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Keysight Launches TimeKeeper

Keysight Technologies has partnered with FSMLabs to offer TimeKeeper, an enterprise-class clock synchronization, monitoring and time distribution platform that manages and monitors time-critical applications, devices and infrastructure.

"This partnership with Keysight Technologies further accelerates the rollout of TimeKeeper, FSMLabs' enterprise-class clock synchronization and time distribution platform," said Cort Dougan, CEO of FSMLabs. "Our expertise in time synchronization technologies, coupled with Keysight's superior sales and support worldwide, extends the reach of this unique technology, offering Keysight's customers access to advanced time synchronization capabilities for time-critical applications."

Available as software as a service (SaaS), TimeKeeper extends Keysight's portfolio to include time accuracy testing and latency analytics for the following solutions:

- Network Emulator – enables users to accurately test application performance due to network delays by emulating realistic network conditions in the lab at speeds from 1 Gigabit per second (Gbps) to 100Gbps.

- TradeVision – network and market data analytics solution that allows users to monitor worldwide trading infrastructure latency in real-time and improve performance of trading applications.

- Metronome Timing System – scalable time distribution platform that offers time-synchronized testing of complex, distributed networks and time-sensitive applications.

- IxNetwork – network infrastructure performance test solution that enables users to test the scalability, accuracy, failure recovery and correction algorithms for IEEE 1588v2 PTP transparent and boundary clocks.

"The addition of TimeKeeper strengthens Keysight's market leadership in time synchronization and latency simulation in key markets such as financial services," said Areg Alimian, Senior Director of Product Management at Keysight Technologies. "TimeKeeper enables Keysight to offer customers a single time measurement tool to reduce regulatory risk where reporting, regulatory compliance and management of their timing infrastructure is critical to their success."

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Keysight Launches TimeKeeper

Keysight Technologies has partnered with FSMLabs to offer TimeKeeper, an enterprise-class clock synchronization, monitoring and time distribution platform that manages and monitors time-critical applications, devices and infrastructure.

"This partnership with Keysight Technologies further accelerates the rollout of TimeKeeper, FSMLabs' enterprise-class clock synchronization and time distribution platform," said Cort Dougan, CEO of FSMLabs. "Our expertise in time synchronization technologies, coupled with Keysight's superior sales and support worldwide, extends the reach of this unique technology, offering Keysight's customers access to advanced time synchronization capabilities for time-critical applications."

Available as software as a service (SaaS), TimeKeeper extends Keysight's portfolio to include time accuracy testing and latency analytics for the following solutions:

- Network Emulator – enables users to accurately test application performance due to network delays by emulating realistic network conditions in the lab at speeds from 1 Gigabit per second (Gbps) to 100Gbps.

- TradeVision – network and market data analytics solution that allows users to monitor worldwide trading infrastructure latency in real-time and improve performance of trading applications.

- Metronome Timing System – scalable time distribution platform that offers time-synchronized testing of complex, distributed networks and time-sensitive applications.

- IxNetwork – network infrastructure performance test solution that enables users to test the scalability, accuracy, failure recovery and correction algorithms for IEEE 1588v2 PTP transparent and boundary clocks.

"The addition of TimeKeeper strengthens Keysight's market leadership in time synchronization and latency simulation in key markets such as financial services," said Areg Alimian, Senior Director of Product Management at Keysight Technologies. "TimeKeeper enables Keysight to offer customers a single time measurement tool to reduce regulatory risk where reporting, regulatory compliance and management of their timing infrastructure is critical to their success."

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Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...