Enterprise automation rarely fails because teams lack ideas. It fails because scaling automation safely is challenging, and conventional workflows often remain static after they ship, while systems and conditions continue to evolve.

The new update to Dynatrace Workflows helps teams accelerate their innovation loop and run automation like production software. Five improvements make it easier to iterate and operate at scale: workflow drafts, sub-workflows, approval requests, persistent execution data, and real-time notifications. Together, they support safer change, reusable standards, built-in governance, and data-driven refinement.

The innovation loop in enterprise automation

The innovation loop is a repeatable cycle for quickly and safely improving enterprise automation that treats workflows as living systems that evolve with your environment, self-improving as systems, teams, and requirements change, rather than remaining static:

• Teams start by designing or updating a workflow and validating it safely before it goes live.
• Next, they standardize the parts that work into reusable building blocks so future workflows are faster to create and more consistent.
• Then they add governance where it matters, such as approval checkpoints for high-impact actions, so speed does not bypass policy.
• After execution, they observe what actually happened using execution records and signals, including errors, latency, and outcomes, to understand reliability and impact.
• Finally, they refine the workflow based on those findings by tuning logic, tightening controls, improving reuse, and adjusting notifications.

The result is a compounding impact: faster delivery over time, fewer incidents at scale, automation that integrates changing circumstances, and greater organizational trust, as transparency, auditability, and tight feedback loops back improvements.

How Dynatrace Workflows accelerates the innovation loop

Dynatrace Workflows helps you accelerate your innovation loop with powerful new features designed for adaptability, collaboration, transparency, and control. Each feature is designed to empower teams to safely iterate on automation in dynamic enterprise environments.

Faster iteration with safer testing and experimentation with workflow drafts

The new workflow drafts feature allows teams to create, edit, and refine workflows in a draft state, allowing them to validate logic and configuration without triggering live automation. Workflows that remain in draft mode don’t consume workflow hours.

Workflow drafts support the first step of the innovation loop: iterate safely. Teams can make changes, review them with peers, and confirm the workflow is ready before anything runs in production.

Key benefits

• Reduce risk by testing configuration changes without production impact.
• Allow faster iteration and cleaner change management.
• Improve collaboration during the design and review stages.
• Control costs with included workflow-hour consumption for draft-only workflows.

Standardize reusable blocks for reliability and speed with sub-workflows

Sub-workflows allow a workflow to be executed as a task inside another workflow, so teams can package repeated logic into reusable sub-workflows and compose larger automations from consistent building blocks.

This supports the “standardize” step of the innovation loop. When teams standardize and reuse proven components, automation becomes more consistent, easier to maintain, and quicker to expand across teams and environments.

Key benefits

• Enhance reliability and consistency by reusing proven workflow components.
• Speed up delivery by composing workflows from standard blocks.
• Simplify maintenance by updating shared logic in a single location.
• Keep complex automations understandable by breaking them down into smaller, manageable units.

Example use cases

• Notification workflows: Automatically send notifications based on incident severity or timing.
• Data processing workflows: Run filter/transform/store stages as distinct sub-workflows, processing large datasets in stages.
• Incident response workflows: Automate repetitive tasks as reusable steps, such as creating tickets, logging incidents, notifying stakeholders, and triggering remediation steps.

Supervised human-in-the-loop control where it matters with approval requests

With Dynatrace Workflows, you can now insert an approval request into a workflow and pause execution until a human reviews and approves the next step.

This feature supports the “governance” step in the innovation loop. Approvals turn your workflows into supervised autonomous operations. This allows the automation of more processes while ensuring strict compliance with enterprise requirements for governance, risk management, and accountability, particularly for high-impact actions.

Key benefits

• Add explicit human control at high-risk decision points.
• Reduce errors in sensitive operations (changes, remediations, escalations).
• Ensure compliance with organizational standards.
• Increase trust in automation across security, ops, and platform teams.
• Minimize operational risks.

