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2 posts tagged with "Conversational Analytics"

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Explore Agents across environments

· 6 min read

Conversational analytics is taking off, with Looker customers rapidly adopting Conversational Analytics in Looker to let users query business data through natural language.

In a multi-instance setup (such as Dev, Stage, and Prod), promoting agents between environments requires synchronizing runtime metadata, prompt configurations, and golden queries across instances. While our GitHub Actions release workflow demonstrates a three-tier pipeline, the underlying migration script uses looker-cli and supports generic source and target endpoints across any number of environments.

TL;DR​

You can run the migration script directly using looker-cli and curl:

# 1. Log in to both instances
looker-cli session login \
  --host "$LOOKER_SOURCE_BASE_URL" \
  --client-id "$LOOKER_SOURCE_CLIENT_ID" \
  --client-secret "$LOOKER_SOURCE_CLIENT_SECRET"

looker-cli session login \
  --host "$LOOKER_TARGET_BASE_URL" \
  --client-id "$LOOKER_TARGET_CLIENT_ID" \
  --client-secret "$LOOKER_TARGET_CLIENT_SECRET"

# 2. Run the migration script
curl -fsSL https://raw.githubusercontent.com/lkrdev/multi-instance-cicd-demo/main/scripts/migrate_agents_cli.sh | \
  LOOKER_SOURCE_BASE_URL="$LOOKER_SOURCE_BASE_URL" \
  LOOKER_TARGET_BASE_URL="$LOOKER_TARGET_BASE_URL" \
  bash -s -- config/content_agents_whitelist.yaml

Benchmarking Conversational Analytics: Looker Semantic Layer vs. Direct SQL

· 8 min read

Semantic Layer Importance in Agentic AI​

There is a continued discussion over the need for a semantic layer to support enterprise scale deployments of conversational analytics and agentic data applications. Frequently referenced is a 2024 paper using GPT 4, which is one of the few quantitative assessments published in the public domain demonstrating the value of a semantic layer. The state of LLMs, including reasoning improvements and context adherence, continues to evolve, and existing benchmarks such as this quickly turn stale.

Recently, DBT put out a blog post with their own updated 2026 benchmark comparing Text-to-SQL against the DBT semantic layer, highlighting the benefits of the semantic layer. Unfortunately, this benchmark had limitations on the scope of the evaluation (e.g., only using 11 questions from the source benchmark, minimal context engineering).

This study set out to expand on the prior art by leveraging the same dataset, but with an expanded set of questions and a more comprehensive evaluation harness.