OpenAI GPT-5.2 is now available on Databricks, giving teams day one access to OpenAI’s latest models inside the Databricks Data Intelligence Platform. This release also adds native support for the Response API, which unlocks the full set of OpenAI model capabilities, allowing developers to build agent systems more quickly and with much less custom integration work.
when mixed with databricks agent bricksDevelopers can securely connect models to controlled data, evaluate every response with custom metrics, and deploy and monitor agent Scale reliably. Together, these capabilities provide a foundation for building AI agents that can reason accurately and securely act on your enterprise data and processes.
GPT-5.2 Features and Benefits
GPT-5.2 directly improves on GPT-5.1 in the areas that matter most to enterprise and agent workflows: higher accuracy and better token efficiency on medium-to-complex tasks, stronger instructions with cleaner formatting, more deliberately scaffolded logic, and less verbosity with more task-focused responses. It also reflects a more conservative grounding bias, favoring clear, evidence-based reasoning and reducing drift when input is vague or less specified.
These improvements directly benefit use cases that depend on accuracy and structured execution:
- Structured Extraction and Document/PDF AnalysisWhere strong grounding and cleaner formatting minimize drift and missing fields.
- Coding and agentic workflowWhere better instruction following and tool grounding enables more reliable multi-step execution.
- Finance and multimodal workWhere clear logic and less ambiguity improves consistency and correctness.
To understand how these improvements translate to real enterprise workloads, we evaluated GPT-5.2 OfficeQA, Databricks’ benchmarks are designed to test the types of document-heavy, multi-step analytical tasks customers perform every day. OfficeQA, built from 89,000 pages of US Treasury bulletins, measures a model’s ability to extract information in documents, interpret complex tables, and make accurate calculations based on real enterprise data.
In both the full benchmark and the hardest subset, GPT-5.2 achieves the strongest OpenAI performance to date, significantly improving over GPT-5.1 in both agent settings and the Oracle Pages baseline. These benefits highlight GPT-5.2’s stronger grounding, more stable logic, and improved reliability on document-heavy workloads.
“OpenAI GPT-5.2 was designed to excel in agentic tasks in the enterprise, delivering higher accuracy and better token efficiency on medium-to-complex workloads. We are excited to have GPT-5.2 available on day one in Databricks Agent Bricks, providing customers with a stronger foundation for building and deploying AI agents that reason accurately and securely in enterprise use cases.” – Nikunj Handa, API Product Lead, OpenAI
Introduction to Response API on Databricks
The Response API is now available on Databricks, giving developers a single interface for building agents that can use tools, process files, retrieve documents, and generate structured output. It enables a model to be implemented mcp equipment, Perform computer-use actions, or generate images within a single request, eliminating the need for manual orchestration layers. Responses are returned as typed and ordered items, which makes integration, validation, and debugging far more reliable than working with free-form messages. Because the API handles text, images, and tool calls in a consistent flow, it becomes much easier to implement multimodal and tool-driven workloads. And soon, the Response API will be available as a unified interface across all Foundation models on Databricks, making it even easier to build and scale multimodal and tool-driven workloads.
Build trusted AI agents with the Response API and Agent Bricks
Now that GPT-5.2 and the Response API are available on Databricks and integrated with Agent Bricks, teams can build governed, data-aware agents that take real actions with full traceability. GPT-5.2 and the Response API build on the Databricks-OpenAI partnership that is already accelerating how customers develop and deploy AI.
“The Databricks and OpenAI partnership has been phenomenal for us. We’re using the OpenAI SDKs and APIs, and all Databricks components. We can build and deploy apps in Databricks within a few days, sometimes even during workshops, to create MVPs and POCs that help teams see how they can consume insights, take action, and rethink applications and solutions with the tools we have.” – Richard Masters, Vice President, Data and AI, Virgin Atlantic
Add Data Intelligence with MCP Tools
Agents need access to internal data and services, but it is difficult to do so in a controlled and auditable manner. The Response API allows GPT-5.2 to directly call MCP tools as part of its logic, enabling the agent to query delta tableBring features, or trigger internal APIs without leaving the platform. Agent BRICS define which devices the agent is allowed to access through the MCP catalog, and mlflow Records traces and evaluations so developers can observe how each tool was implemented. This creates a controlled and observable path for agents who use your proprietary data to make informed decisions.
Build multimodal AI agents with integrated APIs
Multimodal workflows often require multiple endpoints, custom routing, and brittle preprocessing. The Response API removes this complexity by treating files like text, images, and PDF as native input in a single logic step. GPT-5.2 can summarize documents, extract information from charts, analyze scanned pages, or generate new visuals without switching interfaces. Because everything runs on Databricks, data remains controlled and lineage is preserved.
Evaluate and deploy trusted AI agents with Agent Bricks
Once an AI agent is connected to the data and tools, the next step is to ensure reliable behavior in real workloads. Agent Brix captures detailed traces of each run with MLflow, enabling evaluation to catch regressions, and tracking versions as you refine the logic. It provides a repeatable, enterprise-grade workflow for testing changes, comparing outputs, and promoting high-performing agent versions into production.
next steps
start in Databricks AI Playground Try prompts, tool calls, and multimodal input with GPT-5.2 and in seconds. Use once you are comfortable agent bricks To register your connected MCP tool Lakehouse, Create a small data-aware agent, and iterate with tracing and evaluation until the agent behaves reliably. When it performs consistently on your data, promote it to production.
