Category: Blogs

  • From Automation to Orchestration: The Key to Digital Success

    From Automation to Orchestration: The Key to Digital Success

    Digital transformation continues to be a key focus for many organizations, and this usually means the automation of processes and data.

    At its core, automation is meant to simplify and streamline business operations. However, if not implemented correctly, it can introduce complexity, risk and fragmentation. The proliferation of automation tools, combined with the rapid growth of interconnected systems has created a tangled web of interdependencies that are becoming increasingly difficult to manage.

    In fact, recent research has shown organizations now manage an average of 50 endpoints to execute tasks that are part of a process in their business (this is a 19% increase over the past five years). Without a fundamental shift in approach, organizations risk becoming overwhelmed by digital chaos – what’s more, 82% fear they could find themselves dealing with ‘Automation Armageddon’ if processes are left unchecked.

    Bringing Order to Automation Complexity

    As automation becomes more ingrained, organizations are facing new operational risks that threaten efficiency, compliance, and agility. One of the biggest challenges is the sheer volume of interconnected systems and workflows that businesses now rely on. When it comes to process complexity, more than three quarters of organizations say that a lack of control has resulted in increased risk that core business processes are not working anymore.

    At the same time, rising regulatory demands are making compliance more difficult. A lack of control has also increased business risk relating to compliance and many organizations lack the visibility to ensure compliance across all automated processes.

    As automation scales, it becomes harder to gain insights into what’s working, what’s redundant, and where bottlenecks exist. This lack of visibility makes it difficult for leaders to drive continuous improvement and ensure automation is delivering real value.

    AI: The Operationalizing and Scaling Challenge

    Despite AI being at a relatively early stage in terms of widespread adoption, AI is already revolutionizing industries, enhancing decision-making, improving customer experiences, and driving efficiencies across business processes. Many organizations are eager to expand their AI capabilities, integrating machine learning models, predictive analytics, and AI agents to uncover new opportunities.

    A significant challenge facing organizations is being able to operationalize and scale AI; 85% of organizations report such challenges, highlighting the difficulty of moving from isolated AI implementations to fully embedded AI-driven end-to-end processes. This fragmented approach makes it difficult for businesses to realize AI’s full value, as disconnected AI initiatives fail to work cohesively within larger business processes.

    Another hurdle is governance and compliance. As AI becomes more embedded in decision-making, organizations need clear oversight into how models are trained, deployed, and maintained. Transparency is crucial, particularly in regulated industries where AI-driven decisions must be explainable and auditable.

    To overcome these challenges, organizations must implement “guardrails” that combine deterministic workflows with the non-deterministic nature of AI capabilities. By clearly defining operational parameters, businesses can ensure AI operates autonomously while adhering to organizational policies and regulatory standards. This approach allows businesses to leverage AI’s adaptability and predictive power within structured, reliable processes without sacrificing compliance or control.

    How AI Can Be Integrated

    AI systems need to be seamlessly integrated into existing business processes to ensure they deliver real value. Without orchestration, AI often functions in isolation, requiring manual effort to synchronize with other process endpoints. This disconnect leads to inefficiencies and makes it impossible to “control” AI sufficiently. 

    Embedding AI within a structured orchestration framework provides clear governance and simplifies management – effectively balancing predictable, deterministic workflows with the adaptive, non-deterministic capabilities of AI, which are increasingly executed by one or more AI agents. Structured orchestration defines clear interactions between human and AI-driven tasks and delivers real-time visibility into operations. This reduces complexity, eliminates silos, and unlocks AI’s full potential as a strategic asset. 

    Process orchestration also enables organizations to build processes quicker with the help of AI, as modern orchestration tools can help teams in the process modelling phase. Furthermore, process orchestration allows organizations to gain insights into end-to-end processes in real-time and detect bottlenecks or other problems within an automated process. AI can then help to find solutions to solve the process issues,  enabling businesses to optimize operations, improve decision-making, and scale AI usage effectively. 

    To fully leverage AI, organizations must move beyond ad hoc implementations and toward a strategic, orchestrated approach. This way, they can maximize efficiency, maintain compliance, and ensure that AI contributes meaningfully to broader digital transformation efforts.

