Report 5.3.3 — Limitations and Future ImprovementsNext Steps
Outlines known constraints inside the current solution and recommended enhancements to make the AI + HubSpot automation more robust.
Current Limitations
- HubSpot deletion removes all deals without selective filtering; requires caution in production.
- Action item parsing still relies on LLM accuracy; malformed JSON only logs raw output without retries beyond basic fallback.
- No asynchronous processing; every REST call runs serially, potentially slowing large transcripts.
- No caching for repeated transcripts or repeated HubSpot owner lookups; each call hits APIs anew.
Future Enhancements
- Selective deletion controls: add filters or dry-run options before invoking
deleteAllHubSpotDeals().
- LLM prompt tuning: iterate on prompts using telemetry from the metadata fields to improve JSON fidelity.
- Async task queue: introduce persisted job records and background workers to prevent synchronous waits during heavy operations.
- Assignee resolution: map extracted names/emails to HubSpot owner IDs automatically rather than passing raw text.
These improvements will make the automation safer, faster, and more predictable while keeping the core architecture intact.
Monitoring Suggestions
Leverage the execution metadata (executionTimestamp, executionDurationMs) and extend logging around OpenAI/HubSpot failures to feed dashboards or alerts.
Behavior Diagram Reference
Use plantuml/behavior/uml_state_machine_diagram.html and uml_activity_diagram.html to understand fallback states when APIs fail.