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Apica Ascent 2.16 Released

Apica announced the release of Ascent 2.16, delivering the clean, governed, real-time telemetry that autonomous AI systems require at up to 40% lower total cost of ownership than legacy observability platforms.

Ascent 2.16 represents foundational advances to Apica Ascent that expand the intelligent telemetry pipeline, extend real-user and service-level visibility, and harden platform performance for AI-scale workloads.

Ascent is a complete telemetry data management product suite that gives enterprises the pipeline control, storage foundation, and operational visibility their agents require, with up to 40% lower total cost of ownership than the legacy platforms they’re already paying too much for.

Apica’s architecture inverts the model: Process, transform, enrich, and govern telemetry in the pipeline before expensive platform ingestion. Route intelligently. Store cost-efficiently. Enable real-time access for both human operators and AI agents.

“The enterprises winning with AI won’t be the ones with the most agents, they’ll be the ones whose telemetry infrastructure can support them. Ascent 2.16 is the foundation: A product suite that treats every data type, including synthetics, as a first-class pipeline citizen, that puts real-time intelligence and cost visibility directly in the hands of SRE and platform teams, and that’s architecturally ready for the volumes agentic AI will generate. This is how you get agentic-ready before the wave hits,” said Andi Mann, Chief Product Technology Officer, Apica.

Ascent 2.16 is a foundational release that advances the intelligent telemetry pipeline and prepares enterprise infrastructure for the demands of agentic AI at production scale.

2.16 introduces the ability to expose synthetic check results as a live data stream directly within Apica Flow, making synthetics a first-class telemetry type alongside logs, metrics, and traces.

  • Apply full Flow pipeline capabilities to synthetic check data for the first time: Filtering, enrichment, PII/PHI masking, volume governance, and cost routing.
  • Enable AI validation workflows: Synthetic probes generate known-result signals that AI agents can use to detect hallucination and verify autonomous decision outputs.
  • Advances Apica’s “all data is good data” architecture: Collapsing the boundary between monitoring and telemetry pipeline management

Apica Flow now surfaces real-time cost savings calculations at the individual rule level, giving SRE and platform teams immediate attribution of downstream ingestion and storage savings.

  • Transforms pipeline configuration from an engineering task into a visible business lever, with cost impact surfaced at the moment a rule is written.
  • Directly supports observability budget control at AI scale, where cost optimization decisions must be made in real time, not inferred from billing reports after the fact

A new RUM dashboard extends Ascent’s observability to the endpoint, capturing live user experience data from real devices and sessions, with built-in AI-driven analysis that surfaces anomalies and performance insights automatically.

  • Extends visibility beyond the server to the end user, capturing the endpoint-level signals that agentic systems serving real users depend on to operate reliably.
  • AI integration surfaces patterns and anomalies in real user metrics automatically.
  • Lays the groundwork for edge observability, an emerging requirement as agentic workloads extend further toward the user

A new SLO dashboard gives enterprise IT and SRE teams native tooling within Ascent to define, monitor, and report against the service commitments that their business depends on.

  • Enables teams to define and track reliability contracts against the precise service level agreements their business has committed to, within the same platform managing their telemetry pipeline. 
  • Prepares enterprise teams to establish and monitor the reliability standards that AI-integrated and agentic workloads will need to meet before those workloads reach production

Broad architectural improvements to the Ascent substrate deliver measurably faster response times, higher throughput, and improved stability across the product suite.

Architectural enhancements to the Ascent substrate directly set up the high-throughput, high-concurrency processing that agentic AI workloads will demand at production scale

Improvements are most visible at the substrate level, where AI agents will generate 10–100x the telemetry of traditional applications, making product throughput capacity a foundational agentic-readiness requirement

Apica Ascent 2.16 is generally available now for all Ascent customers. All capabilities described in this release, including synthetic data streaming in Flow, real-time ROI on pipeline rules, the RUM dashboard with AI analysis, and the SLO dashboard, are included within existing Ascent subscription tiers.

