Without trusted business context, enterprise AI agents are forced to guess definitions and hunt through schemas—leading to slow responses, high token costs, and inaccurate answers. See how Knowledge Studio in Google Cloud’s Knowledge Catalog turns raw schemas, semantic models, and institutional knowledge into governed, search-ready context for every AI agent.
Explore Knowledge Catalog → https://g.dev/ai/always-on-context
In this extended demo, watch how data stewards and context engineers use Knowledge Studio to ground Gemini Enterprise and BigQuery agents:
• Interactive Context Graph: Index and search across structured BigQuery tables, unstructured policy documents in Google Cloud Storage, enriched metadata, and golden query-answer pairs inferred from real workloads.
• Unified Semantic Foundation: Connect lakehouse and semantic assets—including SAP catalogs, BigQuery, Spanner, dbt, Looker semantic models, and LookML metrics—into one cohesive graph.
• Self-Learning Enrichment Agent: Automatically crawl schemas and documents across Google Cloud Storage, Google Workspace, GitHub, and SharePoint while mining live interaction traces from Gemini Enterprise and BigQuery agents to turn conversational feedback into structured knowledge candidates.
• Human-in-the-Loop Governance & Context Sets: Review, edit, or approve AI-generated enrichment diffs and publish them into IAM-governed Context Sets—delivering up to a 4x accuracy lift, 1/3 lower latency, up to 75% lower token costs, and 67% fewer tool calls.
Chapters:
0:00 - The problem: AI agents guessing business definitions without context
0:32 - Introducing Knowledge Studio & the interactive context graph
1:10 - Grounding Gemini Enterprise with the Knowledge Catalog connector
1:34 - Visualizing the knowledge supply chain (SAP, BigQuery, Spanner, dbt, Looker & LookML)
2:19 - Scaling context engineering as enterprise definitions evolve
2:48 - Launching the Enrichment Agent workflow across structured & unstructured sources
3:11 - Mining live agent interaction traces & configuring confidence thresholds
3:35 - Human-in-the-loop steward review of AI-generated enrichment proposals
3:58 - Publishing governed Context Sets with built-in IAM security boundaries
4:16 - Instant grounding across downstream enterprise agents & benchmark results
Learn more about Google Cloud Knowledge Catalog → https://g.dev/ai/always-on-context
Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
#GoogleCloud #KnowledgeCatalog #BigQuery #Gemini #AgentContext
Speaker: Manoj Gunti
Products Mentioned: Knowledge Catalog
Explore Knowledge Catalog → https://g.dev/ai/always-on-context
In this extended demo, watch how data stewards and context engineers use Knowledge Studio to ground Gemini Enterprise and BigQuery agents:
• Interactive Context Graph: Index and search across structured BigQuery tables, unstructured policy documents in Google Cloud Storage, enriched metadata, and golden query-answer pairs inferred from real workloads.
• Unified Semantic Foundation: Connect lakehouse and semantic assets—including SAP catalogs, BigQuery, Spanner, dbt, Looker semantic models, and LookML metrics—into one cohesive graph.
• Self-Learning Enrichment Agent: Automatically crawl schemas and documents across Google Cloud Storage, Google Workspace, GitHub, and SharePoint while mining live interaction traces from Gemini Enterprise and BigQuery agents to turn conversational feedback into structured knowledge candidates.
• Human-in-the-Loop Governance & Context Sets: Review, edit, or approve AI-generated enrichment diffs and publish them into IAM-governed Context Sets—delivering up to a 4x accuracy lift, 1/3 lower latency, up to 75% lower token costs, and 67% fewer tool calls.
Chapters:
0:00 - The problem: AI agents guessing business definitions without context
0:32 - Introducing Knowledge Studio & the interactive context graph
1:10 - Grounding Gemini Enterprise with the Knowledge Catalog connector
1:34 - Visualizing the knowledge supply chain (SAP, BigQuery, Spanner, dbt, Looker & LookML)
2:19 - Scaling context engineering as enterprise definitions evolve
2:48 - Launching the Enrichment Agent workflow across structured & unstructured sources
3:11 - Mining live agent interaction traces & configuring confidence thresholds
3:35 - Human-in-the-loop steward review of AI-generated enrichment proposals
3:58 - Publishing governed Context Sets with built-in IAM security boundaries
4:16 - Instant grounding across downstream enterprise agents & benchmark results
Learn more about Google Cloud Knowledge Catalog → https://g.dev/ai/always-on-context
Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech
#GoogleCloud #KnowledgeCatalog #BigQuery #Gemini #AgentContext
Speaker: Manoj Gunti
Products Mentioned: Knowledge Catalog