Senior Product Manager

Honeycomb and Embrace have launched a strategic partnership integrating Embrace's real user monitoring for mobile and web into Honeycomb's observability platform. The integration enables engineering teams to correlate frontend performance data with backend system health, addressing a longstanding gap where backend teams lacked visibility into user experiences whilst frontend developers used disparate tools. Built on OpenTelemetry, the partnership allows session data, crash signals, network insights and Core Web Vitals from mobile and web applications to flow directly into Honeycomb. This creates a unified view across the entire stack using a shared data standard. The integration is available through AWS Marketplace. Embrace customers include AllTrails, Best Buy, Ford and The New York Times. The company has raised Series B funding from NEA, Allianz, Greycroft and Eniac.

Embrace partners with Honeycomb to unite frontend performance depth with backend observability precision. partnership Here's the big news: embrace.io has launched a strategic partnership with Honeycomb to bring web and mobile RUM to the industry-leading services and infrastructure reliability platform its friends at Honeycomb are building. The reasons go deeper than the product. embrace.io built Embrace on OpenTelemetry because embrace.io believe the future of observability is open, composable, and high-fidelity. embrace.io has always believed that frontend and mobile performance and reliability aren't a separate discipline from observability, they're just the part of the system that legacy platforms didn't reach. Honeycomb has pioneered the same philosophy embrace.io has been building on at Embrace on the backend: that high-cardinality, context-rich data lets engineering teams understand systems in ways that weren't previously possible. That shared foundation, around high-fidelity data and OpenTelemetry, makes this partnership much more than a typical integration, and will help entire engineering organizations better understand highly complex systems and deliver better experiences for every user on every screen. Scattered tooling and separate workflows. Backend and reliability teams have had strong observability options for a while now. Honeycomb has led the pack with a best-in-class high-cardinality model for services observability, and a genuine commitment to OpenTelemetry. But engineers across teams kept telling embrace.io the same thing: they wanted to know what end-users were experiencing on the other side of the API call. Mobile and web developers didn't want shallow RUM tools bolted onto an expensive "all-in-one" platform that wasn't built for them, so they'd seek out point solutions with zero ties to the reliability teams. Client-side environments like mobile apps are notoriously complex to troubleshoot. The result is predictable: no shared data, duplicate investigations, and a structural disconnect between the teams who need to collaborate the most. The pain isn't subtle. An SRE gets paged at 9am on a Monday, narrows a latency spike to a specific endpoint, and then has to stop and ask: is anyone actually feeling this? They have no way to know without calling someone on the mobile team. Or, a mobile engineer sees an ANR spike on the checkout screen and spends twenty minutes trying to rule out a client bug before they can even ask for backend help. A frontend engineer watches a Core Web Vitals metric deteriorate for three weeks because it never crossed the threshold that would have paged someone (yet the users felt it). These aren't edge cases. They're the standard experience for engineering teams working on user-facing software. And they all share the same problem: the ability to get answers stops at the API boundary. What changes when the data is connected and tooling depth matches expertise. Because both platforms are built on OpenTelemetry, user telemetry from Embrace flows into Honeycomb as standard, enriched, composable data with the same format and same dimensions, queryable with the same tools. No proprietary schema, no translation layer. In practice, this changes how three kinds of engineers work: * The SRE on-call starts in Honeycomb and ends in Embrace. When a backend issue fires, Honeycomb narrows the problem to a specific service and traffic slice. From there, one click surfaces the affected real user sessions in Embrace, including who they are, how many, and what they were doing when things went wrong. That's usually enough to decide whether it's worth waking up the frontend engineering team. Honeycomb tells them the "where," Embrace tells them the "who," "how many," "what customer activities are affected," and "how bad." * An app engineer starts in Embrace and ends in Honeycomb. When a checkout flow in the app is freezing (and frustrating users), Embrace quickly shows whether the problem is on the device or somewhere upstream. If it's upstream, one click opens the matching backend trace in Honeycomb, and the engineer can hand off a specific, evidence-backed ticket instead of a hunch. * The web performance / frontend engineer loops between both. Honeycomb catches a Core Web Vitals metric drifting in the wrong direction before it crosses an alert threshold. Embrace gives per-page and per-interaction breakdowns along with Synthetic analysis. They identify a recently-shipped vendor SDK as the cause, ship a fix, and can confirm the improvement in Honeycomb within a day. What this means for composable observability, and for its AI-driven future. The handoff points work because the data is open and the philosophy is shared. The session ID in a Honeycomb span drives into Embrace where users can explore populations of affected users, and the trace ID in an Embrace network span is a link to Honeycomb where you can explore the dynamics of service behavior. The data doesn't stop at the API boundary anymore. Today's integration is the foundation, not the destination. Where embrace.io is headed is a single observability surface where any question about a service, a user, a revenue line, or a cohort on a specific device build has one place to live. That means BubbleUp running on the joint event set, SLOs that incorporate real user experience scores, and web and mobile apps treated as first-class services. Drift on either side gets cross-checked automatically before any threshold fires, and the impact gets surfaced in language that product and leadership can actually act on, not just the engineering team. Engineering teams worry less about the tooling and more about their end-users. That also matters for where AI-driven development is heading. When an AI agent can pull backend trace data from Honeycomb and jump directly to the affected user sessions in Embrace, it can traverse the full stack in one step and surface the context that tells a developer whether to page the team or close the ticket. When Honeycomb's querying capabilities and Embrace's MCP server are working together inside an agentic workflow, the on-call engineer supervises rather than assembles. The quality of that agent's answer is only as good as the data it can reach, of course. Connected data means connected reasoning, and embrace.io'll have a lot more to share on that soon. Engineering teams that have been treating web and mobile performance as separate from their observability practice can now bring it into the same workflow, the same data, and the same tools. That's the promise, and embrace.io is excited to deliver it with Honeycomb.