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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Apica Ascent 2.16 Released

Apica announced the release of Ascent 2.16, delivering the clean, governed, real-time telemetry that autonomous AI systems require at up to 40% lower total cost of ownership than legacy observability platforms.

Ascent 2.16 represents foundational advances to Apica Ascent that expand the intelligent telemetry pipeline, extend real-user and service-level visibility, and harden platform performance for AI-scale workloads.

Ascent is a complete telemetry data management product suite that gives enterprises the pipeline control, storage foundation, and operational visibility their agents require, with up to 40% lower total cost of ownership than the legacy platforms they’re already paying too much for.

Apica’s architecture inverts the model: Process, transform, enrich, and govern telemetry in the pipeline before expensive platform ingestion. Route intelligently. Store cost-efficiently. Enable real-time access for both human operators and AI agents.

“The enterprises winning with AI won’t be the ones with the most agents, they’ll be the ones whose telemetry infrastructure can support them. Ascent 2.16 is the foundation: A product suite that treats every data type, including synthetics, as a first-class pipeline citizen, that puts real-time intelligence and cost visibility directly in the hands of SRE and platform teams, and that’s architecturally ready for the volumes agentic AI will generate. This is how you get agentic-ready before the wave hits,” said Andi Mann, Chief Product Technology Officer, Apica.

Ascent 2.16 is a foundational release that advances the intelligent telemetry pipeline and prepares enterprise infrastructure for the demands of agentic AI at production scale.

2.16 introduces the ability to expose synthetic check results as a live data stream directly within Apica Flow, making synthetics a first-class telemetry type alongside logs, metrics, and traces.

  • Apply full Flow pipeline capabilities to synthetic check data for the first time: Filtering, enrichment, PII/PHI masking, volume governance, and cost routing.
  • Enable AI validation workflows: Synthetic probes generate known-result signals that AI agents can use to detect hallucination and verify autonomous decision outputs.
  • Advances Apica’s “all data is good data” architecture: Collapsing the boundary between monitoring and telemetry pipeline management

Apica Flow now surfaces real-time cost savings calculations at the individual rule level, giving SRE and platform teams immediate attribution of downstream ingestion and storage savings.

  • Transforms pipeline configuration from an engineering task into a visible business lever, with cost impact surfaced at the moment a rule is written.
  • Directly supports observability budget control at AI scale, where cost optimization decisions must be made in real time, not inferred from billing reports after the fact

A new RUM dashboard extends Ascent’s observability to the endpoint, capturing live user experience data from real devices and sessions, with built-in AI-driven analysis that surfaces anomalies and performance insights automatically.

  • Extends visibility beyond the server to the end user, capturing the endpoint-level signals that agentic systems serving real users depend on to operate reliably.
  • AI integration surfaces patterns and anomalies in real user metrics automatically.
  • Lays the groundwork for edge observability, an emerging requirement as agentic workloads extend further toward the user

A new SLO dashboard gives enterprise IT and SRE teams native tooling within Ascent to define, monitor, and report against the service commitments that their business depends on.

  • Enables teams to define and track reliability contracts against the precise service level agreements their business has committed to, within the same platform managing their telemetry pipeline. 
  • Prepares enterprise teams to establish and monitor the reliability standards that AI-integrated and agentic workloads will need to meet before those workloads reach production

Broad architectural improvements to the Ascent substrate deliver measurably faster response times, higher throughput, and improved stability across the product suite.

Architectural enhancements to the Ascent substrate directly set up the high-throughput, high-concurrency processing that agentic AI workloads will demand at production scale

Improvements are most visible at the substrate level, where AI agents will generate 10–100x the telemetry of traditional applications, making product throughput capacity a foundational agentic-readiness requirement

Apica Ascent 2.16 is generally available now for all Ascent customers. All capabilities described in this release, including synthetic data streaming in Flow, real-time ROI on pipeline rules, the RUM dashboard with AI analysis, and the SLO dashboard, are included within existing Ascent subscription tiers.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...