    Why Orchestration Helps Ensure AI and Automation Initiatives Play the Right Tune

    To regain control, organizations must embrace end-to-end process orchestration as a core part of their business and IT strategy. Process orchestration and automation allows organizations to tame complexity, operationalize AI, and accelerate transformation. Organizations can design, manage, and improve the processes that underpin their business, no matter what the processes entail or where they run. Additionally, it serves as a critical link between IT and business teams, aligning automation initiatives with real operational needs and long-term business goals.

    Automation Is Inevitable, But Chaos Isn’t

    Automation without orchestration leads to fragility, not resilience. Those organizations that prioritize process orchestration will be those who turn automation and AI into a competitive advantage and avoid the looming threat of digital chaos or ‘Automation Armageddon’.

  • New Relic Adds Support for Model Context Protocol to Observability Platform

    New Relic Adds Support for Model Context Protocol to Observability Platform

    New Relic this week added support for the Model Context Protocol (MCP) to its observability platform to surface insights into artificial intelligence (AI) agents and applications.

    Originally developed by Anthropic, MCP is rapidly becoming a de facto application programming interface (API) for enabling interoperability between AI agents and other sources of data.

    New Relic has now incorporated MCP with its application performance monitoring to surface insights into AI agents and applications alongside its ability to monitor and observe legacy applications.

    Previously, New Relic had added an ability to observe large language models (LLMs), but via MCP it’s now able to extend the reach of that capability to AI agents and applications that access those LLMs. That capability makes it possible to visualize specific usage and patterns of the entire lifecycle of an MCP request, including invoked tools, call sequences and execution durations.

    Additionally, DevOps teams can identify which prompts are being invoked and what choices were made by an AI agent along with surfacing latency, errors and other MCP performance issues.

    New Relic AI Monitoring MCP support is now available in Python Agent version 10.13.0, with additional languages coming soon.

    In general, New Relic reports it has seen a steady 30% growth in usage of its AI monitoring tools quarter-over-quarter for the past year. The company has also seen a 92% increase in the number of unique AI models being used. However, the ChatGPT models provided by OpenAI accounted for 86% of all the LLM tokens being generated.

    Nic Benders, chief technical strategist for New Relic, said that level of activity suggests that OpenAI is well on its way to becoming a de facto standard, even as the number of AI models being made available continues to rapidly increase.

    It’s not immediately clear why one LLM might be preferred over another but OpenAI, in addition to making a steady stream of investments, has successfully generated a significant amount of brand recognition. That helps make OpenAI one of the first platforms that most organizations are going to look to when building an AI application, said Benders.

    However, there are use cases, such as coding, that lend themselves, for the moment at least, better to coding, he added.

    Less clear is the degree to which the various AI models that are available will gain enough traction to warrant the ongoing level of investment required to sustain them. IT teams should carefully consider what foundations they want to build and deploy AI applications on for the long term, noted Benders.

    Additionally, not every AI application requires the latest, and most expensive, LLM, said Benders. The cost of previous generations of AI models has declined considerably as more advanced models have become available. Many applications, however, may not require access to a more advanced model, especially when a previous generation will enable a developer to automate a task at a much lower cost.

    Ultimately, observability platforms will play a critical role in enabling organizations to track which AI models are being used for which use cases. The challenge now is understanding not only which ones are gaining traction among application developers, but just as importantly, the symbiotic relationship that exists between AI models and the IT infrastructure upon which they depend.

  • From Noise to Narrative: Rethinking Observability for AI-Augmented DevOps Pipelines 

    From Noise to Narrative: Rethinking Observability for AI-Augmented DevOps Pipelines 

    Because DevOps teams deal with so much data, they may feel like they are drowning, but rarely find the insights they need. There is more observability data now than ever, yet it often stays separate, disorderly, inaccessible or confusing. As AI enters more organizations’ processes, observability moves from just monitoring to gathering early insight through meaningful narratives. It is not only about having new tools but also changing how development is done and organized. Supporting today’s DevOps — where AI is involved — requires developers to switch from seeing observability as a solely technical aspect to thinking of it as a way to explain what happens in the system: One that links unrelated incidents, highlights how each one is related, and warns users ahead of any failure. 

