Operations | Monitoring | ITSM | DevOps | Cloud

Agentic AI is coming to financial services. Elastic provides the data foundation and tools to make it work. In a recent talk at Stanford University, Jamie Dimon, chairman and CEO of JPMorganChase, addressed the firm’s use of AI and ended with mentioning that agentic AI was the next frontier of AI at the firm, inferring it wasn’t ready to be deployed yet. Let’s break down why that may be the case and what the financial services industry can do to become more comfortable with agentic AI.

In the world of log management and security analytics, one thing is abundantly clear: data volumes fluctuate. Yet most pricing models haven’t caught up. Traditional ingest-based licensing models force organizations to size their license needs based on a worst-case capacity scenario—the “high-water mark”—whether those spikes are rare and/or expected.

Loki is a powerful, scalable log aggregation system designed by Grafana to efficiently collect, store, and query logs. It’s often deployed alongside Prometheus as part of modern observability stacks. Loki’s design emphasizes cost-effective storage by indexing only metadata, which makes it a great choice for high-volume environments. But while Loki excels at log ingestion and indexing, many teams overlook the critical task of monitoring Loki itself.

Artificial Intelligence (AI) is revolutionizing businesses across industries. From personalized customer experiences to predictive analytics and process automation, there are hardly any sectors untouched by AI's impact. Its applications in data management aren't left behind. In fact, AI has the potential to transform traditional data management practices.

How SWIFT and Elastic are tackling infrastructure complexity, false alerts, and rising compliance demands.

React logging is the practice of recording relevant information about your application's behavior during runtime. Unlike traditional server-side logging, React logging happens in the browser and focuses on frontend concerns: component lifecycle events, state changes, user interactions, performance metrics, and network requests. Effective logging creates breadcrumbs that help you understand application flow and quickly pinpoint problems.

Troubleshooting containerized applications can quickly become complex when logs are scattered across multiple systems. Most DevOps teams face this challenge daily—what starts as a simple container deployment often evolves into a complex logging puzzle. This guide explores Docker logging drivers in depth, covering configuration options, best practices, and practical solutions.