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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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Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...

Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

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Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...

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