End-to-end visibility with an audit trail and persistent execution data

Dynatrace Workflows now persists workflow execution data in DQL. All workflow activities are recorded as system events in the dt.system.events table, creating a queryable execution record for tasks, states, and outcomes as a comprehensive audit trail.

Persisting execution data supports the “observe” step of the innovation loop. When workflow execution is captured as data, teams can troubleshoot failures, provide transparency, prove what happened, and improve automation based on evidence rather than anecdotes.

Key benefits

• Provide an audit trail for enterprise traceability and review.
• Monitor workflow health and reliability through execution outcomes.
• Speed up troubleshooting by pinpointing where failures occur.
• Optimize automation using DQL-driven actionable insights (error rates, trends, hotspots, and more)

Example: Workflow health overview

This example demonstrates how to use execution data to support users in identifying those workflows that need improvement.

The following DQL query…

fetch dt.system.events
| filter event.kind == "WORKFLOW_EVENT" and event.provider == "AUTOMATION_ENGINE" and event.type =="TASK_EXECUTION"
| filter dt.automation_engine.state == "ERROR"
| summarize count = count() , by:{dt.automation_engine.workflow.title}

…counts task execution errors per workflow. Using a honeycomb visualization, it displays error distribution by workflow and provides visibility into those workflows that fail too often.

Instant alerts for failures and workflow changes with real-time notifications

Easily set up and customize real-time notifications by email for all workflow errors or changes made by others, ensuring you’re always informed.

These real-time notifications support the “respond and refine” step of the innovation loop. Proactive alerting allows you to manage workflows effectively, minimize disruptions, and maintain seamless operations.

Key benefits

• Get immediate visibility into failures to address potential issues before they escalate.
• Detect workflow changes early to reduce drift and surprises.
• Improve operational coordination across teams that share workflows
• Ensure uninterrupted operations with timely, actionable alerts.

Empower your team: Build smarter, safer, and scalable workflows today

Scaling automation today is hard, changes are risky to test, automation becomes outdated and buggy as soon as one part of the organization makes changes, and when something inevitably breaks, you’re left hunting for answers across scattered logs and screenshots. But it doesn’t need to be like this.

Are you ready to make things easier, run your workflows like production software, and accelerate your innovation loop? Here’s how you get started:

• Explore our documentation to learn more about these features.
• Join the Dynatrace Community to connect with other users and share your insights in the Workflow and AutomationEngine feedback channel.

The post Reliable enterprise automation at scale: Accelerate the innovation loop with Dynatrace Workflows appeared first on Dynatrace news.

• Your AI investments can’t reach their potential without effective AI governance.
• AI governance is a challenge that demands unprecedented agility, proactive measures, and comprehensive oversight to manage complexity.
• With Dynatrace, you’re prepared for whatever comes next.

Stay compliant and build trust in your AI systems

AI regulation is tightening, and non-compliance is becoming a huge risk to broader, production-scale AI adoption. Penalties are only part of the impact: reputational damage, customer mistrust, and stalled innovation can cripple even forward-looking organizations. That’s why we’re introducing data governance and audit trails for AI observability: a scalable way to manage, monitor, and secure the AI data lifecycle with end-to-end lineage, retention controls, and evidentiary records of model and user interactions.

Our platform helps you turn governance into a competitive advantage. Built-in audit support helps with emerging regulations like the EU AI Act, and alignment to industry standard frameworks such as NIST AI and ISO/IEC 42001:2023.

The hidden challenges of AI data governance

The complexity of compliance

AI regulations are becoming stricter, and new regulations are on the horizon. Organizations must maintain detailed records of AI activities for years, ensure transparency of data and processes, and align retention policies with legal requirements. These measures are imperative for trust and safety, but they introduce significant challenges. For instance, AI-related events are often scattered across multiple systems, applications, and teams, complicating efforts to create a unified audit trail. Default retention periods can fall short of regulatory needs, and manual governance processes are error-prone and infeasible at scale.

