memory model, capability system, and module linking.
III. Developer Experience
Making WAT writable with a custom preprocessor.
IV. Agent Architecture
Expanding the plugin system and building the ReAct orchestration loop.
V. Integration
CLI tooling, HTTP server with streaming, and a browser frontend.
VI. Production
Defense in depth, error recovery, and where the project goes next.