Welcome back to the Maia New Features Blog! This week, we're bringing you proactive pipeline anomaly detection, better iterator visibility, and GitLab API support, all designed to give you greater control and insight into your workflows. For a full list of recent changes, be sure to check our changelog updates.

⏱️ Monitor pipeline run duration with new anomaly alerts

Maia now provides early warning alerts when your pipelines take longer or shorter than expected to complete, helping you catch performance issues before they impact your business operations.

For every scheduled or API pipeline run, Maia compares the duration against its historical baseline and fires an anomaly alert if it falls outside the statistically normal range. You'll receive two types of alerts:

• Run is taking longer than expected: Fires mid-run, so if a pipeline is hanging, you find out while it's happening, not after a timeout.
• Run finished unusually quickly: Detected at completion, useful for identifying unexpected data volume drops or processing being skipped silently.

Alerts include the actual duration, the expected range, and how far outside it the run fell, making them immediately actionable. Learn more about pipeline duration anomaly alerts in our documentation

🔄 See iterator variable names and values in task history

We've made a valuable improvement to all iterators in Maia – the new "Record values in task history" parameter allows you to print the names and values of variables used in each iteration directly in the task history tab in Designer and in the Observability dashboard.

This gives you better visibility into your pipeline execution, making it easier to debug and monitor iterator performance. You'll now have clear insight into exactly what variables are being processed during each iteration cycle.

🔗 GitLab repositories now supported via the API

You can now associate a GitLab repository to a project directly through Maia's public API, eliminating the need to use the UI for project creation.

Previously, the Project Provisioning API endpoints only supported GitHub and Azure DevOps. With this expansion, you have more flexibility in how you manage your version control integration with Maia.

Read the documentation for detailed setup instructions.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows—we're all ears! Feel free to add any comments or questions below.

Want to get involved?

Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

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Welcome back to the Maia New Features Blog! This week, we're bringing you enhanced pipeline monitoring and control capabilities. We've expanded notification options beyond just failures and introduced new system variables to give you better visibility into your Iterator components. For a full list of recent changes, check our changelog updates.

🔔 Additional pipeline notification triggers

You can now subscribe to completion alerts for any pipeline state, not just failures. When adding a notification, you can now also choose to be alerted when pipelines are successful, canceled, and skipped.

Mix and match your notification triggers per project or environment, and choose how to receive them by email, Slack, or Webhook. This provides more flexibility for teams who want confirmation that critical pipelines are running successfully, or need to react to cancellations and skips.

Check out our Pipeline notifications guide to get started.

🔄 Iterator component-specific system variables

We've added three new component-specific system variables for iterator components to give you better visibility and control over your pipeline iterations:

• .thisComponent.iterationsAttempted counts the number of attempted iterations
• .thisComponent.iterationsGenerated counts the number of started iterations, counting both successful and failed iterations
• .thisComponent.iterationsSuccessful counts the number of successfully completed iterations

This gives you more granular monitoring for your iterator components, helping you troubleshoot and optimize your pipeline performance. Read Component-specific system variables in our documentation to learn more.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows—we're all ears! Feel free to add any comments or questions below.

Want to get involved?

Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

Welcome back to the Maia New Features Blog! This week, we're thrilled to announce the public preview launch of Mission Control and Context Engine—two major new capabilities that transform how you manage AI agent tasks and ground Maia AI Agents in your business data. For a full list of changes this week, check out our changelog updates.

🗺️ Context Engine: give Maia AI Agents a living map of your data

Context Engine is now in public preview! This powerful new feature enables you to builds knowledge graphs—a living map of the business logic, entities, and relationships in your data landscape—so Maia AI Agents can generate accurate, production-ready pipelines that speak the language of your business.

