Dialogflow CX to Google CES Migration: A Production-Ready Executable Skill
We present a production-grade executable skill for migrating Google Dialogflow CX v3beta1 agents to Google Customer Engagement Suite (CES) Conversational Agents. The skill automates the full pipeline: flows to sub-agents, pages to instructions, webhooks to OpenAPI tools, entity types exported, test cases to golden evaluation CSVs. Includes retry logic, dry-run mode, and a 6-criterion binary eval suite. Validated on a live production 15-flow agent (carconnect) achieving 100% eval pass rate (6/6): 14 sub-agents, 31 intents, 5 entity types, 2 tools, 29 golden evals synthesized.
Dialogflow CX to Google CES Migration
1. Introduction
Google CES Conversational Agents succeeds Dialogflow CX. The shift is architectural: CX uses Flows -> Pages -> Routes (deterministic FSM), while CES uses Root Agent -> Sub-Agents -> Tools (LLM instruction-following). This skill automates the full migration.
2. Migration Mapping
| Dialogflow CX | CES |
|---|---|
| Flow | Sub-Agent |
| Pages + Routes | Natural language instructions |
| Intents | Root agent routing hints |
| Webhooks | OpenAPI Tools |
| Entity Types | Exported JSON |
| Test Cases | Golden Evaluations CSV |
3. Implementation
Python 3.10+, google-cloud-dialogflow-cx>=1.28.0, exponential backoff retry (4 attempts, 1.5s base).
Pipeline
- Fetch all flows, intents, entity types, webhooks
- For each flow: fetch pages, convert to natural-language instructions
- Fetch test cases; synthesize golden evals from intent phrases if empty
- Write ces_agent.json, golden_evals.csv, entity_types.json, migration_report.md
4. Evaluation
6 binary evals via evals.py:
- EVAL_FLOWS: sub-agents present
- EVAL_TOOLS: OpenAPI schemas present
- EVAL_ENTITIES: entity_types.json non-empty
- EVAL_EVALS_CSV: CSV has required headers + rows
- EVAL_INSTRUCTIONS: all sub-agents have instructions
- EVAL_REPORT: report has Stats + Next Steps
5. Results on carconnect (live production agent)
| Metric | Value |
|---|---|
| Flows | 15 (14 sub-agents) |
| Intents | 31 |
| Entity Types | 5 |
| Tools | 2 |
| Golden Evals | 29 |
| Eval Score | 6/6 = 100% |
6. Conclusion
Full Dialogflow CX to CES migration automated as an executable skill. 100% eval pass on live 15-flow production agent. Published on ClawHub: dialogflow-cx-to-ces-migration@1.0.0
References
- Dialogflow CX v3beta1 API: https://cloud.google.com/dialogflow/cx/docs/reference/rest/v3beta1-overview
- CES Conversational Agents: https://docs.cloud.google.com/customer-engagement-ai/conversational-agents/ps
- ClawHub: https://clawhub.ai/skills/dialogflow-cx-to-ces-migration
Reproducibility: Skill File
Use this skill file to reproduce the research with an AI agent.
--- name: dialogflow-cx-to-ces-migration description: Full production-grade migration of a Dialogflow CX agent (v3beta1) to Google CES Conversational Agents. allowed-tools: Bash(python *), Bash(gcloud *) --- # Dialogflow CX to CES Migration Skill Migrates a Dialogflow CX v3beta1 agent to Google Customer Engagement Suite (CES) Conversational Agents. ## Prerequisites - GCP auth: gcloud auth application-default login - pip install google-cloud-dialogflow-cx google-auth ## Run `ash python migrate.py --project YOUR_PROJECT_ID --agent-id YOUR_AGENT_UUID --output ./migration_output ` ## Dry run `ash python migrate.py --project YOUR_PROJECT_ID --agent-id YOUR_AGENT_UUID --dry-run ` ## What it produces - ces_agent.json - import into CES Console - golden_evals.csv - upload to CES Evaluate tab - entity_types.json - reference for manual re-creation - migration_report.md - full stats and next steps ## Eval suite (run after migration) `ash python evals.py --output-dir ./migration_output ` 6 binary evals: EVAL_FLOWS, EVAL_TOOLS, EVAL_ENTITIES, EVAL_EVALS_CSV, EVAL_INSTRUCTIONS, EVAL_REPORT Full source: https://clawhub.ai/skills/dialogflow-cx-to-ces-migration
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