New Features:
- Parallel workflow execution: Added an explicit Parallel workflow node, enabling concurrent execution of multiple child nodes with configurable combined outputs.
- Paginated activity logs: Introduced optional pagination for agent activity threads/tasks.
- Connector pipeline progress visibility: Added connector pipeline step status updates and UI indicators (loading/shimmer and tooltips) while custom connectors are processing.
- Workflow test file uploads: Enabled attaching files to workflow test runs and persisting them in test presets.
Improvements:
- Workflow observability: Enriched workflow events with resolved variables/conditions and deep parsing of stringified JSON outputs.
- Activity log durability: Added asynchronous S3 synchronization for activity threads to improve retention and auditability.
- LLM default updates: Updated default models to Claude Sonnet 4.6 and refreshed frontend/provider defaults to the latest Sonnet/Opus catalogs.
- AWS Neptune flexibility: Added support for providing the Neptune endpoint via request body and improved endpoint normalization.
Bug Fixes:
- Schema enforcement for summarizers: Enforced strict JSON output schema for summarizer nodes and hardened file URL extraction from user input.
- As always, our agents and humans continuously fix various bugs.
New Features:
- Workflow authoring enhancements: Added NodePointer (“Go to Existing”) support, workflow result selection (choose which end node output becomes the workflow result), and surfaced workflow result in the execution console.
New connectors:
- Microsoft Connectors: Added new SharePoint and OneDrive connectors with site/drive discovery, recursive tree browsing, search, path resolution, file read/edit/create flows, agent-friendly content extraction.
- AWS Neptune connector: Added an AWS Neptune connector with graph query execution (Gremlin/openCypher) and bulk loader operations.
- Google Cloud Monitoring: Added support for Google Cloud Monitoring with metrics collection across GCP services, real-time alerting, uptime checks, and deep ecosystem integration, enabling AI agents to track system health, respond to alerts, and take action on live performance insights.
Improvements:
- Workflow orchestration reliability: Hardened orchestration with DAG validation, improved routing/order determinism (including node reuse and parallel detection), and support for persisting agent task/thread IDs across orchestration runs.
- Activity & runs scalability: Added optional pagination and infinite-scroll support for agent activity threads and workflow runs.
- BYOK transparency: Marked BYOK usage in monitoring/activity and improved UI visibility so BYOK token usage is clearly distinguished.
- Managed vector DB search quality: Improved large-document handling (chunking + export/delete semantics) and increased hybrid search reliability with full-text + vector ranking.
- AWS connection UX: Improved AWS region selection with a searchable dropdown and refined workflow graph handle interactions.
Bug Fixes:
- Agent execution resiliency: Ensured the agent executor captures all base-level exceptions to reduce hard crashes.
- Other general bug fixes
New Product launch!
Introducing Workflows: A visual interface for orchestrating AI Agents and building automation workflows. We’ve eliminated the friction of traditional automation by removing the need for tedious manual field mapping. Instead of wiring up data inputs and outputs between steps, every node on the canvas is powered by an AI agent that understands the context of the previous step’s output. Whether you’re deploying an Email node or a Summarizer, you simply provide natural language instructions, and the agents handle the data flow for you. This gives you a deterministic, repeatable runtime with full control over execution and branching, without the overhead of mapping a single field.Read more in the docs: https://docs.xpander.ai/user-guide/build/workflowNew Features:
- Azure AI Foundry provider support: Added Azure AI Foundry as an LLM provider (including required configuration for custom credentials and endpoint settings).
- LLM request customization: Added organization-wide and per-agent support for default LLM extra headers.
Improvements:
- LLM provider resilience: Added exponential backoff retries across multiple LLM providers to better handle transient failures.
- Model catalog updates: Refreshed Amazon Bedrock and Google AI Studio model catalogs and identifiers.
- Self-hosted environments: dash-based subdomains: Added support for dash-based subdomain formats and centralized service URL construction.
Bug Fixes:
- UI correctness: Fixed issues in action/icon rendering, agent state flag updates, and sleep node identifier normalization.
New Features:
- Pre-deploy change review: Added a deployment comparison step that lets users review visual and JSON diffs between the staged and currently deployed agent before deploying.
- API trigger support and testing modal: Added an API trigger option alongside Webhook, with a unified modal to test sync/async/stream invocations, generate curl commands, and view expanded responses.
- PDF document layout templates: Added multiple PDF layout modes (whitepaper, invoice, letter, and order form with signature support) for markdown-to-PDF generation.
- Web search and text-to-speech tools: Added built-in web search and text-to-speech tools. Enable them from the Tools tab in the AI Workbench.
- Agent reasoning visibility: Improved reasoning step generation by deriving steps directly from activity threads and enhancing activity timeline rendering for JSON/Markdown with better duration formatting.
Improvements:
- Webhook lifecycle hooks: Added webhook lifecycle hooks (e.g., before, on-task-created, on-tool-call, on-error, after) with event streaming for richer integrations.
- User attribution via MCP auth: Propagated MCP OAuth user context into agent invocations for more accurate user attribution.
- Locations management (renamed from Environments): Renamed “Environments” to “Locations” and moved location management into Admin Settings.
- Webhook parameter mapping: Added path-based parameter mapping for webhooks, with improved task ID parsing for UI rendering and clearer error handling.
- Container execution reliability: Increased Docker runner memory limits and hardened log streaming and exit-code retrieval when containers are auto-removed.
Bug Fixes:
- PDF rendering reliability and security: Improved PDF typography and spacing, ensured reliable logo rendering by inlining images, and added URL validation/allowlisting and size limits for safer logo embedding.
- Hosted assistants UI rendering: Fixed rendering of structured (JSON) sub-task descriptions and improved task step update stability.
- Custom functions reliability: Stabilized custom functions typing/serialization and made analysis asynchronous for improved correctness.