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Tag: ai

Copado Adds AI Agents to Automate DevOps Workflows for Salesforce Apps

Copado today revealed it has developed a series of generative artificial intelligence (AI) agents capable of automating specific tasks that DevOps teams routinely encounter when building and deploying applications on top of the software-as-a-service (SaaS) application platform from Salesforce.

Announced in advance of the Dreamforce 2024 conference hosted this week by Salesforce, the agents have been trained using data that Copado has been collecting for more than a decade.

David Brooks, vice president of products for Copado, said the first agents to be made available will focus on generating code and automating testing, followed shortly by agents that accelerate the creation of user stories, deploy scripts and surface suggestions to optimize application environments. Those agents can automate tasks versus providing a co-pilot capability that leverages generative AI to create code, noted Brooks.

DevOps teams will be able to orchestrate these agents to automate workflows in a way that should also serve to make best DevOps practices more accessible to a wider range of application development teams, said Brooks.

As DevOps continues to evolve in the age of AI more tasks will be automated using agentic AI, a method for using generative AI to create an agent that has been specifically trained to perform a task using a narrow set of data. That approach ensures that the accuracy of the agents assigned those tasks is much higher than can be achieved using a large language model (LLM) that has been trained using a corpus of data pulled randomly from across the Web.

It’s not clear at what pace agentic AI will transform DevOps workflows, but as software engineering continues to evolve teams will be made up of a mix of humans and agents that are assigned specific tasks. DevOps engineers will, of course, be needed to ensure those tasks are accurately completed but many of the rote tasks that often burn out DevOps engineers will soon be eliminated, noted Brooks.

As that level of toil is reduced it should also become possible to significantly increase the pace at which not only is generative AI used to write code, but also deploy applications, he added.

Each DevOps team will naturally need to decide to what degree they are comfortable assigning tasks to AI agents. However, as DevOps enters the AI era many of the issues that often conspired to limit the ability to build and deploy applications at scale are being eliminated. As the pace of application development starts to accelerate, each organization will need to determine how many applications that previously might not have been deployed because of a lack of resources can now be built. There may even come a day when the current level of application development backlog becomes much more feasible to manage.

In the meantime, DevOps teams would be well-advised to start identifying which routine tasks performed today should be assigned to an AI agent to provide the human members of a DevOps team with the time needed to manage workflows at a level of scale, that not too long ago might have seemed unimaginable.

September 16, 2024
Survey Sees AI and Automation Accelerating Pace of Software Development

A survey of 555 software executives published this week finds that 75% have seen up to a 50% reduction in development time by implementing various artificial intelligence (AI) and automation technologies.

Conducted by the CIO Dive arm of studioID on behalf of Outsystems, a provider of a low-code platform, and KPMG, the survey finds more than half (56%) said they experienced or expected to experience a higher quality of applications, with fewer bugs and improved performance. One-third of respondents (33%) said they had a backlog of between 150 and 800 use cases for generative AI specifically.

Those use cases for generative AI include DevOps optimization (59%), code generation (58%), documentation (56%) and user-interface design (50%).

Rodrigo Coutinho, AI Product Manager at OutSystems, said most organizations are using generative AI today for writing code and testing, and that it may be a while yet before it is applied across the entire software development lifecycle (SDLC). In fact, 38% of executives cited difficulties integrating generative AI into existing workflows as their primary barrier to adoption. A total of 39% also noted there is still a lack of AI expertise within their software development teams.

In addition, other challenges such as data privacy and security concerns (56%) and regulatory and compliance challenges (42%) remain.

Nevertheless, nearly all respondents (93%) are planning to increase their investment in AI-augmented tools over the next two years, with 71% planning to incorporate AI into application development and SDLC management workflows.

Most of the benefits of AI have been seen by professional developers but in time AI should expand the ranks of citizen developers, said Coutinho. Nearly half of the survey respondents (47%) said they expect a new type of application developer to emerge with specialized AI skills, such as prompt engineering, while 43% said they expect the responsibility of developers to expand.

In general, organizations should embrace generative AI with care. The recommendations surfaced are probabilistic, so DevOps teams need to understand that some of the answers being surfaced are very creative, noted Coutinho. Over time, however, as more domain-specific large language models (LLMs) are trained the overall quality of the suggestions being made will become more accurate, he added.

