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Honeycomb Launches Natural Language Querying for Observability Using Generative AI

Honeycomb announced that it is the first observability platform to launch fully-executing Natural Language Querying using generative AI for its new capability, Query Assistant.

This development dramatically scales the platform's query power and makes observability more usable for all engineering levels.

Honeycomb's new Query Assistant is a distinctly different approach to AI compared to what's been done historically by traditional APM and ops tools that apply AI to data analytics for features like automated alerting. This capability uses generative AI to enhance human intuition by allowing users, no matter how seasoned, to ask questions and get fast feedback on what's happening with their code.

Query Assistant joins Honeycomb's other human-first, machine-assisted debugging tools, such as BubbleUp. Used by engineering teams to quickly answer complex problems in their code, BubbleUp uses machine analysis to cycle through billions of high-cardinality data points (fields like userId, shoppingCartId, and orderId, etc.), visually compares problematic user experiences to healthy ones, and identifies the differences. This dramatically accelerates the debugging process by eliminating the time-consuming and error-prone legacy APM workflow of jumping from metrics dashboards to individual logs and traces to guess at problematic patterns.

"The best developer tools are increasingly going to be the ones that get out of your way and become invisible," said Charity Majors, CTO of Honeycomb. "Observability shouldn't require you to master complicated tools or languages that force you to constantly switch context and piece together clues to get answers to complex problems. The only thing observability tools should encourage you to focus on is your own curiosity about what's happening in your system."

Honeycomb believes that delivering superior user experiences is a team sport and makes significant investments in making observability usable for all. This is showcased in our unique pricing model that has no additional charge per service, host, memory, custom field, or seat as well as our collaborative team features like the ability to share query histories. With the addition of Query Assistant, anyone on the team can easily understand how their application code is behaving in the hands of real users in unpredictable and complex cloud environments. This new capability is a great first step for Honeycomb R&D to further explore how AI can be incorporated into the product to enhance the Honeycomb user experience.

Query Assistant is available to all Honeycomb users. As of today, it is an experimental feature that can be turned off by teams. No user data is passively sent to OpenAI, and no data is retained for training models.

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Honeycomb Launches Natural Language Querying for Observability Using Generative AI

Honeycomb announced that it is the first observability platform to launch fully-executing Natural Language Querying using generative AI for its new capability, Query Assistant.

This development dramatically scales the platform's query power and makes observability more usable for all engineering levels.

Honeycomb's new Query Assistant is a distinctly different approach to AI compared to what's been done historically by traditional APM and ops tools that apply AI to data analytics for features like automated alerting. This capability uses generative AI to enhance human intuition by allowing users, no matter how seasoned, to ask questions and get fast feedback on what's happening with their code.

Query Assistant joins Honeycomb's other human-first, machine-assisted debugging tools, such as BubbleUp. Used by engineering teams to quickly answer complex problems in their code, BubbleUp uses machine analysis to cycle through billions of high-cardinality data points (fields like userId, shoppingCartId, and orderId, etc.), visually compares problematic user experiences to healthy ones, and identifies the differences. This dramatically accelerates the debugging process by eliminating the time-consuming and error-prone legacy APM workflow of jumping from metrics dashboards to individual logs and traces to guess at problematic patterns.

"The best developer tools are increasingly going to be the ones that get out of your way and become invisible," said Charity Majors, CTO of Honeycomb. "Observability shouldn't require you to master complicated tools or languages that force you to constantly switch context and piece together clues to get answers to complex problems. The only thing observability tools should encourage you to focus on is your own curiosity about what's happening in your system."

Honeycomb believes that delivering superior user experiences is a team sport and makes significant investments in making observability usable for all. This is showcased in our unique pricing model that has no additional charge per service, host, memory, custom field, or seat as well as our collaborative team features like the ability to share query histories. With the addition of Query Assistant, anyone on the team can easily understand how their application code is behaving in the hands of real users in unpredictable and complex cloud environments. This new capability is a great first step for Honeycomb R&D to further explore how AI can be incorporated into the product to enhance the Honeycomb user experience.

Query Assistant is available to all Honeycomb users. As of today, it is an experimental feature that can be turned off by teams. No user data is passively sent to OpenAI, and no data is retained for training models.

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

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In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...