Precedent is an innovative software platform designed to streamline business processes, decision-making, and workflow automation. Built to provide organizations with the flexibility to design, optimize, and implement complex workflows, Precedent supports both simple and highly intricate decision-making scenarios. It allows teams to automate routine tasks, improve the consistency of decisions, and ensure a seamless flow of operations by connecting disparate systems and processes.

At its core, Precedent is about reducing manual intervention, accelerating decision-making, and providing clarity in process management. Its flexible design allows businesses to create automated workflows, support collaborative work environments, and track results in real-time.

Precedent integrates seamlessly with various enterprise systems and can be used in both small businesses and large-scale organizations. It offers a broad range of applications, including document approval workflows, customer service operations, marketing campaign management, and more.

#Major_Use_Cases_of_Precedent

Precedent finds applicability across a wide array of industries where workflow automation, efficient decision-making, and process management are crucial. Some of the major use cases include:

1. Automated Approval Workflows
   Precedent allows businesses to automate approval processes, such as expense reports, document reviews, and contract approvals, ensuring efficiency and consistency in decision-making.
2. Data Management and Processing
   By integrating with various data sources, Precedent helps organizations manage and process data more effectively, making decisions based on real-time insights.
3. Customer Service Automation
   Precedent can be used to automate customer service workflows, such as handling support tickets, processing customer inquiries, or managing the escalation process to improve response time.
4. Marketing Campaign Automation
   Marketers use Precedent to automate repetitive tasks such as email campaigns, social media scheduling, and reporting, enabling them to focus on strategy and creativity.
5. Business Intelligence (BI) and Analytics
   Precedent streamlines data analysis workflows by connecting various data points, allowing teams to generate insights quickly and automate data reporting.
6. Compliance and Risk Management
   Automating the tracking of regulatory compliance and risk management workflows helps organizations stay up to date with laws and regulations without the manual overhead.
7. Employee Onboarding and Training
   Precedent supports HR workflows by automating employee onboarding, training scheduling, and compliance checks, providing new hires with a seamless transition into the organization.
8. Document Management
   Organizations use Precedent to automate the storage, approval, and distribution of documents, ensuring that every process is documented and approved without bottlenecks.

#How_Precedent_Works_and_Architecture

Core Architecture

Precedent is designed to be modular, scalable, and highly configurable. The system works through a combination of several key components:

1. Workflow Designer
   The Workflow Designer is the user interface where business users can visually design workflows by dragging and dropping components. It’s here that automated processes, decision points, and task sequences are defined.
2. Business Logic Engine
   At the heart of Precedent’s functionality is the Business Logic Engine, which executes the workflows created by users. It interprets the workflow designs, manages task assignments, triggers decisions based on predefined rules, and ensures that tasks are routed appropriately.
3. API Integrations
   Precedent can integrate with a variety of other enterprise applications and systems through APIs. Whether it’s a CRM, ERP, HRMS, or custom internal system, Precedent can interact with and pull data from multiple sources, automating actions based on real-time data.
4. User Roles & Permissions
   Precedent has a robust user role management system that allows businesses to set permissions based on the roles within the workflow. It ensures that sensitive data is only accessible to authorized users and that the right team members are involved at each stage of a process.
5. Audit Trails and Reporting
   Precedent tracks every action taken during the execution of a workflow, allowing businesses to maintain an audit trail. This feature is crucial for compliance, as it provides a transparent record of all steps taken during decision-making or process automation.
6. Notification System
   The notification system ensures that stakeholders are always informed. Alerts, reminders, and status updates are sent to users based on workflow progress, ensuring that no step is skipped or delayed.

Execution Flow

1. Workflow Definition: The business user defines a workflow using the drag-and-drop interface in the Workflow Designer.
2. Triggering Action: A predefined event (such as a customer request or data input) triggers the workflow to start.
3. Task Execution: The Business Logic Engine processes each task, interacting with external systems and ensuring that the workflow progresses as designed.
4. Decision Points: Based on the data received, decision points evaluate which path the workflow should take (e.g., approve or reject a request).
5. Finalizing Workflow: Once all tasks are completed, the workflow ends. A report or notification is generated, and any follow-up actions are triggered.

#Basic_Workflow_of_Precedent

1. Workflow Creation: Create a workflow using the Workflow Designer by specifying tasks, decision points, and rules.
2. Data Input: Input data required for the workflow (can be manual or automated from integrated systems).
3. Execution: Precedent starts the workflow based on the defined triggers and runs each task in sequence or parallel.
4. Decision-Making: Precedent evaluates decision points based on predefined rules or AI-powered algorithms.
5. Completion: Once all tasks are completed, Precedent sends notifications, updates databases, or triggers additional workflows as needed.
6. Tracking & Reporting: Real-time tracking and detailed reports are generated for oversight and auditing.