Maia AI Agents are only as good as their understanding of your data and your business. Out of the box, they know the shape of a warehouse, but not what the tables mean, how concepts relate, or which data is trustworthy. Context Engine closes that gap by crawling your warehouse, inferring the business entities behind your schemas, and storing them as a semantic graph that Maia AI Agents can draw on. The result is less hand-holding, fewer wrong guesses, and pipelines that match how your organization actually thinks about its data.

What's new:

• Domain-focused knowledge graphs: Create a graph per domain (Finance, Sales, Marketing...). Keeping each graph focused helps Maia AI Agents learn the language and logic of that data far more effectively.
• Automated crawlers: Attach a crawler to a warehouse connection and it harvests physical assets and infers the business concepts behind them, keeping the graph accurate and in sync. Works across Snowflake, Databricks, and Amazon Redshift.
• Pipeline execution crawls: Bring in runtime lineage and execution history so Maia AI Agents can reason about what's fresh, what's stale, and how data actually flows.
• Scheduling: Run crawlers on a standard interval or an advanced cron schedule, with pause/resume controls.
• Access controls: Choose Public (every project) or Restricted (project allowlist) per graph, and manage who can view or manage each graph at the user level.

Read the docs to get started.

🎯 Mission Control is now in public preview

We're excited to introduce Mission Control—a centralized hub to launch, monitor, and review the work your Maia AI Agents are doing for you. Available now in public preview, Mission Control also introduces two new capabilities: multi-modal chat and “bypass permissions” mode.

Until now, managing Maia work meant staying within a single conversation. Mission Control transforms this experience by providing a kanban-board-level view of everything Maia AI Agents are doing across your projects, enabling you to run multiple tasks in parallel and only intervene when needed.

Key features:

• Kanban board: Every task flows through Backlog → In Progress → Needs Attention → Completed for at-a-glance status tracking.
• Quick task launch: Select a project, branch (or create a fresh isolated working branch), environment, and optional knowledge graph, then describe your requirements.
• Auto-start capability: Tasks begin processing automatically when created—no manual intervention required.
• Live conversation panel: Monitor real-time progress and send follow-up messages without leaving the board.
• Needs Attention column: Tasks requiring your review are clearly surfaced in their own dedicated space.
• Seamless review workflow: Jump to Review in Designer to inspect changes, then move to completed when satisfied.
• Advanced filtering: Sort by project, source, and time period, or search across all tasks.

📎 Multi-modal chat

You can now attach images and PDFs directly to your task prompt in Mission Control. Give Maia AI Agents a supporting diagram of the pipeline you want built, a screenshot of an error you're hitting, or a PDF containing the spec from your team—and they can work straight from the source instead of from your description of it.

• Attach images: Drop in a supporting diagram, screenshot, mockup, or whiteboard photo—perfect for pipeline diagrams or system designs you'd otherwise have to describe in words.
• Attach PDFs: Share a spec, requirements doc, or report you want Maia AI Agents to use as context for the task.

Use the attach (+) control in the prompt area to add an image or PDF to your message.

🔓 Bypass permissions mode

Bypass permissions mode is the opposite of Ask permission mode. Tool calls execute immediately, with no approvals, decisions, or check-ins—great for trusted, hands-off runs like prototyping or clearing a backlog in a sandbox. Under the hood it's the same as approving every permission prompt as it comes up; Maia AI Agents aren't unlocking new capabilities, only skipping the checkpoints.

We don't recommend leaving Bypass permissions on by default. Ask permission should be your normal mode. Flip to Bypass for specific tasks where you know what Maia AI Agents are doing and you've scoped the environment for it. Use it in safe, well-scoped environments:

• A disposable project or branch where unintended changes are easy to throw away (scratch branch, dev environment, sandbox project)
• A warehouse user/role with only the access Maia AI Agents need. Point them at a dev warehouse or a least-privilege role, not your production admin credentials
• Approval gates on when Maia AI Agents are working from untrusted input like a PDF or image you didn't author

Mission Control is currently user-scoped, meaning you'll only see your own tasks.