The risk of non-compliance

Failing to meet regulatory standards risks hefty fines and penalties, but the market consequences, reputational damage, and loss of customer trust are even worse. Without the right tools, organizations will struggle to manage the growing complexity of AI data governance and reap the full benefits of AI investments.

Introducing Dynatrace data governance and audit trails

Dynatrace has a long history of empowering organizations to tackle complex challenges with AI-driven solutions. Building on this expertise, we’re introducing a new set of capabilities designed to simplify compliance, enhance transparency, and streamline data management. With Dynatrace, you can:

• Automatically retain AI-related events for up to 10 years in Grail®, our secure data lakehouse.
• Monitor and capture events from platforms like Amazon Bedrock, tracking everything from model deployments to fine-tuning activities.
• Leverage OpenTelemetry to collect real-time traces and metrics of AI workloads, along with every AI user interaction, giving you a complete picture of your AI ecosystem.

Close the compliance gap with embedded oversight

What sets Dynatrace apart is seamless integration with your existing workflows. With OpenPipeline® on Grail, you can route AI-related events to custom storage buckets with extended retention, automatically, and without forcing teams to change tools or processes. This allows long-term auditability and helps meet sector-specific compliance requirements that might require special retention and auditability measures.
Once configured, Dynatrace can automatically route and store events, creating a reliable and transparent audit trail. This helps to reduce fragmentation, tool sprawl, and manual effort traditionally associated with data governance.

Imagine being able to trace every user interaction, model training session, or deployment event with just a few clicks. Dynatrace makes this possible by consolidating fragmented data into a single, coherent view. Whether you’re responding to a regulatory inquiry or optimizing your AI models, you’ll have the insights you need, when you need them.

Simplified and instant data filtering with Dynatrace segments

Not all audit data carries the same compliance weight. For global enterprises with complex IT environments, the ability to instantly filter data by precise criteria is essential for accelerating compliance across diverse regulations, from strict local regulatory transparency obligations to lighter regimes elsewhere.

Dynatrace segments make it simple to break down and filter data to match your analysis needs and regulatory requirements:

• Targeted compliance views: Instantly filter audit data by region, environment, platform, model, or custom criteria to align with diverse regulatory requirements.
• Dynamic adaptability: Segments automatically update, for example, when new LLM models or environments are introduced, minimizing manual maintenance and keeping governance current.
• Reusable assets: Leverage a single dashboard or notebook across multiple use cases by simply applying different segments, reducing duplication of effort.
• Noise reduction: Exclude irrelevant data such as development or test logs to keep compliance and observability focused on what truly matters.
• Custom team-context: Provide different teams (for example, compliance, data science, operations) with clear, filtered views of their audit data, ensuring ownership and audit-readiness across departments.

From observability to trusted automation

The future of AI governance lies in proactive, automated solutions that not only meet today’s regulations but also anticipate tomorrow’s challenges. With Dynatrace, you’re not just complying—you’re building a foundation of trust and reliability that scales with your business. By capturing and integrating AI events into a unified platform, Dynatrace transforms compliance from a burden into a strategic advantage.

Get started today

Ready to simplify your AI data governance?

Try it out yourself on the Dynatrace playground. Or, learn how to configure AI governance in our documentation.

Read more

• Part one of the Rise of Agentic AI blog series covers the fundamentals of AI agents, models, and emerging communication standards such as Agent2Agent (A2A) and MCP.
• Part two of the Rise of Agentic AI blog series explores AI agent observability and monitoring, A2A and MCP communications, and how to scale and monitor Amazon Bedrock Agents.
• Part three explains how to monitor Amazon Bedrock Agents and how observability optimizes AI agents at scale.
• Part four covers full-stack observability for AI with NVIDIA Blackwell and NVIDIA NIM.
• Part five demonstrates how to build a simple agentic application using the OpenAI Agents SDK and instrument the data with Dynatrace.
• Part six explores AI Model Versioning and A/B testing for smarter LLM services.

The post The rise of agentic AI part 7: introducing data governance and audit trails for AI services appeared first on Dynatrace news.