#Step_by_Step_Getting_Started_with_Precedent

Step 1: Set Up Precedent Account

Begin by creating a Precedent account through their official website or cloud platform. Choose the appropriate subscription tier based on your organization’s needs.

Step 2: Access the Workflow Designer

Once logged in, access the Workflow Designer, where you can start creating your first workflow. The designer features a drag-and-drop interface to simplify workflow creation.

Step 3: Define Tasks and Rules

Define the steps involved in your workflow. For example, if you are automating a document approval process, your workflow will include tasks such as document submission, approval by managers, and final approval by executives.

Step 4: Integrate with Other Systems

Precedent allows integration with your existing systems such as CRM, ERP, or HR software. Set up API connections or use native integrations to pull data from these systems into your workflow.

Step 5: Configure User Permissions

Set roles and permissions for team members. For instance, you might allow only managers to approve requests, while junior staff can only submit documents for review.

Step 6: Test the Workflow

Before rolling out, test your workflow by running it in a controlled environment. Ensure that all steps are executed as expected and that the decision points function correctly.

Step 7: Deploy and Monitor

Once tested, deploy the workflow. Precedent’s reporting tools will allow you to monitor its progress and make adjustments if needed. You can track the workflow’s performance, gather data, and adjust as necessary.

the monitoring and security platform for cloud applications, announced the Datadog Vulnerability Analysis GitHub Action, Datadog’s first action listed on the GitHub Marketplace. GitHub Actions provide powerful, flexible CI/CD with the ability to automate any software development workflow. The Datadog action continuously monitors dependency and version information of code being deployed. By integrating this data with Datadog’s Continuous Profiler and Snyk’s Vulnerability database, this provides a real-time view of what code is actually accessible and vulnerable in production.

Scanning applications for known vulnerabilities often yields a long list of issues that are difficult to prioritize and subsequently fix. With the data collected by the new action, vulnerability analysis will be performed by the Datadog Continuous Profiler based on Snyk vulnerability metadata. This allows engineering teams to immediately detect when and how often vulnerable methods are invoked in live environments and prioritize their security fixes based on real-world application behavior. The Datadog Vulnerability Analysis GitHub Action can be found and installed directly from the GitHub Marketplace without needing to manage scripts or infrastructure.

“Maintaining strong security posture is critical for modern applications, but with traditional vulnerability analysis it can be difficult to distinguish the signal from the noise,” said Ilan Rabinovitch, Vice President, Product and Community at Datadog. “Integrating the Continuous Profiler with the vulnerability database highlights meaningful security vulnerabilities, while utilizing the GitHub Action automates this process by bringing security directly into application development.”

We’re moving towards a world where security, testing, and even responsibility for production operations are shifting left towards the developer,” said Jeremy Epling, Vice President, Product Management at GitHub. “Partnering with full-stack monitoring leaders like Datadog makes it easy for developers and DevOps teams to incorporate critical operations tooling as part of their everyday work environment, so teams can focus on delivering value, at greater velocity.”

“By combining Snyk-enriched vulnerability metadata with the Datadog Continuous Profiler, for the first time developers can precisely pinpoint when an application actually calls vulnerable code, to better prioritize remediation efforts,” said Geva Solomonovich, CTO Global Alliances, Snyk. “Our partnership with Datadog will allow developers to deploy their security resources with greater efficiency.”

SAN MATEO, Calif., — October 27, 2020 — Applitools provider of a test automation platform powered by Visual AI and Ultrafast Grid, announces integrations with GitHub, GitHub Actions, and the Microsoft Visual Studio App Center. The integrations allow developers to seamlessly add Visual AI-powered testing to every build and pull request (PR), resulting in greater UI version control and improved developer workflows.

In addition to the testing process improvements, the integrations enable developers to easily and quickly correlate code changes with UI/UX updates for Visual UI version control across web and mobile apps, using any testing framework and programming language.

When feature branches are created, Applitools instantly mirrors the feature branch to allow a new set of baseline images. This process simplifies the automated testing of all feature branches without impacting the trunk and its tests. As PRs are merged back in, the integration automatically updates the target branch with the source branch’s baseline images while ensuring that no conflicts exist between branches, eliminating test maintenance overhead.

With GitHub Actions, developers get automation directly within their workflow. Now developers can automate visual testing for their code by integrating Applitools through a GitHub Actions workflow. From there, they can quickly address and resolve regressions by using Applitools root cause analysis capabilities. This helps teams customize their workflows to meet developers’ needs while employing Visual AI to help automate and accelerate build, test, and release pipelines.

Applitools Eyes along with the new GitHub integrations is available for free for open source projects hosted on GitHub using the Applitools Open Source plan.