The one certain thing is the pace at which software is being built and deployed is only going to increase in the age of generative AI. The quality of the code used to create that software in the short term may not substantially improve over what humans write today, but in time the quality of applications should improve as, for example, fewer vulnerabilities will be created.

In the meantime, organizations rather than simply diving into AI should put plans in place for operationalizing it thoughtfully, said Coutinho. The winning combination going forward will always include some mix of humans and machines that ensure high-quality applications are deployed, he added.

The challenge, of course, is that enthusiasm for AI far exceeds our collective ability to govern existing SDLC workflows. As such, AI despite best intentions might otherwise soon prove to be too much of a good thing.

September 12, 2024
Navigating Agile Waters: Why Integrating Copilot Demands Methodological Adjustments

The U.S. Congress recently banned its staff from using Microsoft’s AI Copilot — an integrated large language model chatbot that enables automation for Microsoft’s products such as Word, Excel, PowerPoint, Outlook and Teams — citing security concerns. And they are not alone in this sentiment, as many professionals teeter between excitement and dread when it comes to AI. In the meantime, Microsoft has confidently touted Copilot, promising that the power of its AI will reduce the daily grind of running a business. The company has segued from merely talking about AI to infusing it across every layer of its tech stack. Its recent introduction of Copilot Runtime even allows developers to use AI in their own programs, so it’s no surprise that most chief information officers are experimenting with Copilot. Its productivity promises and the resulting scope expansion and cost efficiencies for potential reinvestment are too enticing to ignore. Microsoft is certainly leading by example, and the only question is how others should follow. Over the last decade, agile organizations have outperformed others in making and managing decisions more quickly. Agile adoption in IT has introduced changes in the way infrastructure, applications, data and skills are produced, consumed, or both. The foundational elements of agile, such as collaboration, automation and continuous improvements, are the key sources of innovation pertinent to application, development and deployment.

Recent events have disrupted agile progression in IT. The COVID-led era of flexibility in how, when, and where we work is challenging the status quo in the ways we collaborate. In addition, Generative AI and the associated complexities to IT governance have also shaken up agile progression. Consequently, an incremental economy has been created that forces every company to take the opportunities and the challenges more seriously.

Distributed Agile has already been in practice for quite some time. The remote and hybrid work realities are just extensions of what we have already seen in distributed teams. However, the productivity promises of tools such as Copilot are new, so assuming that current Agile practices will work with GenAI practices is a mistake. So what can we do?

Here are some thoughts on how to integrate agile methodology amidst the Copilot adoption.

Extend DevOps to Include Representation From DataOps and MLOps

Given the importance of data as well as AI and machine learning models, DevOps teams must include representation from the DataOps and MLOps teams (ModelOps is a subset of MLOps). Only then can the objective of bringing the “production” and “operations” closer be achieved. At first glance, replacement may seem to be AI’s biggest threat, but its first act will rather likely be to reveal and deepen cracks in collaboration.

Software Intelligence is More Important Than Ever

Not understanding the application systems holistically before auto-producing the code in production will be disastrous. Corporate IT is a mixture of AI and non-AI applications and IT assets. Additionally, the “explainability” of the code can be achieved only when the software intelligence on the code that GenAI produced is attained. Ultimately, it is not the functional correctness, but the architectural fitment that matters most for unlocking productivity improvements. AI moves fast, but letting speed take too high a precedence sets the stage for failure.

Continuous Compliance and Continuous Security are Equally Important

One of the main concerns of GenAI tools is the vulnerabilities that auto-generated code can introduce to corporate IT, which is the main reason the U.S. Congress has banned the use of Copilot. It is important to make proper adjustments at the pod level for compliance and security, so that they are designed and delivered continuously, rather than audited and assured periodically. Regulation is famously behind innovation, and companies must intentionally think ahead of the curve to avoid future pain.

Augmenting the Quality Gates in Your CI/CD Pipe for AI Assistants

The founding principles of open source — transparency, inspection, and adaptation — can be extended to GenAI products l. “Inspection” should not only cover the quality, performance, security and UX aspects of the code that the tools provide, but also the architectural fitment in the enterprise IT.

