Skip to main content

ThousandEyes Announces Agent-to-Agent Tests

ThousandEyes announced Agent-to-Agent Tests, designed to make it easier for network operations teams to quickly troubleshoot and pinpoint issues caused by performance degradation in the reverse path.

ThousandEyes provides visibility into every network segment, including the Internet, and with this new capability enables organizations to quickly see both forward and reverse paths. This more granular understanding enables networking teams to pinpoint precisely where issues are located and in which direction, leading to faster and more accurate issue resolution and higher service levels — critical as networks become more cloud-centric and less reliable.

"It is often forgotten that most network traffic is transactional and information can come back on a completely different path than the way it was sent when traversing the Internet," said Scott Cressman, Head of Product Management at ThousandEyes. "Traffic asymmetry is nothing new, but it's actually becoming more frequent. The adoption of direct Internet access, SD-WAN and software defined networking are driving more frequent path changes. By providing forward- and reverse-path insights, we're offering more complete path information and richer, intuitive unified visualization of the traffic flows between points. This helps organizations analyze Internet peering and troubleshoot provider issues or unexpected routing configurations."

ThousandEyes Agent-to-Agent Tests enables organizations to gain visibility into their cloud, SaaS and application and services deployments with the addition of bi-directional path visibility. Enterprise and Cloud Agents automatically map both forward and reverse paths between endpoints, providing richer context and practical visualizations. This provides network teams with the knowledge of exactly where a problem exists, enabling them to take immediate action and involve the responsible parties.

Routing between two end-points requires finding the best path across potentially multiple intermediary points, with each network applying its own policies, and this leads to a high likelihood of asymmetric paths. The level of asymmetry can be dependent on a number of factors including:

- Load-balancing algorithms, such as ECMP

- Routers with adaptive routing algorithms, like SD-WAN

- Business relationships and peering policies between networks

Agent-to-Agent Tests also includes network address translation (NAT) traversal, eliminating the need for manual configuration of NAT firewall rules. NAT traversal can automatically detect ThousandEyes agents behind the firewall so that if the internal IP address of the agent changes, the NAT rules don't need be updated.

ThousandEyes Agent-to-Agent Tests is now generally available to all users.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

ThousandEyes Announces Agent-to-Agent Tests

ThousandEyes announced Agent-to-Agent Tests, designed to make it easier for network operations teams to quickly troubleshoot and pinpoint issues caused by performance degradation in the reverse path.

ThousandEyes provides visibility into every network segment, including the Internet, and with this new capability enables organizations to quickly see both forward and reverse paths. This more granular understanding enables networking teams to pinpoint precisely where issues are located and in which direction, leading to faster and more accurate issue resolution and higher service levels — critical as networks become more cloud-centric and less reliable.

"It is often forgotten that most network traffic is transactional and information can come back on a completely different path than the way it was sent when traversing the Internet," said Scott Cressman, Head of Product Management at ThousandEyes. "Traffic asymmetry is nothing new, but it's actually becoming more frequent. The adoption of direct Internet access, SD-WAN and software defined networking are driving more frequent path changes. By providing forward- and reverse-path insights, we're offering more complete path information and richer, intuitive unified visualization of the traffic flows between points. This helps organizations analyze Internet peering and troubleshoot provider issues or unexpected routing configurations."

ThousandEyes Agent-to-Agent Tests enables organizations to gain visibility into their cloud, SaaS and application and services deployments with the addition of bi-directional path visibility. Enterprise and Cloud Agents automatically map both forward and reverse paths between endpoints, providing richer context and practical visualizations. This provides network teams with the knowledge of exactly where a problem exists, enabling them to take immediate action and involve the responsible parties.

Routing between two end-points requires finding the best path across potentially multiple intermediary points, with each network applying its own policies, and this leads to a high likelihood of asymmetric paths. The level of asymmetry can be dependent on a number of factors including:

- Load-balancing algorithms, such as ECMP

- Routers with adaptive routing algorithms, like SD-WAN

- Business relationships and peering policies between networks

Agent-to-Agent Tests also includes network address translation (NAT) traversal, eliminating the need for manual configuration of NAT firewall rules. NAT traversal can automatically detect ThousandEyes agents behind the firewall so that if the internal IP address of the agent changes, the NAT rules don't need be updated.

ThousandEyes Agent-to-Agent Tests is now generally available to all users.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.