Priyanka Halder, Senior Manager for Quality Engineering at GoodRx, said: “The integration of Applitools with GitHub merges two essential products that enable our team to continuously deliver. This collaboration unlocked new efficiencies without any changes to our workflow. Having used this for a while now, I cannot imagine our team being without UI version control and auto-baseline updating on merge.”

With Visual Studio App Center, developers get streamlined workflows for mobile apps including automated UI testing. By combining this capability with Applitools, developers can now automate functional and visual testing seamlessly and find bugs on any device type before release, while ensuring that the app is functioning properly and is visually compliant across all devices and screen sizes. Developers receive test results, full-resolution screenshots of each step, and performance metrics.

“Expanding our integrations with GitHub and Microsoft helps developers deliver innovative solutions faster,” said Moshe Milman, co-founder and COO of Applitools. “These integrations make it effortless for developers to add Visual AI and cross-browser testing capabilities to their pipeline, allowing them to bring their products to market faster and with higher quality.”

In addition to the GitHub and Microsoft integrations, Applitools has also joined the GitHub Partner Program as a Technology Partner.

“We’re excited to welcome Applitools to the GitHub Partner Program and for them to expand their role within the GitHub ecosystem,” said Jeremy Adams, Director of Business Development and Alliances at GitHub. “Applitools’ Visual AI powered testing platform and GitHub’s automated, streamlined developer workflow pair perfectly to support our shared vision of making it easier to ship higher quality software, faster.”

Applitools enables engineering teams to release web and mobile apps quickly at low cost. Applitools Visual AI modernizes test automation use cases—including functional testing, visual testing, web and mobile UI/UX testing, cross-browser testing, responsive web design testing, cross-device testing, PDF testing, accessibility testing and compliance testing—to help organizations deliver innovation at the speed of CI/CD at a significantly lower total cost of ownership (TCO).

Hundreds of companies from verticals including technology, banking, insurance, retail, pharmaceuticals, and publishing—including 50 of the Fortune 100—use Applitools to deliver digital experiences to millions of customers on any device and browser, across all screen sizes and operating systems. Applitools is headquartered in San Mateo, Calif., with an R&D center in Tel Aviv, Israel. For more information, please visit applitools.com.

Private Security Advisories: A Safe Space for Disclosure

When a project maintainer or anyone with admin privileges on a repository discovers a vulnerability, they can now create a draft security advisory inside GitHub. These advisories provide a private, controlled environment to discuss the vulnerability and coordinate a fix before any public disclosure.

Security advisories are private by default, regardless of whether the repository itself is public or private. Access can be restricted to specific collaborators, giving maintainers fine-grained control over who is aware of the issue during the remediation period.

Temporary Private Forks for Safe Patch Development

One of the most practically useful features introduced is the ability to create a temporary private fork of a repository directly from within a security advisory. This allows developers to write, review, and test a fix without accidentally leaking vulnerability details to the public — for example, through a public pull request or a CI build log.

To prevent unintended exposure, temporary private forks are explicitly excluded from continuous integration pipelines and other repository integrations. Once the fix is ready, a pull request can be created from the private fork and merged into the main branch, all without leaving the security advisory workflow.

Everything Under a Unified Security Tab

All of these capabilities are grouped under a dedicated Security tab in the GitHub repository UI. From this single location, maintainers can:

• Create and manage a security advisory
• Spin up a temporary private fork
• Open a pull request with the fix
• Merge the fix into the main branch
• Request and assign a CVE identifier

This consolidation reduces the context-switching that previously forced maintainers to juggle GitHub, email threads, and external CVE request processes simultaneously.

GitHub as a CVE Numbering Authority

The CVE (Common Vulnerabilities and Exposures) system, operated by the MITRE Corporation, provides a standardized way to uniquely identify and reference security vulnerabilities. Having a CVE identifier is important even before a fix is available — it allows security researchers, vendors, and users to refer to the same issue unambiguously across advisories, patch notes, and security bulletins.

By becoming a CNA, GitHub can now issue CVE identifiers directly for vulnerabilities reported through its security advisory workflow. Previously, maintainers had to request a CVE through a separate process with MITRE or another CNA, which added friction and delay to coordinated disclosure. Integrating this step into the GitHub UI removes that barrier for open source projects hosted on the platform.

GitHub’s Broader Security Ecosystem

The security advisory and CNA announcements are part of a longer trajectory of security features GitHub has added to the platform:

• Vulnerability alerts: GitHub notifies maintainers when known vulnerabilities are detected in a project’s dependencies.
• Dependabot automatic security PRs: Introduced in the months before this announcement, Dependabot scans project dependencies and automatically opens pull requests to update any that have known vulnerabilities.
• Token scanning: GitHub scans commits pushed to public repositories for accidentally committed secrets — such as API tokens and cryptographic keys — and alerts the relevant service provider.

