# Release 2026.7

July 2026 brings updates to how agent pipelines handle chat history and adds more control over pipeline versions.

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## New OpenAI GPT-5.6 Models

Three new GPT-5.6 model variants are now available on the platform: **GPT-5.6 Sol**, **GPT-5.6 Terra**, and **GPT-5.6 Luna**. You can select them in the LLM or Agent component configuration in Builder.

GPT-5.6 models support a `reasoning_effort` parameter (`none`, `low`, `medium`, `high`, `xhigh`, `max`) and a `reasoning_summary` parameter (`auto`, `concise`, `detailed`) to control the depth and verbosity of the model's reasoning process. GPT-5.5 and GPT-5.5 Pro have also been updated with `reasoning_effort` (up to `xhigh`) and `reasoning_summary` support. You can set these parameters on the **Advanced** tab of the component configuration panel.
## More Control Over Pipeline Versions

Two new actions are now available in the pipeline versions list in Builder:

- **Finalize** — converts a draft version into a permanent, numbered version. Use this when you want to lock in a draft without deploying it.
- **Set as default** — marks a non-draft version as the default. The default version is used by endpoints when a request identifies a pipeline by name or ID but does not specify a version. Default versions appear with a gold star **Default** tag in the versions list.

For details, see [Manage Pipeline Versions](/docs/how-to-guides/designing-your-pipeline/manage-pipeline-versions.mdx).

## AI Assistant in Builder

Builder now has a built-in AI assistant you can chat with directly in the pipeline editor. Click **AI Assistant** to open the chat panel and start a conversation.

<ClickableImage src="/img/getting-started/ai-assistant-builder.png" alt="AI Assistant in Builder" size="large" />

The AI assistant was designed with debugging in mind — you can describe a pipeline issue, paste error messages, or ask it to help you understand unexpected behavior. But it's not limited to debugging: you can also use it to get help with pipeline design, understand how components work, or explore other use cases as you build.

When you have issues listed in the Issues panel, you can also click **Fix with AI** next to any issue. The AI assistant opens, reasons through the fix, and can apply it for you.

For details, see [Debug with Builder](/docs/how-to-guides/productionizing-your-pipeline/debug-with-pipeline-builder.mdx).

## Bedrock Prompt Caching

Amazon Bedrock models now support prompt caching for system prompts. You can set the `system_cachepoint_config_ttl` for models hosted on Amazon Bedrock to cache your system prompt for 5 minutes (`"5m"`) or 1 hour (`"1h"`), reducing latency and cost for repeated requests that share the same system prompt. The system prompt must stay static, with no parameters, for the cache to work. For details, see [AmazonBedrockChatGenerator](/docs/reference/pipeline-components/legacy-components/AmazonBedrockChatGenerator.mdx) and [Use Amazon Bedrock Models](/docs/how-to-guides/designing-your-pipeline/use-hosted-models-and-services/using-amazon-bedrock-models.mdx).

## Full Message History for Agent Pipelines <BetaLabel />

You can now control how much chat history is replayed to agent pipelines on each turn using the `chat_history_granularity` setting. By default, the platform sends only user query and final answer pairs to the pipeline (`QUERY_ANSWER`). Setting granularity to `ALL_MESSAGES` sends the complete message trace from the previous turn — including tool calls, tool results, and reasoning steps — so agents retain full context across follow-up requests.

You can set this per request through the API or as a pipeline-level default in the pipeline YAML under `history.granularity`:

```yaml
history:
  granularity: all_messages
```

The API field `chat_history_granularity` accepts `ALL_MESSAGES` or `QUERY_ANSWER` (uppercase). The YAML field accepts lowercase values. If both are set, the API request value takes priority over the YAML default.

For details, see [Agent Memory](/docs/concepts/ai-agents/agent-memory.mdx).

## Search History Export Improvements

### Pipeline Version ID in CSV Export

You can now filter the search history CSV export by pipeline version ID. The export also includes a `pipeline_version_id` column. Each row shows the ID of the pipeline version that served the query, so you can compare behavior across versions or investigate queries tied to a specific deployment.

### Feedback Created At Column

The Search History table and CSV export now include a **Feedback Created At** column that shows when a user submitted feedback for each query. This column is separate from **Created At**, which records when the query was made. The column appears by default in both extractive and generative pipeline history views. In the CSV, the column is named `feedback_created_at` and is empty when no feedback has been given for a query.

For details, see [Monitor Pipeline Performance](/docs/how-to-guides/productionizing-your-pipeline/pipeline-performance.mdx).