Coforge Limited announced key advances of EvolveOps.AI, a next-generation agentic AI-powered IT operations management platform.
It is designed to empower enterprises to prepare for an AI-first era, leveraging purpose-built hybrid cloud architecture and agentic operations for agility, resilience and transformation. Powered by advanced agentic AI agents, EvolveOps.AI delivers end-to-end autonomous operations across the lifecycle of enterprise systems running on hybrid cloud.
EvolveOps.AI amplifies and augments enterprise investments in observability, data fabric and automation platforms to accelerate the journey towards agentic AI-powered operations. Built entirely on open-source technologies, EvolveOps.AI can be deployed quickly, leveraging a large repository of pre-built adaptors and plug-ins, a fine-tuned and purpose-built Small Language Model (SLM) and agentic AI resolver personas to autonomously administer enterprise IT operations management.
By applying AI and machine learning across a unified data fabric for technology operations, EvolveOps.AI enables enterprises to cut down the noise, accelerate incident lifecycle management, and dramatically improve reliability of mission-critical systems. With EvolveOps.AI, enterprises have reduced systems downtime by 25%, cut IT operational expenses by 40%, reduced Mean Time to Detection and Mean Time to Resolution by 60%, and achieved 40% faster time to market for products.
EvolveOps.AI directly addresses some of the most pressing challenges in modern IT operations. “Our strategy for cloud and infrastructure is anchored in Mission Zero – Zero Disruption, Zero Touch, Zero Friction – for every cloud transformation we execute,” said Ashish Kumar, Global Business Unit Head – Cloud at Coforge. “With EvolveOps.AI, we are operationalizing that mission by infusing AI agents into every stage of the technology operations lifecycle. The platform helps our clients shift from reactive firefighting to proactive, autonomous operations—delivering higher resilience, improved developer productivity, and a superior experience for business users and customers.”
At its core, EvolveOps.AI combines the power of fine-tuned SLMs with the proven value of deterministic models, delivering superior performance across IT operations. This approach enables Coforge to achieve results dramatically improving time to value while significantly reducing cost to serve.
To date, Coforge has developed 28 agentic personas that perform various tasks across the IT Operations lifecycle, including SRE agents, Infrastructure and Cloud Engineering Agents, Network Engineering Agents, Kubernetes Engineering Agents, Command Center Engineering Agents, Service Management Agents and FinOps Agents. Each has the ability to analyze, reason, decide and act across various complex IT scenarios. The platform incorporates robust guardrails, enabling enterprises to toggle from human-in-the-loop (HITL) mode to truly autonomous operations to deliver necessary enterprise-grade precision.
EvolveOps.AI has been architected for seamless integration with the industry’s leading hyperscalers and enterprise IT Operations Management platforms. Its Hybrid Cloud Manager module supports full-stack builds and policy-driven automation across Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), Oracle Cloud Infrastructure (OCI), and private cloud environments, while also interfacing with a broad ecosystem of observability, ITSM, security, and automation tools. This ensures that enterprises can adopt autonomous operations without disrupting their existing technology landscape, and standardize governance, FinOps, and reliability practices across multi-cloud estates.‑stack builds and policy‑driven automation across Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), Oracle Cloud Infrastructure (OCI), and private cloud environments, while also interfacing with a broad ecosystem of observability, ITSM, security, and automation tools. This ensures that enterprises can adopt autonomous operations without disrupting their existing technology landscape and can standardize governance, FinOps, and reliability practices across multi‑cloud estates.
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 ...