You can access Mission Control through the AI Agents icon in the left navigation. Click + Add task to launch your first task. Any tasks requiring your input will appear in the Needs Attention column.

Read the docs to get started.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows—we're all ears! Feel free to add any comments or questions below.

Want to get involved?

Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

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Welcome to this week's new features blog! We're bringing you a packed set of improvements that make Maia more powerful and intuitive than ever—from enhanced data exploration and expanded cloud support to streamlined variable management and brand-new pipeline testing. For all the details, read our docs changelog.

☁️ Maia runner for Google Cloud

The Maia runner for Google Cloud has been released, expanding hybrid deployment options beyond AWS and Azure. This new runner enables you to seamlessly integrate your Google Cloud infrastructure with Snowflake through Maia's hybrid architecture.

You can create a Google runner through the Runner manager, just like other runners. To streamline deployment, our public repository includes Terraform templates specifically designed for deploying the runner into Google Kubernetes Engine (GKE), providing a comprehensive Kubernetes-based deployment experience.

For more information, read our GKE deployment guide and explore the deployment templates in our public deployment library.

🔧 Public API now supports Azure DevOps

You can now provision Maia projects backed by Azure DevOps repositories directly through the Maia API. Once provisioned, Maia operates on your Azure DevOps repository as a machine user. This expansion gives you more flexibility in how you integrate Maia with your existing development workflows and repository management systems.

The Project Provisioning API guide walks you through the one-time prerequisite of granting the service principal access to your Azure DevOps organization and shows you how to use "provider": "azure-devops" in your request body.

🚀 Guided custom connector setup with Maia Team

Setting up custom connectors in Maia just got easier! You can now create custom connectors in Designer without interrupting your workflow, simply by asking Maia Team to help.

In the Maia Team chat interface, ask for help setting up a custom connector, and Maia Team will help you get started without ever leaving the Designer canvas. Maia Team will guide you through custom connector creation and can automatically add the new custom connector to your pipeline, making data ingestion from new sources faster and more intuitive.

🔐 Cloud credential environment overrides and APIs

We've released significant updates to cloud credentials that provide greater flexibility and automation capabilities:

• Cloud credentials now support environment overrides directly in the UI, giving you better control across different deployment stages.
• The Connections API endpoints can now manage cloud credentials programmatically, enabling zero-touch provisioning workflows for AWS, Azure, and GCP credentials.
• Combined with the Project Provisioning API endpoints, you can now fully automate the setup and management of your cloud infrastructure.

Learn more: Check out our updated Cloud provider credentials documentation and the Project Provisioning guide.

🧪 Pipeline testing is now live

You can now create tests for your pipelines! These enable you to perform tests with mock inputs, comparison tests for competitive migration equality, and operational tests like idempotency and temporal behavior.

To get started, make sure that Test suggestions is toggled in in your Maia Team chat interface settings. Maia Team’s built-in skill will then offer to create tests when building a new pipeline. Plus, when you make changes to a pipeline, it will automatically flag any existing tests that are affected.

Tests can also be triggered in bulk via the Maia API Test Execution endpoints. Check out the API reference at that link, or our Test pipelines documentation to learn more.

🖌️ Designer quality of life improvements

We've released a host of improvements for all users in Maia, ranging from variable management to sampling, and from warehouse data to pipeline notes. Read on for all the details!

📐 Manage variables while chatting with Maia Team

Managing variables in Maia just became much more user-friendly! The Variables panel has been relocated from the left panel in Designer to a tab at the bottom of the canvas. This means you can now manage variables while keeping Maia Team visible at the same time.

All variable capabilities remain fully available in the new location, including pipeline variables, project variables, grid variables, and environment overrides. Changes save automatically, eliminating the need for a separate save step.

🧩 Select variables in component properties

You can now search for and add pipeline variables directly in any ‘dual listbox’ type parameter, such as the Column Name parameter in the Table Input component.

