Translating Agent Context into Visual Reality
Modern AI engineering has moved beyond basic chat transcripts into autonomous execution environments. When agents execute multi-step plans—such as refactoring database abstractions or auditing security perimeters—communicating the output requires more than raw terminal logs.
Live artifacts translate internal reasoning, dependency graphs, and code diffs into clean, interactive web interfaces that stakeholders can immediately inspect, test, and review.
Building from Full Session Context
An agent builds an artifact using the complete context of the engineering session, including local filesystem state, connected runtime tools, and memory caches.
* **Incident Investigations**: Aggregating stack traces, error rate spikes, suspect commits, and verified patches into a single live dashboard.
* **Component Variations**: Generating multiple production-ready UI variations styled with existing design system CSS tokens.
* **Architecture Diagrams**: Mapping service topologies from real AST imports rather than static whiteboard diagrams.
Live Updating in Place
Every artifact revision updates the live view in real time with version history, allowing seamless peer review without requiring full local checkouts.
Artifact Generation Protocol
The underlying protocol uses structured JSON schemas with client-side reactive rendering:
artifactProtocol.ts
interface ArtifactPayload {
id: string;
version: number;
type: 'component' | 'markdown' | 'diagram' | 'dashboard';
title: string;
metadata: {
author: string;
branch: string;
timestamp: string;
};
content: string;
}
export function publishArtifact(payload: ArtifactPayload): Promise<string> {
// Signs and validates payload against zero-exposure security rules
return fetch('/api/artifacts/publish', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(payload)
}).then(res => res.json());
}