agent-pipeline-integration

name	agent-pipeline-integration
description	Integrate a new ML component (classifier, reranker, etc.) into an existing agent pipeline by reading the codebase, identifying hook points, implementing the module, and wiring it in.

/agent-pipeline-integration

When to use

Use this when adding a new ML-backed component (intent classifier, reranker, retrieval layer, etc.) to an existing agent or pipeline. Applies whenever the new module must slot between existing processing stages.

Steps

1. Read existing code — Load the relevant agent, memory, and LLM call files to understand the current pipeline structure before touching anything.
2. Identify insertion points — Trace the data flow (e.g., frame processors, aggregators, search paths) and pinpoint exactly where the new component should be inserted.
3. Fetch / confirm model architecture — Check the training script or model card to match the exact architecture (layer sizes, activations, dropout, output labels) so the inference code is correct.
4. Implement the module — Write the new file (e.g., agent/intent.py, memory/reranker.py) as a self-contained processor or helper, handling missing-model gracefully.
5. Wire it into the pipeline — Edit the existing integration file (e.g., memory/facts.py, app.py) to call the new module at the identified insertion point.
6. Verify the hook — Confirm the data types flowing in/out match what adjacent pipeline stages expect (e.g., TranscriptionFrame, search result lists).

Examples

• Adding an intent classifier between audio tagging and the LLM aggregator: read app.py → find TranscriptionFrame path → implement agent/intent.py → insert processor between audio_tags and user_agg.
• Adding a BM25 + cross-encoder reranker to memory search: read memory/facts.py search() → implement memory/reranker.py → call reranker inside search() before returning results.