Dual listboxes now let you search for and select variables alongside static options. Variables appear in the same picker and can be added to the selection like any other item. This solves a common workflow challenge where you could reference variables in single-value fields but not in multi-value parameters.

🔍 Explore warehouse data without leaving Maia

These improvements let you inspect tables and navigate across databases without interrupting your pipeline work:

• In transformation pipelines, the Schemas panel now shows column details and sample rows when you click a table or view.
• If you work in an environment that spans multiple databases, a database picker is now available when browsing warehouse data, letting you switch databases without changing your environment configuration. This drop-down is available in the Schemas panel and the Warehouse data tab when adding data directly to a pipeline.

🎯 Sampling filter is always visible

The filter field in the Sample data tab in Designer is now visible as soon as a component has been validated, rather than only appearing after a successful sample has been taken. As a result:

• You can now enter a data filter before taking your first sample, perfect for when you already know the filter you want to apply.
• If a sample request fails, the filter bar and retry controls stay on screen, allowing you to correct your input and resample without interruption.

🗒️ And a whole lot more…

We’ve also made the following improvements in Designer, designed to eliminate friction when building pipelines, making the design process smoother and more intuitive:

• Skip, copy, or delete multiple pipeline components at once — Selecting two or more components on the canvas now shows an action toolbar so you can act on the whole group in a single click.
• Shift-click to fine-tune a canvas selection — Shift-clicking an already-selected component now removes just that item from a multi-selection.
• Copy and paste canvas notes — Notes can now be copied and pasted on the canvas, including multi-note selections, so you can duplicate annotations as easily as pipeline components.
• Multi-select note action bar improvements — When multiple canvas notes are selected, a single unified toolbar applies color and other formatting changes to the whole group in one action.

📤 Post-processing and grid variable updates

You can now use grid variables in orchestration component post-processing, and export grid variables from child to parent pipelines. This new feature allows you to reuse values from child pipelines in parent pipelines, update grid variables after an orchestration component runs, and import existing Matillion ETL pipelines that use grid export features more easily. Interested? Read our Post-processing guide for more information.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows — we're all ears! Feel free to add any comments or questions below.

Want to get involved? Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

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📚 Introducing docs.maia.ai

We're excited to launch a major upgrade to how you access and interact with Matillion’s product documentation. This week, we announced docs.maia.ai, the new home for all of our product and API documentation for Maia (formerly Data Productivity Cloud).

docs.maia.ai is a modern, responsive docs site that we believe delivers a significantly improved experience for accessing Maia documentation.

Key improvements include:

• Subscribable changelog: Stay up-to-date with automatic notifications via RSS feed for our product and API changelogs. We’ve made our changelogs available in two formats: by year and by runner tracks. Check them out:
  • 2026 changelog (use the navigation if you want to look back through 2025, 2024, and 2023 releases).
  • Stable runner track changelog (current coming soon!)
• Interactive API playground: Test API endpoints directly in your browser with our native OpenAPI spec rendering, no plugins required.
• Cleaner reading experience: Modern, responsive design that works seamlessly across all devices, including mobile.
• Streamlined maintenance: More efficient content delivery as Maia continues to scale.
• Improved navigation: Find the content you want faster than before.

The new site includes integrated AI-powered support for answering your questions, and all previous documentation links automatically redirect to the new platform.

What are you waiting for? Explore docs.maia.ai today and use the thumbs-up/thumbs-down and feedback form to let us know what you think or how we can improve.

Terminology changes

Matillion originally launched Maia in June 2025 as the agentic AI workforce inside Data Productivity Cloud with the goal of providing our customers with the capabilities to exponentially increase the rate of efficiency and productivity with which they do data engineering.

In early 2026, we made the decision at Matillion to rebrand “Data Productivity Cloud” as a whole to “Maia” to clearly reflect our mission: to build the best AI data automation platform in the world.

