# BigQuery Graph is now GA: the knowledge foundation for the agentic era > Source: > Published: 2026-08-31 16:00:00+00:00 Many of the questions that matter in enterprise data aren't just about individual rows — they're about how things connect: how two accounts are linked, what path a payment took, what context grounds an AI agent's answer. That’s what a graph is built to solve. Historically, unlocking these insights meant extracting data into standalone graph databases, creating silos and operational overhead. To remove these barriers, we brought native graph capabilities directly to the data warehouse. Today, we are announcing the general availability of [BigQuery Graph](https://docs.cloud.google.com/bigquery/docs/graph-overview). We introduced BigQuery Graph in [preview](https://cloud.google.com/blog/products/data-analytics/introducing-bigquery-graph?e=48754805) to unify graph and relational analytics. ISO-standard Graph Query Language (GQL) sits alongside SQL, traversals run natively, and there’s no ETL. And because it’s built on BigQuery, BigQuery Graph inherits and expands its capabilities: It reaches petabyte-scale without the memory bottlenecks of a scale-up database, runs under your existing row- and column-level security, and calls BigQuery ML and AI functions in the same query. One engine, two jobs — large-scale graph analytics, and connected context for AI agents. "BigQuery Graph has been a game-changer for our threat detection pipeline, allowing us to move beyond simple, siloed alerts. By modeling our security signal data as a property graph, we can now perform complex, multi-hop traversals in seconds - something that was previously computationally prohibitive. This graph-centric approach automatically clusters anomalies into coherent attack stories, which, combined with the seamless integration of Gemini models, helps us generate actionable threat narratives. We look forward to integrating native BigQuery Graph algorithms to further streamline our workflows." - Pete Rubio, VP of Global engineering at Thales Cybersecurity Products Since preview, we saw data teams across industries adopt BigQuery Graph for both analytical and agentic workflows: **Threat and fraud detection:** Security and financial organizations correlate signals across event logs to uncover multi-hop attack paths, fraud networks, and suspicious transaction loops. **Supply chain digital twins**: Manufacturing and logistics organizations map dependencies across suppliers, parts, and distribution routes to simulate disruptions and optimize fulfillment. **Identity resolution and Customer 360**: Ad-tech and retail platforms stitch fragmented user identifiers and behavioral touchpoints into unified customer profiles across channels. **Knowledge graphs and AI agent grounding**: Enterprise AI teams build structured knowledge graphs from unstructured documents, providing domain context to ground Gemini models and GraphRAG workflows. **Network lineage and infrastructure management**: Telecommunications and enterprise IT teams track complex network topologies, service dependencies, and data lineage across multi-hop paths. Reaching GA is more than a stability milestone. The work fell into two movements: we made the graph engine itself faster and broader, and we built an agentic ecosystem around it — so agents can build a graph, chat with it, and keep an auditable memory on it. Some of what follows is generally available today; some is in preview or rolling out over the coming weeks. “Advertising has spent decades optimizing individual events; the agentic era will optimize the relationships between them. At Yahoo, BigQuery Graph gives our AI agents connected context - campaigns, audiences, exposures, and outcomes, traversable with standard GQL right where our monetization data already lives, with no separate graph engine and no data movement. Our agents don't just read the graph; they reason over it and write their conclusions back as new relationships. That's how monetization moves beyond automation, to autonomous systems we can trust to act.” - Mikul Bhatt, Director of Engineering, Monetization Platform at Yahoo Agents are only as good as the context they can reason over, and that context is rarely in one place. With [borderless Lakehouse](https://docs.cloud.google.com/lakehouse/docs/about-borderless-lakehouse), a single BigQuery Graph can span native BigQuery tables and open Iceberg tables in other clouds — through Databricks Unity Catalog, AWS Glue, or Snowflake — traversed in place, without copying data or building ETL pipelines. Say a support agent needs to answer, "who supplies the product behind this customer's delayed order, and where are they based?" The customer data sits in an Iceberg lakehouse on Google Cloud, the product and supplier records in a Databricks catalog on AWS. Instead of stitching the sources together per request, the agent traverses one virtual knowledge graph that already connects them — over data that never moved. The following DDL statement shows how you can define this virtual graph, mapping your node and edge tables directly across both cloud environments: With that, the agent gets a grounded, multi-hop answer assembled across two clouds in a single traversal: BigQuery Graph is built for questions about connection: how two accounts are linked, what path a payment took, which entities sit within a few hops of a flagged one. These are the questions SQL joins struggle to express, and they're where a graph engine earns its place. At GA, we've made them both faster to run and easier to write: **Faster execution.