Measure Success, and Be Transparent With Shortcomings

AI’s impact can be foggy, but some results must be measurable to justify its adoption. Crafting AI-specific KPIs can help solidify the Copilot’s role on the team. Finding the right metrics to measure is of the utmost importance, and is a large challenge in itself.

In the detailed review of AI’s impact, it’s also important to openly accept shortcomings. The system is, of course, imperfect by nature, and these imperfections need to be tracked and addressed. Because AI is evolving so rapidly, many issues will likely be solved in the short term. Note the flaws, and be intentional with revisiting them at regular intervals.

A Mismatch of Skills Will Impact Productivity Promises

Tools are only as good as those who wield them. An experienced developer can demonstrate an above-average productivity level with an AI assistant, but an inexperienced developer can quickly create more problems than solutions. Training the development and quality assurance communities to master the tools and guidelines for the code and test governance requires adjusting the Agile operating model.

Remember that DevOps teams are more than developers, DataOps teams are more than data engineers, and ModelOps teams are more than data scientists. The cross-disciplinary skills of the DevOps teams will significantly change when AI is part of the conversation. As the boundaries between these disciplines melt away, those willing to adapt will rise to the top.

Adjusting the methodology for the perceived challenges should not curtail the potential benefits that GenAI can produce. If used properly, GenAI can help in hyper-automation of development and quality assurance tasks, evaluating design options through rapid prototyping, simplifying the documentation process, monitoring the production environment to predict performance bottlenecks, and so forth.

If we don’t adjust our agile methods to match these new realities and unlock value at a faster pace, “time to market” and the associated cost benefits will be perceived poorly. Changes to agile methodology are inevitable because GenAI and Agile are providing real competitive advantages. Don’t take it easy.

September 11, 2024
JFrog CEO: Developers Need to Adapt to AI to Keep Their Jobs

JFrog CEO Shlomi Ben Haim told attendees of the company’s swampUP 2024 conference that unless application developers adapt their jobs are indeed at risk because of the rise of generative artificial intelligence (AI).

Multiple surveys of CIOs and business leaders clearly show that generative AI is the top investment priority for most organizations, says Ben Haim. The amount of code being generated with the aid of generative AI is only going to increase as business and IT leaders look to spur innovation, he added.

Additionally, DevSecOps teams are now being asked to support data scientists who are building and deploying AI models in production environments alongside other classes of artifacts. Most of those models are being deployed using practices that are the same as the DevOps and DevSecOps workflows already in use, noted Ben Haim.

As such, the integration of DevOps and machine learning operations (MLOps) practices is inevitable, he added.

JFrog has been investing in MLOps for more than a year now and most recently acquired Qwak AI in addition to integrating its DevSecOps platform with the NIM microservices provided by NVIDIA. Data scientists are simply another persona that needs to be incorporated into the DevSecOps team, noted Ben Haim.

At the same time, DevSecOps should be moving to consolidate their tools and platforms to both reduce costs and optimize workflows, he added. There is simply no way a DevOps team is going to master, for example, 20 different tools and platforms, noted Ben Haim. An integrated DevSecOps platform that includes, for example, extensions to source code managed in GitHub repositories that make DevSecOps teams much more productive by providing them with a single source of truth, said Ben Haim.

It’s not clear at what pace organizations are melding DevOps and MLOps workflows but as AI models are increasingly infused into applications, they will soon become just another type of software artifact that needs to be deployed. While many of the first wave of AI models might have initially been deployed by data scientists working with data engineers, there simply isn’t enough of that expertise available to deploy AI models in production environments at scale.

Regardless of how AI models are deployed, the one certain thing is there will soon be a lot more of them. Organizations have spent the better part of 2024 determining how best to operationalize generative AI with an eye towards deploying them in production environments in the months ahead. In addition to using retrieval augmented generation (RAG) techniques to further customize AI models, many of those AI models over time will need to be replaced by AI models that have been trained using more current data.

The challenge, as always, will be defining the workflows needed to automate as much of the process as possible in an era where it’s expected the amount of software being deployed in the next few years will far exceed all the software that has been deployed in the past decade.

September 10, 2024
Sustainable IT and Data Innovation: Driving the Future of DevOps

The worlds of sustainable IT and data innovation are converging, and two upcoming events are set to showcase the latest developments in these critical areas of sustainable IT and data innovation. The SustainableIT Impact Awards & Symposium and the DevOps + Data Impact 2024 promise to bring together industry leaders, experts, and practitioners to explore how technology can drive positive change in both business and society.