Honeycomb unveils Agent Timeline, Canvas Agent & Skills for AI observability. Honeycomb.io, the observability platform built for the new shape of software, today introduced a series of agentic intelligence and agent observability features purpose-built for AI agents in production. These new and enhanced capabilities include Agent Timeline, Canvas Agent, and Canvas Skills. Engineering teams will now have real-time visibility into what their agents are actually doing without proprietary software development kits (SDKs) or framework lock-in. AI agents are rapidly handling tasks such as code generation, incident triage, cloud infrastructure deployment, and customer service. However, the tools built to observe software systems weren't designed for non-deterministic, multi-hop agent workflows. Dashboards break down, averages lie, and when an agent causes an incident, teams have no way to reconstruct what it decided or why. Observability for AI systems Honeycomb's new AI-native observability capabilities allow Engineering teams to track and analyze the performance, behavior, and interactions of AI agents. With Agent Timeline, users can render multi-agent, multi-trace workflows as a single coherent view, connecting every LLM call, tool invocation, agent handoff, and downstream system impact in real time. Engineering teams can now trace what an agent did, reconstruct the full decision path, and understand failures without switching tools or piecing together logs by hand. Observability with AI agents New and enhanced features from Honeycomb supercharge human and agent teams by surfacing intelligence at machine speed. These include: * Canvas, newly rebuilt to serve as a collaborative workspace, a chat interface, and autonomous agent all in one, allows engineering teams to query issues in plain English, work with human and agent team members on investigations, and produce sharable visualization snapshots. * Auto-investigations set your Canvas agent to work automatically when an alert fires, an SLO burns, or an anomaly surfaces, gathering data, creating and testing hypotheses, and proposing remediation, all before an engineer even opens their laptop. * Canvas Skills encode your best engineers' debugging knowledge and best practices for frameworks or services like Kafka into reusable playbooks that run autonomously. Built on open standards for maximum flexibility As a prominent supporter and contributor to the OpenTelemetry project, Honeycomb has integrated the OpenTelemetry (OTel) GenAI semantic conventions (v1.40.0) into its platform. By making gen_ai.* attributes first-class citizens, Honeycomb ensures that model evaluations, tool executions, MCP calls, LLMs, and agents are all properly observed. This alignment with OpenTelemetry GenAI standards allows for the automatic generation of structured GenAI insights without requiring re-instrumentation as specifications change, custom setups, or the use of proprietary SDKs. Honeycomb's proprietary unified data store was built for exactly the kind of unpredictable, high-dimensionality telemetry that AI agents generate, from the ground up. Availability Canvas, Canvas Agent, and Skills are available starting next week for all Honeycomb customers. Agent Timeline is available in Early Access, and anticipated to be generally available next month. Christine Yen, cofounder and CEO of Honeycomb AI has upended how software works: introducing real nondeterminism into production, and fundamentally changing how teams of humans and agents build and validate code, Engineers are drowning in uncertainty as most observability tools weren't built for this sort of "unknown unknown." Honeycomb was built for the hardest parts of building software - and autonomous agents have taken those hardest parts mainstream. Shogo Wada, Staff Software Engineer at Bubble Honeycomb is great at dealing with unknown-unknowns, and Canvas takes it even further, During a recent investigation, we found a cause of slowness in our API by asking Canvas to investigate. While the cause was not directly visible on the affected spans, Canvas compared whole traces and found patterns within their child spans. Before Canvas, this would have been a manual process of opening traces one by one. Additionally, as Canvas is a collaborative tool among the team, we could theoretically explore multiple hypotheses in parallel, getting to a root cause faster. Ray Sharma is an Industry Analyst and Editor at The Fast Mode. He has over 15 years of experience in mobile broadband technologies and solutions, conducting research and analysis on various technology segments and producing articles and write-ups on the latest developments within the sector. He is also in charge of social media engagement and industry liaisons. The Fast Mode 9658 likes TWEETS 28.4K FOLLOWING 3479 FOLLOWERS 13.2K