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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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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

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In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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