Inaugural Impact Summit & Awards, SustainableIT.org

Sustainability Takes Center Stage

Sustainable IT has become a key focus for organizations across the globe, as the environmental impact of technology comes under increasing scrutiny. The SustainableIT Impact Awards & Symposium will be held in Austin, Texas. It aims to celebrate the institutions and individuals who are leading the way in this field. The event, organized by the nonprofit SustainableIT.org, will feature a range of awards, including the Environmental Award, which recognizes success in reducing energy consumption and emissions across technology infrastructure, and the Governance Award, which celebrates institutions that have woven sustainability into their decision-making processes.

Meanwhile, the DevOps + Data Impact 2024, presented by Perforce and Delphix, also in Austin, will dive deep into the world of data management and its role in driving innovation. As enterprises grapple with the challenges of managing and leveraging vast amounts of data, the conference promises to offer valuable insights and solutions. Attendees can expect to learn about the latest trends in data analytics, security, and compliance. Plus they will gain insights on strategies for optimizing data management to drive business success.

Perforce’s Acquisition of Delphix: Driving Sustainable Data Management

Perforce’s recent acquisition of Delphix, a leading provider of DataOps solutions, reflects the growing importance of data management in the DevOps landscape. This strategic move aims to help customers automate and accelerate their DevOps pipelines while promoting sustainable IT practices. As Perforce CEO Jim Cassens noted, “Data is at the heart of how enterprises operate today and essential for successful software development, but accessing and managing that data is extremely challenging. Many teams do not have rapid access to solid, high-quality test data.”

By integrating Delphix’s capabilities, Perforce seeks to address these challenges and enable more efficient and sustainable data management practices. As Cassens stated in a blog post announcing the acquisition, “Another unique ability of the Delphix platform is how it virtualizes data and ultimately reduces storage footprints, which is good news for sustainability and operational costs.”

Bridging Sustainable IT and Data Innovation

The convergence of these two events points to a broader trend in the DevOps world: the need to balance sustainable IT and data innovation. Organizations are increasingly recognizing that sustainability efforts can drive efficiency and innovation. Accordingly, robust data management can support environmental goals.

As the DevOps landscape continues to evolve, look for more initiatives that emphasize balance with AI-driven energy management in data centers, sustainable IT and software development practices, and circular economy approaches to IT hardware. Certainly, the upcoming events in Austin serve as a barometer for these trends, offering a glimpse into how industry leaders are navigating the complex intersection of technology, data, and sustainability. By attending these events and engaging with the insights they provide, DevOps teams can position themselves at the forefront of sustainable innovation, driving both business success and positive environmental impact.

September 2, 2024
GitLab Makes AI Add-on for Enterprise Edition of CI/CD Platform Generally Available

GitLab this week made generally available a set of generative artificial intelligence (AI) capabilities that DevOps teams can add on to the continuous integration/continuous delivery (CI/CD) platform it provides.

Priced at $39 per user per month, the add-on for GitLab Duo Enterprise provides all the AI capabilities of GitLab Duo Pro along with tools to improve software development workflow efficiencies, proactively detect and fix security vulnerabilities, and securely enhance team collaboration.

Capabilities only available in GitLab Duo Enterprise include vulnerability explanation and automated vulnerability resolution tools, root cause log analysis to resolve CI/CD bottlenecks and failures, summarization and templating tools for discussions, merge requests and code reviews, and a dashboard for tracking the impact AI is having on DevOps workflows.

David DeSanto, chief product officer at GitLab, said GitLab Duo Enterprise add-on extends the generative AI capabilities being provided by GitLab across the software development lifecycle (SDLC), including later this year additional capabilities for automating DevSecOps workflows.

The overall goal is to reduce the level of stress and toil that over time burns DevOps teams out, he added.

It’s not clear at what rate DevOps teams are embracing AI across their DevOps workflows, but a recent GitLab survey found improved productivity (51%), faster deployments (44%) and increased accuracy and security (40%) as key organizational benefits of AI.
More than three-quarters (78%) of all respondents said they are either already using, or plan in the next two years to employ, artificial intelligence (AI) to help develop software. However, more than half (55%) also conceded introducing AI into the software development lifecycle is risky, with data privacy and security being the number one concern.

