The Observability Journey: From Events to Logs and Back
Why Legacy Observability Breaks with AI
Observability is at a crossroads, and AI only makes it harder. Enterprises pour millions into telemetry yet remain blind to how their AI actually behaves. Cost, scale, and context have peaked under decades-old architectures, but history offers clues for what comes next.
To see that full circle, let me share my own observability journey.
Phase 1: Rise of APM
My observability journey began in 2002 at Wily Technology, the pioneer that coined “APM” (Application Performance Management) that became the foundation for observability. Wily saw that Java application servers were the plumbing for enterprises building web applications that lacked monitoring and management, a vision that slashed mean-time-to-remediation and APM became enterprise standard. However, APMs did not provide the full picture.
Phase 2: The Era of Centralized Logs
In 2005, I joined Splunk’s founding team to launch v1. As the “big-data analytics pioneer,” Splunk gave IT and SecOps a Google-style search over logs vs. dealing with rule-based events that were built on relational databases. Centralizing logs drove huge gains with compliance and troubleshooting at another scale. Fast forward today, APM and Log Management have become synonymous, but moved under the umbrella of Observability, thus creating challenges with AI.
Phase 3: The AI Observability Gap
Enterprises spend up to $10 million a year on telemetry, yet still lack real-time behavioral correlation, leaving AI decisions opaque. Traditional observability tools, built for request-response infrastructure, revolve around logs, metrics, and traces in static, index-based pipelines. That model collapses under AI’s demands:
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Unbounded volume & velocity. AI workloads generate trillions of events, overwhelming legacy indexes.
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Behavioral & contextual data. Model interactions are non-deterministic, asynchronous, and often unstructured.
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Human bottlenecks. Manual instrumentation and centralized dashboards introduce latency, drift, and noise.
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Tool sprawl. Indiscriminate log ingestion balloons costs and alert fatigue.
Some newer platforms cut log volumes, but they leave a downstream intelligence gap. Without true event correlation, teams still chase needles in a haystack.
Gartner has recognized that sheer data collection no longer scales, rechristened AIOps category as Event Intelligence Solutions, ushering in a new era where smart event correlation replaces brute-force ingestion (April 2025).
“AIOps (event intelligence) is inescapable for the future” -Gartner
How Event Intelligence Bridges the Gap
Rather than chase every log, event intelligence flips the script:
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Collect Smarter. Ingest only high-value signals. Company X cut alert noise by 80% in weeks.
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AI-first Correlation. Automate behavioral stitching at the source, no brittle rules or upfront schemas.
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Context on Arrival. Deliver events pre-enriched with topology, baselines, and behavioral metadata.
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These actionable events empower teams to move from raw data to rapid decisions without drowning in telemetry.
A Nod to the Past and a Look Ahead
That’s why I’m excited to introduce Behavure AI: The Machine Data Engine for AI. We deliver real-time behavioral event correlation as the connective tissue between raw telemetry and true AI observability, unifying measurement, governance, and keeping humans in the loop at AI scale.
Behavioral Event Intelligence extracts self-describing “who/what/where/why” events at the source to process only the signals that matter, with no upfront schemas or brittle rules, dramatically cutting costs and speeding intelligence at AI scale.
“To handle the scale of tomorrow, you need machines (AI) to help, and those machines need to make sense of events, not just store data.” –Gartner
Team with experience building and using data platforms
Our team comes from Meta, LinkedIn, Splunk and Sonos, having scaled infrastructure to billions of users and processed trillions of events. Our collective experience positions Behavure to redefine AI Observability.
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