Overview

Observability demands are growing rapidly—but many teams are still overpaying for data and drowning in manual alert triage.

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As telemetry volumes increase across complex systems, relying on traditional, passive dashboards leaves engineering teams to manually sift through messy raw logs during critical outages. This static approach drives up operational burnout and creates runaway cloud or AI token costs when scaling basic AI prototypes.

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In this session, we take a strategic look at how to shift from reactive monitoring to active, automated assistance. You’ll learn how the Elastic Agent Builder acts as an intelligent "on-call assistant," transforming raw infrastructure telemetry into clear, plain-English troubleshooting checklists while keeping cloud budgets strictly under control.
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What You'll Learn

• How Elastic Agent Builder turns complex, raw system data into plain-English troubleshooting checklists.
• How to reduce operational burnout for senior engineers while helping junior team members isolate system failures faster.
• How to identify and eliminate the financial risk of runaway AI costs when scaling basic agent prototypes.
• How condensing telemetry streams ensures the AI assistant only receives high-signal data without wasting expensive tokens.
• Where Elastic's built-in tools stop and Hyperflex’s hands-on backend engineering begins to ensure massive enterprise scale.
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Real-World Applications

Replacing traditional alert charts with proactive AI assistance ; converting static runbooks into real-time troubleshooting workflows ; reducing day-to-day backend management overhead for platform teams ; condensing massive telemetry streams to prevent runaway token bills ; and moving from basic AI prototypes to resilient enterprise-grade systems.
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Practical Insights

• Designing context-engineered workflows for high-ingestion telemetry environments.
• Cleaning and filtering data streams so AI assistants only receive high-signal information.
• Automating diagnostic workflows by live-configuring an Elastic Agent inside Kibana without relying on slides.
• Eliminating runaway LLM API bills through precise data and token management.
• Tuning underlying backend cluster architecture and indexing strategies to sustain enterprise-scale automation.

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