In general, continued AI advances should help make DevOps more accessible to a broader range of organizations. Many midsize companies, for example, have not been able to find and retain the software engineering expertise required to implement DevOps workflows. AI tools should reduce the level of skills required to implement best DevOps workflows.

At the same time, organizations that have already adopted DevOps workflows should find it easier to manage them at scale as part of a larger platform engineering transition that is occurring, noted DeSanto. Senior engineers with the aid of AI should be able to automate more workflows without, for example, having to manually write additional scripts.

Regardless of motivation for adopting AI, the one thing that is certain is the pace at which applications are being built and deployed is only going to accelerate. Many DevOps teams may, as a result, revisit the way pipelines are constructed using legacy CI/CD platforms that were not designed for the AI era.

In the meantime, DevOps teams would be well advised to start listing the tasks that AI tools should be able to soon automate with an eye toward determining where human software engineers can now add the most value. After all, AI isn’t going to replace the need for software engineers, but it will undoubtedly change their roles within organizations as more code is automatically generated.

August 23, 2024
Five Ways AMD’s ZT Systems Acquisition May Enhance AI Sustainability

AMD’s recent acquisition of ZT Systems for $1.9 billion opens new doors for enhancing sustainability in the data center and AI markets. Here’s how this strategic move could advance AMD’s environmental goals, along with some potential challenges:

1. Improved Energy Efficiency

ZT Systems specializes in energy-efficient hyperscale server solutions. By combining their expertise with AMD’s high-performance processors, data center energy consumption could potentially decrease. Fundamentally, ZT Systems makes servers that use less energy in big data centers. AMD makes computer chips that are powerful but rely on less energy. When you put AMD’s chips in ZT Systems’ servers, data centers can do more work with fewer servers. This aligns with AMD’s goal of achieving a 30x increase in energy efficiency of AI and high-performance computing processors by 2025.

2. Greener Supply Chains

AMD has been actively working with suppliers to reduce environmental impact. Integrating ZT Systems’ practices may further strengthen these initiatives, potentially leading to more sustainable manufacturing processes across a broader network. The challenge lies in harmonizing supply chain practices across different regions, which may have varying environmental regulations and standards. For instance, one region may have strict carbon emission limits, while another is more lenient, requiring companies to manage different regulations. This complexity makes it harder to establish a consistent, eco-friendly supply chain strategy.

3. Smaller Data Center Footprints

ZT Systems’ rack-scale solutions could help AMD optimize AI infrastructure deployment. This may result in fewer physical servers needed, reducing both energy use and the measurable size of data centers. With less space required to house the servers, data centers can be smaller overall. Real estate is expensive so reducing costs associated with building and maintaining larger facilities is a positive. However, headwinds may arise with the transition to more space-saving solutions. It might require a larger initial investment and infrastructure changes.

4. Advanced Cooling Technologies

Effective cooling is crucial for data center efficiency. ZT Systems brings expertise in advanced cooling solutions, which could help AMD further reduce energy consumption in its data center offerings. The challenge here is ensuring these cooling solutions are adaptable to different data center environments and climates. However, ensuring these cooling solutions are adaptable to different data center environments and climates can be a challenge. For example, data centers located in hot and humid climates may require more advanced cooling systems to maintain optimal temperatures. Traditional air-cooled systems account for up to 40% of a data center’s energy consumption.

5. Accelerated Renewable Energy Adoption

AMD and ZT Systems’ combined expertise could accelerate renewable energy adoption in data centers, reducing fossil fuel dependence and carbon footprints. Their integrated solutions offer better power management for variable renewable sources. Data centers could optimize workloads to match energy availability, using solar power during the day and wind power at night. While renewable energy costs and availability vary by location, this approach enables more efficient use of intermittent energy supplies.

By leveraging ZT Systems’ capabilities, AMD is poised to make significant strides in delivering environmentally responsible technology solutions. This acquisition not only strengthens AMD’s market position but also reinforces its commitment to sustainability in the tech industry, despite the challenges that may arise.