    The Telemetry Tsunami 

    Legacy monitoring was created for stable, predictable systems, including static servers, stable networks and well-known failure points. Nowadays, architectures made of ephemeral containers, dynamic microservices and AI-driven automation result in high-volume and high-velocity telemetry data that older monitoring systems cannot handle. 

    AI models add more factors to consider in analyzing systems – model drift, low data quality, inference delays and unclear ways AI decides make things difficult to observe. Logs and alerts are insufficient to make sense of what is happening in such systems. Observing these complex, AI-driven systems calls for tracing and feedback that can reveal valuable insights with a lot of noise around them. The result? As a result, most teams experience so much data that it’s hard to find the reason behind any performance, regression or security problems. 

    Observability as Narrative 

    Moving forward, DevOps teams need to treat observability as a way to construct coherent stories. DevOps teams should focus on building stories from the signals they gather that help explain what happened and what to do next. This shift involves three fundamental changes: 

    Correlated Context Over Raw Data: Systems should first add metadata like deployment IDs, feature flags, user behavior and model versions to metrics, traces and logs to describe their life cycle independently (refer to figure 1). 

    Declarative Telemetry Pipelines: Bringing AI/ML into the system will allow systems to describe their activities using words humans can easily understand. A pipeline should ensure that the effects of an AI model update on latency can be noted and followed, not simply added later on. 

    Semantic Layering with AI Assistance: Instead of being used only in the system, AI/ML can simplify understanding what is happening. With the help of AI/ML, observability tools are becoming easier for humans to understand. 

     

    Figure 1. The three pillars of observability that are essential in an AI-augmented DevOps pipeline. 

    From Dashboards to Dialogues 

    Most current observability tools rely solely on the user to interpret data. However, more practitioners are starting to wonder: Why can’t the system simply offer explanations? It all boils down to how observability tools combine AI to help sum up what’s happening, pinpoint regressions and provide possible explanations. With the help of AI, systems can now describe issues, indicate regression points and identify probable problems in the data. An AI-augmented observability system could tell you: “Starting at 12:06, service X saw a 30% increase in latency because many cold starts from model Y, which had just been redeployed recently. Service level objectives (SLOS) did not meet expectations by 12:12. Consider raising the pool size of warm data or re-adjusting the model’s time frame for batch processing”. 

    This is no longer science fiction but something that is becoming true. Several open-source and commercial solutions integrate machine learning into observability stacks to help find root causes, group anomalies and make suitable suggestions for resolving issues. 

    Designing for Explainability 

    AI models used in DevOps also require monitoring for issues, as they can perform worse than expected for reasons that might not be obvious in tests. Unlike standard software, changes in AI models can be challenging to detect until they appear in production. Sometimes, a model that looks good during testing can have problems in the real world, caused by small or unusual changes in the input data. 

    Explainability is key. AI observability must track: 

    • Input/output distributions over time 
    • Feature importance changes 
    • Data pipeline lineage 
    • Confidence scores and decision paths 

    When correlated with infrastructure and application data, this information will tell teams if the incident was caused by a system problem, an issue with the pipeline code, or faulty model behavior. 

    Practitioner-Centric Observability 

    For observability to progress, it must be designed for all system users, not just for central teams. This means that not only platform teams but also the mid-level workers responsible for trying to understand user complaints, triaging incidents, or fine-tuning production models need observability tools. 

    Some key principles include: 

    • Narrative-Driven Interfaces: Less reliance on dashboards; more chances to look at timelines, see how things are linked and get simple explanations from AI. 
    • Integrated Feedback Loops: Ability for engineers to fix, add notes to and correct system alerts so the information shown in the future gets better. 
    • Cross-Domain Visibility: Bringing together data about how applications are doing, how the systems are working and how well your AI is doing all together into one place to help you see your systems better. 

    Observability must evolve, so it is smarter than simply collecting more data. It will require the entire culture of the industry to change, not just new technology. People in the industry should start seeing observability as telling a story, where each log, trace and metric adds to how the system can be easily explained. This evolution means that your systems must be able to define themselves well, rather than simply based on how much data you can collect.