Inside the Asset Interceptor: How Agent Outputs Become Your Library

Every AI agent that generates content has the same problem: outputs live in the chat. A beautiful LinkedIn carousel, a generated hero image, a competitive analysis report — all buried in a conversation thread, inaccessible to anyone who didn't witness the run.

The asset interceptor pattern solves this by treating tool outputs as first-class artifacts, not chat messages.

The Problem with Chat-as-Storage

Chat history is optimized for conversation flow, not retrieval. After a dozen workflow runs, finding a specific generated image means scrolling through tool calls, error messages, and user approvals.

Worse, chat is ephemeral in many AI frameworks. Context windows have limits; older messages get truncated. An output from three runs ago might simply be gone.

Assets need a separate home.

How the Interceptor Works

The interceptor sits between the tool execution layer and the message saving layer. Every time an agent completes a tool call, the interceptor checks:

1. Is this tool in the asset registry? (e.g., generate_image, format_post, generate_report)
2. Did the call succeed with a non-empty output?
3. Has this exact output already been saved? (deduplication by content hash)

If all three pass, the interceptor extracts the relevant fields from the tool output and writes a row to the assets table — without any instruction from the LLM.

// Simplified interceptor logic
for (const result of toolResults) {
  const extractor = ASSET_EXTRACTORS[result.toolName];
  if (!extractor) continue;
 
  const asset = extractor(result.output);
  if (!asset) continue;
 
  await db.insert(assets).values({
    teamId,
    type: asset.type,
    title: asset.title,
    content: asset.content,
    metadata: asset.metadata,
  });
}

The LLM never knows this happened. It doesn't need to.

The Asset Registry

Each tool that produces a saveable output registers an extractor function — a pure transform from tool output to asset row.

Adding a new asset type means writing a new extractor and registering it. Nothing else changes.

Deduplication

Workflows often retry steps. Without deduplication, the same image or post gets saved multiple times. The interceptor uses a content hash — a short SHA of the asset content — to skip duplicates.

This also handles approval retries gracefully. If an agent re-generates a post because the user rejected the first draft, the second version gets its own hash and saves cleanly.

Searching Your Library

Once assets are rows in a database, the full power of SQL is available:

• Filter by type, date range, or workflow
• Full-text search across titles and content
• Download any asset at any time, even if the original run is long gone

This is the payoff of the interceptor pattern: you get a searchable, permanent library of everything your agents have ever produced, with zero effort from the agent itself.