August 20, 2024
GitHub Steers Copilot Autofix Into Eye of AI Security Storm

Distributed version control and collaboration platform company GitHub has come forward with its new Copilot Autofix tool. This AI-driven software service is targeted at developers who need to address software vulnerabilities in code destined for traditional applications and inside those infused with an injection of new or existing breeds of AI.

Copilot Autofix resides in the GitHub Advanced Security (GHAS) platform product grouping. First mentioned in the spring of this year, the technology was at the public beta stage before this month progressing to a full-blown release.

This finalized first-release product features GitHub’s CodeQL scanning engine, a code analysis engine developed by GitHub to automate security checks so developers can analyze and display the results as code scanning alerts. It also features GPT-4o, a multi-modal large language model that offers real-time conversation functionalities and text generation. Copilot Autofix also features heuristics and the technology features its own set of APIs to enable teams to implement its toolset and create code suggestions (and code snippets) to fix and remediate vulnerabilities. Developers can accept, edit or reject code suggestions made.

Found Yes, Fixed Maybe

“Code scanning tools detect vulnerabilities but they don’t address the fundamental problem [of fixing software]: Remediation takes security expertise and time, two valuable resources in critically short supply. In other words, finding vulnerabilities isn’t the problem. Fixing them is,” said Mike Hanley, chief security officer and senior vice president of engineering at GitHub.

Using its newly minted “Found Means Fixed” tagline to address this exact point, Hanley says that Copilot Autofix analyzes vulnerabilities in code, explains why they matter… and offers code suggestions that help developers fix vulnerabilities as fast as they are found.

“During the public beta, we found that developers were fixing code vulnerabilities more than three times faster than those who do so manually, a powerful example of how AI agents can radically simplify and accelerate secure software development,” enthused Hanley. “Developers can keep new vulnerabilities out of their code with Copilot Autofix in the pull request, and now also pay down the backlog of security debt by generating fixes for existing vulnerabilities.”

The GitHub GHAS division said it has big plans for Copilot Autofix and its related platform toolsets. It is working to improve the scope and accuracy of “secret scanning” today. The organization defines secret scanning as a security feature that helps detect and prevent the accidental inclusion of sensitive information such as API keys, passwords, tokens and other secrets in a DevOps’s teams code repository. GitHub’s approach here means secret scanning scans code commits in repositories for known types of secrets so that repository administrators can be alerted upon detection. GitHub’s Hanley has also said that the team is developing new workflows that scale Copilot Autofix for organizations with a high volume of security debt, all on familiar developer platforms.

With initial support for JavaScript, TypeScript, Java and Python, Copilot Autofix has now been reported to also extend support to C#, C/C++, Go, Kotlin, Swift and Ruby. Available for free use to developers working on open source projects, paying enterprise GitHub Enterprise Cloud customers who subscribe to GHAS will find Copilot Autofix enabled by default in their GHAS settings.

AI for Good, for Good?

Given the speed at which modern DevOps teams must attempt to create working functional software offerings and the always-present fact that not every group will feature a security guru, GitHub says the market is ripe for an AI-fuelled tool capable of remediation at this level. At a time when less technical businesspeople are “worried about the impact of AI”, this is perhaps a good example of automation intelligence being used to fix problems rather than create new ones.

August 20, 2024
Hello Henrik, Neuralogics’ Multi-Contextual Intelligence AI Engine

Neuralogics has followed the industry trend that sees technology vendors label their AI engines with a human name. With Salesforce Einstein, IBM Watson and Davis (that one’s Dynatrace) and even the possible inclusion of Waldo Search already with us, Neuralogics now joins the family with Henrik.

Named after Neuralogics co-founder Henrik Hofmeister (his partner Jacob Laurvigen was generous enough to step back), this is an AI framework designed to enable any user to create what the company promises are “fully functional software applications” from a simple prompt.

Laurvigen, who serves as CEO and Hofmeister, who serves as chief scientist & CTO exited their previous AI and intelligence company in an acquisition two years ago. They then assembled a software engineering team made up of developers and data scientists fromTradeshift, Booking.com and Comcast. The team spent two years building the framework that made today’s launch possible.

Cheesy, But Heartfelt

“It’s our life’s work,” said Hofmeister. “Henrik.ai is pushing the boundaries of AI to amplify human potential and meet the dynamic needs of our evolving reality. We are committed to leading the AI paradigm shift, providing state-of-the-art solutions that enhance efficiency, spur creativity and tackle complex challenges.”

