Conversation tags are custom labels applied to individual voice agent conversation records. Each conversation generates a record with transcript, analysis results, and data collection outputs. Tags add a user-defined classification layer — labelling conversations by topic, outcome, customer type, escalation status, or any category relevant to operations. Tags are set via API and usable as filter parameters when retrieving conversation lists.
| API Endpoint | Function | Example Use |
| POST /v1/convai/conversations/{id}/tags | Apply tags to conversation | Tag ‘escalation_risk’ when analysis detects frustration |
| GET /v1/convai/conversations?tags=value | Filter list by tag | Retrieve all ‘human_handoff’ conversations for review |
| DELETE /v1/convai/conversations/{id}/tags/{tag} | Remove specific tag | Remove ‘pending_review’ after QA completed |
| GET /v1/convai/batch-calls?agent_id=value | Filter batch calls by agent | Compare agent A vs agent B batch performance |
How to Use Conversation Tags
Setting Tags
POST /v1/convai/conversations/{conversation_id}/tags with a tags array of string labels. Tags are additive — new tags add to existing ones without removal. Case-insensitive, alphanumeric with underscores and hyphens. Example tags: ‘escalation_risk’, ‘conversion_success’, ‘human_handoff’, ‘new_customer’, ‘appointment_set’, ‘low_quality’, ‘compliance_review_needed’.
Filtering by Tag
GET /v1/convai/conversations?tags=escalation_risk retrieves only conversations with that tag. Combine multiple tags for specific segmentation: ?tags=new_customer&tags=product_inquiry retrieves first-time customers asking about products. Tag-based filtering transforms an unmanageable conversation log into targeted operational queues.
Automated Tag Application
Most powerful when integrated with post-conversation processing: set up a webhook that fires after each conversation → receive analysis results → determine tags based on evaluation scores, data collection fields, or call duration → POST tags programmatically. Example: if analysis_score < 3 → tag ‘low_quality’; if data_collection.appointment_confirmed = true → tag ‘appointment_set’; if duration > 600s → tag ‘long_call’. Automated tagging creates a structured operational dataset without manual review overhead.
Use Cases
QA and Quality Management
Tag conversations automatically by quality score: ‘low_quality’ (analysis below threshold), ‘escalation’ (handoff triggered), ‘long_call’ (unusual duration). QA teams retrieve targeted review queues — the conversations most likely to reveal problems — rather than random sampling or reviewing all conversations. More efficient and more effective than undifferentiated review.
Analytics and Performance Dashboards
Tag-based conversation counts produce the segmentation data for performance dashboards: conversion rates, escalation rates, topic distribution, success metrics. Tag distributions over time reveal trends — rising ‘escalation_risk’ tags signal degrading agent performance before aggregate metrics reflect it.
Compliance Documentation
Tag conversations where mandatory disclosures were confirmed. Retrieve the filtered list for compliance audit. Export tagged conversations with transcripts as compliance evidence. Without tags, compliance documentation requires either reviewing all conversations or building a separate compliance logging system — tags integrate it directly into the conversation record.
CRM and External System Integration
Tags are the bridge between ElevenLabs conversation data and external systems. Tag ‘appointment_set’ → trigger CRM opportunity update. Tag ‘complaint_received’ → create helpdesk ticket. Tag-based conversation metadata is cleanly mappable to external system fields without complex transcript parsing or NLP.
Three Insights Most Conversation Tag Coverage Misses
1. Tags Enable Compliance Without Custom Development
Regulated industries deploying voice agents need compliance documentation — records of specific conversation types, evidence of specific disclosures, audit trails. Conversation tags combined with transcripts enable compliance workflows without custom development: tag all conversations where mandatory disclosure confirmed, retrieve filtered list for audit, export as compliance evidence. The alternative — building a separate compliance logging system — requires significant additional development investment.
2. Batch Calling Agent_id Filter Enables A/B Testing at Scale
The simultaneous release of batch calling agent_id filtering alongside conversation tags is not coincidental. Running two agent configurations in parallel batch calls — different prompt variants, different guardrail settings — and filtering results by agent_id produces the data for controlled A/B testing of agent behaviour at production scale. Tag each batch call conversation by outcome (conversion, decline, voicemail) and filter by agent_id to compare performance between agent variants systematically.
3. Tags Are a Leading Indicator Before Aggregate Metrics React
Tag-based operational monitoring catches problems before aggregate metrics do. If ‘escalation_risk’ tagged conversations spike on Tuesday, the issue is visible in the tag distribution before it appears in overall conversion or satisfaction metrics — which are weekly averages that mask intra-week events. Real-time tag monitoring creates earlier warning of agent behaviour degradation than any aggregate performance dashboard.
Key Takeaways
- ElevenLabs conversation tags (May 7, 2026 — v2.46.0) label voice agent conversations with custom tags, then filter and retrieve the conversation list by tag via API.
- Also released May 7: conversation list filters, batch calling agent_id filtering, contextual update metadata — completing conversation management infrastructure.
- Use for: QA targeted review queues, analytics segmentation, compliance documentation, CRM integration, and escalation management.
- Automate via post-conversation webhook — determine tags from analysis results, apply programmatically. No manual review overhead.
- Tags are a leading operational indicator — rising tag counts signal issues before aggregate metrics reflect them.
Conclusion
ElevenLabs conversation tags complete the operational management infrastructure production voice agent deployments require. At scale — thousands of conversations per week — unfiltered conversation lists are not manageable. Tags transform the conversation list into a structured, filterable operational dataset that feeds QA, analytics, escalation management, compliance, and external system integration. Implement tagging from day one — the value compounds as conversation volume grows.
Frequently Asked Questions
What are ElevenLabs conversation tags?
Custom labels applied to voice agent conversation records, enabling filtering and retrieval by tag. Released May 7, 2026 (v2.46.0). Set via POST /v1/convai/conversations/{id}/tags, filter via GET /v1/convai/conversations?tags=value.
Can tags be applied automatically?
Yes — configure a webhook that fires after conversation completion. Process analysis results and data collection fields to determine tags, then POST them programmatically.
What else was released in the May 7, 2026 update?
Conversation list filters, batch calling agent_id filtering, optional test invocation agent_id, contextual update metadata, and MCP response timeout configuration updates.
Are tags available in the dashboard or API only?
The May 7 update adds tag support via API. Check current ElevenLabs documentation for dashboard-level tag management availability.
Methodology
Conversation tags from ElevenLabs changelog May 7, 2026 (v2.46.0). Related features from changelogs April 13, 2026 and January 2026. This article was drafted with AI assistance and reviewed by the editorial team at ElevenLabsMagazine.com.
References
ElevenLabs. (May 7, 2026). Changelog — conversation tags and list filters. https://elevenlabs.io/docs/changelog
Releasebot. (May 2026). ElevenLabs May 2026 release notes. https://releasebot.io/updates/eleven-labs