Additionally, with the rise of agentic AI, we have opted to rename “agents” to “runners” to free up the term “agent” for AI-related nomenclature.

The following terminology changes have been made across our documentation and other assets (Academy, Certification, Support, etc) to reflect updates in our product naming:

Additionally, to reflect the broader scope of the name “Maia” as it replaces “Data Productivity Cloud”, the term “Maia AI Agents” now reflects the AI capabilities of Maia. You might also see Maia AI Agents referred to in our marketing material as Maia Team.

Matillion ETL documentation

While we have launched docs.maia.ai to supercharge your documentation experience for Maia, Matillion ETL users can still make use of docs.matillion.com, which remains the home of our product and API docs for Matillion ETL.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows—we're all ears! Feel free to add any comments or questions below.

Want to get involved?

Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

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Welcome back to the Maia New Features Blog! This week, we're excited to deliver updates that enhance your observability experience, make troubleshooting more intelligent, and unlock new programmatic possibilities with Maia. Let's explore what's new in our changelog updates.

🤖 Maia AI Agents' public API is here

We're excited to introduce the Agent Tasks API — Maia's first public API that lets you programmatically create, monitor, and manage tasks. This new capability allows you to send instructions to Maia AI Agents and have them executed as background processes. You now have the same level of control and agentic power via the API as you do when chatting to Maia AI Agents in Designer.

The API enables you to integrate Maia AI Agents’ problem-solving capabilities into your own workflows, CI/CD pipelines, or custom scripts—allowing Maia AI Agents to work through complex instructions as a background service.

What's new:

• From chat to API: Transition from manual 1-to-1 conversations to programmatic task creation. Define an objective for Maia AI Agents and manage the entire work lifecycle via the API.
• Asynchronous background tasks: Unlike a chat session that requires you to stay in the window, API tasks run independently. Send a request, receive a task ID, and check back whenever you're ready to review the output.
• Programmatic decision handling: Maia AI Agents still pause for safety when high-impact actions are needed. You can approve tool usage or answer clarifying questions through the dedicated endpoint to keep the task moving.
• Full Designer parity: You maintain the same level of granular control you have in the Designer—including the ability to manage permissions, track reasoning, and iterate on work—giving you complete command over Maia AI Agents' actions via the API.

How do I use it?

To get started, create a task by providing an initial instruction—this serves as your first prompt to Maia AI Agents. Once the task is created, you will receive a task ID which allows you to monitor the task’s status, handle decisions, and iterate on the task using follow-up messages.

Check out our API reference to try out the new endpoints, or read our guide to Using the Agent Tasks API for more details.

🔍 Root cause analysis gets smarter with enhanced pipeline visibility

Root Cause Analysis (RCA) has received significant improvements to make it more accurate and useful when pipeline failures occur. The enhanced RCA now provides comprehensive visibility across your entire pipeline infrastructure.

Key improvements include:

• Complete pipeline visibility: RCA now analyzes failures inside iterators and orchestration steps, not just top-level pipeline issues.
• Enhanced failure detection: Pipeline-level failures such as specific SQL errors are now captured and analyzed.
• Richer contextual analysis: Warning-level agent task logs are now included to provide the lead-up context needed to understand why failures occurred.
• Structured, actionable output: Every identified issue now includes a category and a clear indication of whether it's fixable within your pipeline.

For more information about Root Cause Analysis, check out our documentation.

💾 Saved views in Pipeline Run History

Pipeline Run History just got a major productivity boost! You can now save your filter sets and set a default view, eliminating the need to reapply filters every time you visit this page. Simply apply and save a view, and it’s there every time you need it.

💬 We'd love to hear from you!

Let us know how these new features are improving your workflows—we're all ears! Feel free to add any comments or questions below.

Want to get involved? Join the Matillion Community to stay up to date, share feedback, and help shape our product roadmap for future innovations.

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