| 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
- Read existing code — Load the relevant agent, memory, and LLM call files to understand the current pipeline structure before touching anything.
- Identify insertion points — Trace the data flow (e.g., frame processors, aggregators, search paths) and pinpoint exactly where the new component should be inserted.
- 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.
- 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.
- 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.
- 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.