Not content with using every currently available buzz phrase in the technology industry in his commentary, Hofmeister suggests that this new AI tool can be used by any worker, regardless of the scope of their technical knowledge. He says that this simplified approach to application development can be used by anyone from a business leader, to a healthcare professional, an educator, or an entrepreneur.

“During my time as senior director of product at Comcast, I saw firsthand how long turnaround times and critical delays in bringing new customer solutions to market could slow down innovation,” said Christian O. Petersen, chief growth officer and founding team member at Neuralogics.

Multi-Contextual Intelligence

At the core of Henrik.ai is a network of specially trained AI models, known as “multi-contextual intelligence” functions, which work together to build complete, compliant software systems from scratch. The AI-native approach ensures that the software created is not only functional but also scalable, secure and adaptable to the technological landscape.

As it sounds, the algorithmic logic behind multi-contextual intelligence is designed to create an AI model capable of working across multiple contexts, adapting to various environments and scenarios. This is not multi-modal AI in the sense of intelligence applicable to text, speech, video and other unstructured (perhaps spatial) data; and this is not multi-model AI as such i.e. intelligence functions drawn from a selection of AI services that have been woven together to form a more complex notion of machine understanding… if anything, it is an amalgam of the two approaches combined with what Neuralogics calls its disciplinary matrix.

The Disciplinary Matrix, Re-Coded

“The disciplinary matrix at Neuralogics serves as the foundational representation of established truths and knowledge across various domains – computer science, cognitive psychology, linguistics and systems engineering. It ensures that our AI systems are built upon a robust framework of interdisciplinary knowledge, allowing them to understand and solve complex problems with depth and precision,” notes the company, in its core literature.

Neuralogics says it has also incorporated a good deal of adaptive learning into its platform so that its AI systems can make use of advanced learning algorithms that allow them to adapt based on new data, user interactions and changing environments. Cementing guardrails to ensure all AI “decisioning” is logical, ethical and transparent, the company’s multi-contextual approach may mark this out as not just another AI service innovation, but one that emulates a new facet of encoded human behavioral activity.

Plus, it’s AI made by a man called Hofmeister, so any users in the UK will drink to that.

August 19, 2024
Say ‘No’ to ‘NoOps’: Why We Can’t Afford to Let AI Run Wild

We have moved beyond the initial hype surrounding AI. Now, industries and roles across the board recognize the necessity of integrating this technology into their workflows, leading to a substantial increase in adoption. Just last year, the global market for AI was valued at nearly $200 billion and now it is expected to see an annual growth rate of 40% through the end of the decade.

This growth is prominent among software development teams. Research shows that 70% of software teams have adopted AI and 30% have implemented a strategy around it. These teams witness substantial gains in code writing velocity through AI. The numbers are proof. Software development teams with an effective AI adoption strategy are reporting an increase of 250% in the development speed.

AI technology is not perfect; it has limits and drawbacks. For example, while AI may speed up code writing, it can also increase code churn — and twice as much code means the possibility of twice as many mistakes. Popular developer platform GitHub recommends taking precautions when using code not written by a human. Whether a code is written by humans or GenAI, checking the code for quality is critical (aka a clean code approach).

Despite this reality, some envision a near future of ‘NoOps,’ where the development community can fully depend on AI through advanced automation, self-healing systems and intelligent monitoring. That may one day become possible to an extent as technology continues to advance and evolve, but our current environment is not yet equipped for this.

Right now, AI is not reliable or accurate enough to replace human developers and DevOps teams. The stakes are too high, and critical thinking and oversight are necessary for companies to start preparing for a time when AI takes over all IT operations. Believing otherwise puts your organization at risk for mistakes, poor-quality code and software and ultimately, business loss.

Will AI ever fully replace developers? I lean toward a ‘no’. But here are some things to consider when thinking about ‘NoOps’ and AI adoption.

The DevOps Community Cannot Miss the AI Opportunity

AI is the present and the future. Development teams cannot afford to miss out on the value AI provides or to fall behind in learning its different use cases, quirks and drawbacks. Developers and engineers of all roles and skill levels must be well-versed in using these GenAI tools. If they aren’t, they can be left behind.

