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CloudFabrix Releases Macaw - The Generative AI Assistant

CloudFabrix announced the availability of its "Macaw", Generative AI assistant and Edge enhancements for Telcos, MSP's and Enterprises.

Operational domains are converging around data and operational personas are having a hard time deriving actionable insights from this data deluge. Observability and AIOps require asking ad-hoc and forward-looking questions about your operational data and connecting the business and IT context.

Macaw and RDAF Are a Perfect Match

The "Macaw" Generative AI Assistant leverages natural language Conversational queries and uniquely identifies the prompt context leveraging the Low code Robotic Data Automation Fabric (RDAF) platform. It then uses Azure's OpenAI LLM (large language model) service and CloudFabrix's LLM, for semantic search on local knowledge corpus to glean insights and investigate data, compose and explain pipelines, and compose dashboards and service tickets. "Macaw" ensures privacy and governance over local data and data transfer between LLMs with user-defined policies. "Macaw" breaks down operational silos around data and democratizes Observability and AIOps. Initially, Macaw will be used for upskilling and reskilling of operational personas and eventually for AIOps insights.

RDAF Edge

With 5G and Edge use cases proliferating, this RDAF platform release also includes RDAF Edge, packaged in a single VM to run on edge endpoints. RDAF Edge ingests actionable data with real-time topology enrichment, to a central data platform or data lake. RDA Edge also enables Observability Data Modernization Service where Non-OTel data is transformed into OTel data for ingestion into any OpenTelemetry-based Observability backend cloud.

Composable Dashboards for Telco

Telcos need the ability to ingest multiple data types - syslogs, SNMP traps, gNMI, Bulkstats, OpenTelemetry across domains - Campus, Datacenter, Cloud, Mobility / 5G, Optical, etc. across devices, controllers, Physical(PNF's), virtual(VNF's) and cloud-native (CNF's) network functions. Telco Service Assurance needs a single pane of glass to visualize aggregated data across these disparate sources with persona-based composable dashboards.

Field Customizable and Extensible Bot Based Architecture

RDAF's Bot-based streaming architecture builds on top of microservices and event-driven Function-as-a-service (FaaS) with the added advantage of pre-built code for composability, field customization, and extensibility. This lends itself very well to multiple data-centric use cases.

"This is a very transformative release, converging Generative AI for efficiency and RDAF low code platform for accelerated development," said Shailesh Manjrekar, Vice President of AI and SaaS Marketing. "Generative AI is transforming the industry, but it is important to integrate it into the core product, understand the prompt context, and train on private data, which is uniquely done by CloudFabrix's Macaw. This is going to fuel innovation for operational personas," he added.

The Latest

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...

CloudFabrix Releases Macaw - The Generative AI Assistant

CloudFabrix announced the availability of its "Macaw", Generative AI assistant and Edge enhancements for Telcos, MSP's and Enterprises.

Operational domains are converging around data and operational personas are having a hard time deriving actionable insights from this data deluge. Observability and AIOps require asking ad-hoc and forward-looking questions about your operational data and connecting the business and IT context.

Macaw and RDAF Are a Perfect Match

The "Macaw" Generative AI Assistant leverages natural language Conversational queries and uniquely identifies the prompt context leveraging the Low code Robotic Data Automation Fabric (RDAF) platform. It then uses Azure's OpenAI LLM (large language model) service and CloudFabrix's LLM, for semantic search on local knowledge corpus to glean insights and investigate data, compose and explain pipelines, and compose dashboards and service tickets. "Macaw" ensures privacy and governance over local data and data transfer between LLMs with user-defined policies. "Macaw" breaks down operational silos around data and democratizes Observability and AIOps. Initially, Macaw will be used for upskilling and reskilling of operational personas and eventually for AIOps insights.

RDAF Edge

With 5G and Edge use cases proliferating, this RDAF platform release also includes RDAF Edge, packaged in a single VM to run on edge endpoints. RDAF Edge ingests actionable data with real-time topology enrichment, to a central data platform or data lake. RDA Edge also enables Observability Data Modernization Service where Non-OTel data is transformed into OTel data for ingestion into any OpenTelemetry-based Observability backend cloud.

Composable Dashboards for Telco

Telcos need the ability to ingest multiple data types - syslogs, SNMP traps, gNMI, Bulkstats, OpenTelemetry across domains - Campus, Datacenter, Cloud, Mobility / 5G, Optical, etc. across devices, controllers, Physical(PNF's), virtual(VNF's) and cloud-native (CNF's) network functions. Telco Service Assurance needs a single pane of glass to visualize aggregated data across these disparate sources with persona-based composable dashboards.

Field Customizable and Extensible Bot Based Architecture

RDAF's Bot-based streaming architecture builds on top of microservices and event-driven Function-as-a-service (FaaS) with the added advantage of pre-built code for composability, field customization, and extensibility. This lends itself very well to multiple data-centric use cases.

"This is a very transformative release, converging Generative AI for efficiency and RDAF low code platform for accelerated development," said Shailesh Manjrekar, Vice President of AI and SaaS Marketing. "Generative AI is transforming the industry, but it is important to integrate it into the core product, understand the prompt context, and train on private data, which is uniquely done by CloudFabrix's Macaw. This is going to fuel innovation for operational personas," he added.

The Latest

While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...

Top-performing businesses prioritize data-driven decision making, enabling leaders to move from intuition and gut feel towards evidence-based judgment. But that judgment is only sound when the data underpinning decisions is accurate. With incident management, data accuracy is particularly important. Long-term revenue, customer trust, and operational stability depend on high-quality data that enables teams to quickly identify and address the root cause of major incidents. Against this backdrop, governance becomes a critical endeavor to ensure the right data drives the right action ...

In MEAN TIME TO INSIGHT Episode 26, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network compliance ... 

Most production autonomous agents do not run in a vacuum. They run inside cloud infrastructure: virtual machines, containers, pods, managed clusters or private servers. That is where most operations teams start monitoring. Is the VM alive? Is the container running? Did the pod restart? Is memory stable? Is CPU too high? Did the health check pass? Those signals are useful. They tell you whether the shell around the agent is alive. They do not tell you whether the agent inside is actually operational ...