Dynatrace Davis

Dynatrace 2026: Davis Hypermodal AI & Smartscape Topology Review

As of 2026, the Dynatrace Davis architecture has transitioned into a hypermodal framework, unifying causal AI for deterministic troubleshooting, predictive AI for forecasting, and generative AI for natural language orchestration 📑. The engine operates natively atop the Grail Data Lakehouse, which provides a schema-on-read capability that eliminates the need for manual data indexing or pre-defined management of high-cardinality metrics 📑.

Causal AI & Smartscape Dependency Logic

The technical core of Davis is its ability to traverse the Smartscape topology, a real-time vertical and horizontal map of the entire environment. This enables the engine to distinguish between causal triggers and downstream symptoms 🧠.

• Dependency Precision: Unlike probabilistic models, Davis uses the actual process-to-process communication paths captured by OneAgent to validate fault propagation 📑.
• Operational Scenario (Causal RCA): Input: Anomaly detection triggers on service latency + CPU spike on a host + Smartscape topology update → Process: Davis traverses the graph, identifying a recent automated canary deployment as the root node while marking 50 secondary alerts as mere symptoms → Output: A single 'Problem' ticket identifying the specific container image version and deployment ID causing the regression 📑.

⠠⠉⠗⠑⠁⠞⠑⠙⠀⠃⠽⠀⠠⠁⠊⠞⠕⠉⠕⠗⠑⠲⠉⠕⠍

Grail Data Lakehouse & Hypermodal Reasoning

The Grail architecture facilitates the hypermodal transition by providing a unified storage layer for traces, logs, and metrics without data silos. This allow Davis to perform 'cross-context' reasoning across massive datasets at scale 📑.

• Predictive Capacity Management: Davis analyzes seasonal trends and resource consumption rates to forecast potential outages 📑.
• Operational Scenario (Predictive Scaling): Input: 30 days of disk usage metrics + application growth rate + upcoming seasonal event metadata → Process: Predictive AI calculates the exhaustion point (T-minus 48 hours) while the Orchestration layer checks available cloud quotas → Output: Automated expansion of the storage volume or a proactive alert to the SRE lead with a suggested quota increase 📑.
• Davis CoPilot: Acts as the generative interface for the Grail backend, translating natural language into specialized DQL (Dynatrace Query Language) for rapid forensic investigation 📑.

Evaluation Guidance for Platform Architects & SRE Leads

Architects should evaluate the data ingestion costs associated with Grail's high-fidelity storage compared to traditional tiered sampling methods. SRE teams should validate the reliability of the Agentic Orchestrator's self-healing runbooks in non-production environments to establish trust in the hypermodal logic ⌛. Confirm that the 'Davis CoPilot' privacy layer meets internal data masking requirements for log visibility 🌑.