** GA optimizes path-finding for acyclic and undirected traversals: against public benchmarks, GQL is 2x faster since preview and undirected traversal 100x, with faster, more resource-efficient cycle detection in `ACYCLIC` and `TRAIL` path modes. Lower query latency keeps the neighborhood and path lookups that ground an agent's answer responsive under frequent, interactive access. **More expressive queries.** With the new [ CALL statement ](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/graph-query-statements#gql_call)and extended “Companies have plenty of workforce data, but very little shared understanding of what their people can do or where they fit. BigQuery Graph lets us turn that scattered information into a reusable property graph and traverse connections across people, roles, capabilities, and evidence at scale, so the same connected workforce context can support thousands of decisions instead of being recreated one decision at a time. That gives AI a stronger foundation for much harder questions about how work should get done.” - Heiko Roth, Founder & CEO, Workerbee You don't have to write GQL to explore a graph. BigQuery [conversational analytics](https://docs.cloud.google.com/bigquery/docs/conversational-analytics?content_ref=when%20you%20ask%20questions%20about%20your%20graph%20the%20agent%20constructs%20sql%20queries%20to%20answer%20them%20agents%20can%20use%20descriptions%20and%20synonyms%20that%20you%20define%20on%20your%20graph#graphs) lets you [chat with your graph](https://docs.cloud.google.com/bigquery/docs/graph-chat) directly in natural language: it reads the relationships in your schema to translate a question into SQL or GQL, and visualizes the traversal for path-based answers. The agent draws on graph metadata like descriptions and synonyms to keep results grounded — the relationships that make a graph a graph are exactly what cut the ambiguity and hallucination that plague free-form natural language querying. You can also connect [Gemini Enterprise](https://cloud.google.com/gemini-enterprise?utm_source=google&utm_medium=cpc&utm_campaign=1713762-Gemini_Enterprise-DR-NA-US-en-Google-BKWS-EXA-GEnterprise&utm_content=c-Hybrid+%7C+BKWS+-+MIX+%7C+Txt_Gemini+Enterprise-189528400785&utm_term=gemini+enterprise&gclsrc=aw.ds&gad_source=1&gad_campaignid=23370621055&gclid=Cj0KCQjw4orUBhCjARIsAIbF3qwrXsr1khkuSsBNMPTjrHynNaAJTcSyWSavMuVwERJmMqfKVkmO9LIaAjf7EALw_wcB&e=48754805) to BigQuery Graph through an [MCP server](https://docs.cloud.google.com/bigquery/docs/use-bigquery-mcp), or [publish](https://docs.cloud.google.com/bigquery/docs/create-data-agents#publish-agent-gemini-enterprise) the conversational data agent to it directly. Standing up a graph — modeling tables into nodes and edges, then writing GQL against them — is work you can hand to the data agent you already use. We've packaged BigQuery Graph expertise into an agent skill that makes your agent fluent in graph: GQL pattern matching, blending graph and SQL, and schema design that follows our recommended practices. The capabilities are accessible out of the box in your preferred agentic coding tool, such as Antigravity, Visual Studio Code, Claude Code, and Codex, with the Google Cloud [Data Agaent Kit extension](https://docs.cloud.google.com/data-agent-kit/overview). The skill is also learning to author, not just advise — a capability rolling out soon. Point it at a dataset, a model document, or an ER diagram and it proposes the nodes and edges, then verifies each relationship against your data before building, showing you the match rates: this one resolves at, say, 98%, that one 56%. You get a graph you can trust from day one. Grounding an agent is half the job; the other half is remembering what it did. As agents move from advising to acting, every decision has to be explainable after the fact — which option was chosen, which policy applied, which alternatives were rejected. With [context graph](https://adk.dev/integrations/bigquery-agent-analytics/#context-graph) in BigQuery Agent Analytics, each action an agent takes is captured and shaped into a context graph: a typed, queryable trace of the agent's reasoning, stored right in BigQuery Graph. Because the trace is itself a graph, "why did the agent do this?" is a single traversal — and the outcomes you join back to those decisions become the data that improves the next one. BigQuery Graph runs graph analytics and grounds AI agents on your data, across clouds. To get started, check out the [overview and data model](https://docs.cloud.google.com/bigquery/docs/graph-overview) to see how GQL, node tables, and edge tables fit together, then put them to work on your team’s common patterns. Trace suspicious money movement and synthetic identities in the [fraud detection codelab](https://codelabs.developers.google.com/codelabs/fraud-bigquery-graph#0), stitch fragmented emails, devices, and cookies into one customer in the [identity resolution codelab](https://codelabs.developers.google.com/codelabs/identity-resolution-bigquery-graph#0), or model a supply chain as a [digital twin](https://cloud.google.com/blog/products/data-analytics/modeling-a-digital-twin-using-bigquery-graph?e=48754805) you can query for hidden dependencies when disruption hits. From there, take it toward agents. The [agent context graph codelab](https://codelabs.developers.google.com/bqaa-context-graph) turns raw event logs into a graph that audits, explains, and traces what your autonomous agents actually did — the connected memory behind a system you can trust to act. If your workloads span both real-time operational transactions and massive-scale analytics, explore our [unified graph solution](https://cloud.google.com/blog/products/data-analytics/the-unified-graph-solution-with-spanner-graph-and-bigquery-graph?e=48754805) to see how Spanner Graph and BigQuery Graph work together. And when you are ready to go deeper — our [ebook](https://cloud.google.com/resources/graph-ebook) walks the journey end-to-end.