This is especially true as software becomes an even more critical business asset. Think about just how much we rely on technology in every aspect of our lives. The phones we use, the cars we drive, the smart devices we install in our homes — all these are powered by software. This requires software companies that deliver that software to ensure their foundational code is clean, meaning it is secure, reliable, maintainable and of high quality.

It is not just developers who can derive value from AI tools. When used correctly, AI coding assistants can significantly impact the creation, management and testing of software and software infrastructure to benefit DevOps teams. For example, AI can be invaluable in the CI/CD process, streamlining it with greater automated capabilities for building, testing and deployment. AI can also integrate new code into an existing environment, which speeds up deployment as well as reduces the risk of error.

Agility and efficiency are boosted through AI automation as well. It can take care of tedious tasks so that teams don’t need to waste time on grunt work and can focus on priority projects. As AI can handle tedious and repetitive tasks, it reduces human error and allows teams to focus on work that benefits from human creativity, innovation and critical thinking.

With AI, developers can hone their code, ensuring quality in more complex parts of a project while reducing mistakes in ancillary pieces of code. Our reliance on software means code must be of high quality, which also means it is secure by design. Preventing cyberattacks reduces costly, reputation-threatening incidents, and AI can significantly aid in this effort.

But remember, AI is not perfect. It can help streamline DevOps and the development process and will continue to do so as the technology keeps improving. However, human oversight is an undeniable necessity in software. Companies cannot afford to implement AI without proper safeguards. It is not just about having the right tools, it is about ensuring you are using them in the right way and checking their work just as you would do with a human colleague.

Responsible AI Usage Includes Human Oversight

Like any emerging technology, AI needs guardrails. Teams and businesses must ensure that employees use AI in a way that does not compromise output, quality or the company’s bottom line.

The reality is that you cannot replace human critical thinking with machine learning (ML) — not now or anytime soon. Humans still have to be at the crux of vital decision-making in development and DevOps processes. For instance, AI lacks the context of a codebase; it may not understand what you are trying to achieve with a project. Its output cannot simply go into deployment without examination. You need a human safeguard.

In fact, researchers at Vectara, a GenAI platform founded by former Google employees, quantified AI hallucinations to understand the limitations and failures of this technology. They found chatbots invent things between 3% and 27% of the time. To clock these kinds of issues, they even keep a ‘hallucination leaderboard’ on GitHub. Think about how many times you have seen examples of AI chatbots and generators creating nonsensical content or responses to questions — the same concerns apply to software development and DevOps.

Working with AI also requires transparency and accountability. You cannot trust that AI is pulling data from an unimpeachable source; you must be able to verify the origins of AI contributions to ensure their accuracy. Human teams also need to know what these tools can do: Their limitations, rules and specific use cases. This comes down to ensuring teams have proper education and training.

It is not enough though to just learn from current use cases. As these tools change and advance, the way we use them should also be evaluated consistently. Where are we seeing drawbacks? Improvements? Failures? How can we best use this technology right now? These are the kinds of open and honest conversations developers and DevOps teams should constantly have to learn from each other.

We have no way of predicting the future or knowing what AI will look like in five, 10 or 15 years — which means we will always have to stay nimble and evolve alongside it. DevOps teams will need to continue refining and adapting best practices.

An Unpredictable, AI-Powered Future

AI now plays an unignorable, major part in the software landscape. As we move toward an AI-driven future, we need to keep understanding its implications and strive for the best outcome.

As we anticipate AI’s growth and advancement over the next decade, it is important to recognize that we are not currently technologically equipped to grant these tools complete control over all processes. The idea of a ‘NoOps’ future is an intriguing one — but for the time being, it is simply not viable. Just like we are not yet in a place where driverless cars are unquestionably safer alternatives to our vehicles, or we can fully trust chatbots to answer live chat questions correctly, AI coding assistants cannot simply run by themselves.

Developers and DevOps teams cannot discount the need for human involvement in software. Like its human counterparts, AI is not perfect. Still, there is an undeniable opportunity at hand. We live in an exciting time of innovation and on the horizon of incredible transformation. But only by exercising some level of caution as we reach that precipice can we ensure we will reap the benefits when it arrives.

August 9, 2024