Dynatrace Intelligence: Powering the Shift to Autonomous Digital Operations

As cloud environments and AI adoption accelerate, enterprise digital ecosystems are becoming more complex than ever. Executives are under pressure to harness AI not just for innovation — but for operational reliability at scale.

Yet many organizations are discovering a critical reality: agentic AI alone isn’t enough.

While 65% of enterprises are investing in AI-driven monitoring and automation, what leaders truly need is trusted, AI-powered observability — the foundation required to move from human-driven operations to human-supervised autonomous systems.

At the center of this transformation is Dynatrace Intelligence — an agentic operations system designed to deliver reliable, real-time autonomous action across complex digital environments.

Executive Takeaways

• Dynatrace is architected for real-time autonomous operations while integrating seamlessly into broader AI ecosystems.

• A pioneer in large-scale AI-powered root cause analysis, Dynatrace now evolves from automation to true autonomous action: auto-prevention, auto-remediation, and auto-optimization.

• Dynatrace balances deterministic AI, contextual analytics, and stochastic AI to produce precise answers and reliable actions.

• Dynatrace Intelligence orchestrates built-in and external agents across ecosystems.

• Real-time production feedback loops enable AI engineering, AI operations, and agentic SRE at enterprise scale.

Reducing AI Hallucinations in Agentic Systems

One of the greatest concerns among CTOs building agentic frameworks is hallucination risk in generative AI.

In autonomous systems, hallucinations are not minor errors — they can trigger:

• Service outages

• Security incidents

• Financial exposure

• Cascading operational failures

In agentic workflows, inaccuracies can accumulate across multiple decision steps, amplifying risk.

The Dynatrace Approach: Deterministic First

Dynatrace minimizes hallucination risk by prioritizing deterministic AI — ensuring answers are grounded in real-time production data rather than probabilistic guesses.

The platform continuously observes and enriches context through:

• Real-time dependency graphs

• Production telemetry

• High-performance analytics

• Business and infrastructure context

This approach ensures AI acts on verified facts — not assumptions.

Solving the Data Scale Challenge

Large language models cannot directly process petabytes of heterogeneous observability data. Context windows are finite, and performance degrades when overloaded.

Simply “dumping” massive datasets into AI prompts reduces quality.

The solution? Rapid distillation.

This is where Dynatrace Grail becomes critical.

Real-Time Analytics at Exabyte Scale

Grail unifies observability, security, and business data within a single AI-optimized data lakehouse.

Key capabilities include:

• Exabyte-scale processing

• Metrics, logs, traces, user behavior, and security event ingestion

• Schema-on-read architecture (no indexing overhead)

• Zero-latency, always-hydrated storage

• Any-question, any-time analytics

By distilling vast telemetry into high-quality contextual insights, Grail feeds agents with precise and actionable intelligence.

Instant Visibility Through Dependency Mapping

Complementing Grail is Dynatrace Smartscape, a continuously updated real-time dependency graph.

Smartscape maps:

• Services

• Infrastructure

• Business processes

• Teams

• Ownership structures

• Risk relationships

The newest generation supports custom entities and metadata — allowing teams to instantly understand impact and ownership for rapid remediation.

Together, Grail and Smartscape ensure that AI operates on contextual truth, not fragmented data.

Inside Dynatrace Intelligence

Dynatrace Intelligence acts as the unified intelligence layer within the Dynatrace platform.

It fuses deterministic AI with agentic AI to create a reliable operational core where:

• Humans define goals

• AI executes with guardrails

• Policies ensure compliance

• Context ensures precision

Foundational Deterministic Agents

At its core are foundational agents:

• Root Cause Agent – Powered by deterministic causal AI, delivering precise answers faster than LLM-only methods.

• Analytics Agent – Distills Grail’s data into contextual intelligence.

• Forecasting Agent – Expands predictive insights across environments.

• Operator Agent – Orchestrates and coordinates multi-agent workflows.

These foundational agents power every other domain-specific agent.

Domain-Specific Agentic Capabilities

Built on this foundation are ready-made agents tailored for:

• Development teams

• SRE teams

• Security teams

These agents can:

• Detect anomalies

• Predict incidents

• Identify root causes

• Conduct supplementary investigations

• Generate and execute corrective action plans

This enables:

• Auto-prevention

• Auto-remediation

• Auto-optimization

Real-World Examples

For developers:

• Detects rising mobile app crashes

• Analyzes impacted code paths

• Generates fix recommendations in seconds

For security teams:

• Continuously monitors threats

• Identifies related vulnerabilities

• Enables proactive mitigation before exploitation

Assist Agents simplify platform adoption, while Agentic Workflows allow customers to build custom agents tailored to their needs.

Ecosystem Integration

Dynatrace Intelligence orchestrates bi-directional integration across the broader agentic ecosystem, including:

• Amazon Web Services

• GitHub

• ServiceNow

• Microsoft Azure

• Atlassian

It can:

• Submit tickets

• Trigger coding agents

• Assess deployment risk

• Adjust infrastructure

• Provide incident intelligence

• Deliver business impact analysis

This coordination transforms observability into action.

The Journey to Fully Autonomous Operations

Organizations progress through three maturity stages.

1. Automated

Pre-defined workflows execute automatically based on AI-generated insights.

Key questions:

• Can it be automated?

• Can it be observed?

• Can behavior be understood in real time?

Automation requires observability and testability.

2. Supervised Autonomous

AI generates execution-ready plans with reasoning — humans approve and supervise.

Core principles:

• Reliability – Deterministic AI anchors decisions.

• Transparency – Humans define goals and guardrails.

• Feedback Loops – Real-time validation ensures continuous refinement.

Organizations typically adopt a crawl–walk–run approach, beginning with repetitive, well-bounded tasks.

3. Fully Autonomous

In this future state, Dynatrace Intelligence independently fulfills business objectives, requesting human input only when required.

Even in this stage:

• AI continuously observes itself

• Compliance is maintained

• Humans refine goals and strategies

The result:

• Higher resilience

• Lower operational cost

• Improved customer experience

A New Standard for AI-Powered Observability

The fusion of deterministic AI and agentic AI sets Dynatrace apart.

It is AI that:

• Observes production systems

• Observes other AI systems

• Acts on real-time facts

• Reduces hallucinations

• Enables resilient autonomous operations

Dynatrace Intelligence is not just automating workflows — it is redefining how enterprises design, operate, and optimize digital ecosystems.

From human-driven operations to trusted autonomous systems — this is the next evolution of enterprise AI.

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