Together, these features represent GitHub’s effort to shift security practices left — making it easier to catch and remediate vulnerabilities earlier in the development lifecycle, directly within the tools developers already use every day.

Availability

At the time of the announcement in September 2019, maintainer security advisories were available as a public beta. Developers and maintainers hosting open source projects on GitHub were encouraged to try the feature and provide feedback through the beta period.

What Is a CI/CD Pipeline and Why Does It Matter?

Before adopting serverless for DevOps, it helps to be precise about what a CI/CD pipeline actually does.

• Continuous Integration (CI) covers the build and automated testing phases of the development cycle. Developers commit code to a shared repository, and the pipeline automatically builds and tests that code to catch integration errors early.
• Continuous Delivery (CD) extends CI by automating deployment tests and configuration steps, ensuring that every validated change is always in a deployable state.

A typical DevOps toolchain progresses through four broad stages: build automation, continuous integration, application deployment and configuration management, and infrastructure deployment. Each stage is triggered by the successful completion of the previous one, which means the pipeline is inherently an event-driven system — the same model that underlies serverless computing.

Standardizing these stages reduces the time between a code change and a deployed update, improves repeatability, and removes manual handoffs that introduce risk.

Why Serverless and DevOps Are a Conceptual Match

Serverless functions execute code in response to specific events — an HTTP request, a message on a queue, a change to a storage object, or a webhook from a source control system. This maps directly onto how a CI/CD pipeline works: a developer pushes a commit, which triggers a build, which on success triggers a test run, and so on.

Despite this conceptual alignment, there has historically been a gap between serverless and the established CI/CD tooling. Tools such as Jenkins and Travis CI predate serverless computing, and because the overhead of running a Jenkins server is relatively modest, there has been limited motivation to rebuild those tools from the ground up with a serverless-first design.

How the Serverless DevOps Ecosystem Is Evolving

That gap is beginning to close. Several notable projects and products are pushing serverless thinking into the CI/CD space.

Serverless Jenkins X

One of the more prominent examples is Serverless Jenkins, an offshoot of the Jenkins X project that runs on Kubernetes. Proposed by engineers at CloudBees, the goal of Serverless Jenkins is to ensure that Jenkins only processes pipelines when a build is actually needed, rather than running a persistent server around the clock — directly reducing cloud infrastructure costs.

Internally, the approach relies on a combination of tools including Prow, an event-driven CI/CD system built by the Kubernetes development team. Prow was originally created to address the challenge of scaling Jenkins to handle the large volume of GitHub repositories and CI/CD jobs that the Kubernetes project itself required. It automates GitHub actions — issue labelling, PR merging, test triggering — in response to events, fitting naturally into a serverless design.

The Serverless Framework

For teams building serverless applications (as opposed to serverless pipelines), the Serverless Framework simplifies and automates the packaging, deployment, and lifecycle management of serverless functions across multiple cloud providers. It is one of the most widely adopted open source tools in this space.

Cloud-Native Serverless Services for Every Pipeline Stage

For most teams, the path to a serverless DevOps workflow runs through the major cloud providers. AWS, Google Cloud, and Microsoft Azure each offer portfolios of on-demand, event-driven services that cover every stage of the CI/CD pipeline without requiring teams to provision or manage servers.

AWS

• AWS Lambda — serverless function execution triggered by events from dozens of AWS services.
• AWS CodePipeline — fully managed CI/CD orchestration service.
• AWS CodeBuild — on-demand build service; you pay only for the build minutes consumed.
• AWS CodeDeploy — automated deployment to compute services including Lambda, EC2, and ECS.

Microsoft Azure

• Azure Functions — event-driven serverless compute with broad trigger and binding support.
• Azure Logic Apps — low-code workflow automation that integrates with hundreds of connectors.
• Azure DevOps Pipelines — hosted CI/CD service with agents that scale on demand.

Google Cloud

• Google Cloud Functions — lightweight, event-driven functions triggered by Cloud events or HTTP.
• Cloud Build — serverless CI/CD platform that executes builds in managed containers.

Open Source Serverless Platforms

Open source serverless runtimes — including Apache OpenWhisk, Fission, and Fn Project — exist for teams that want to run serverless infrastructure on-premises or avoid cloud vendor lock-in. However, as of the time of writing these platforms had not reached the same level of maturity, ecosystem integration, or community adoption as their managed cloud equivalents.

Practical Tips: Building a Serverless DevOps Workflow

Shifting your pipeline to a serverless model does not have to happen all at once. Consider the following approach:

1. Audit Your Existing Pipeline First

Map your current CI/CD stages and identify which steps involve persistent infrastructure (long-running build servers, always-on test environments) versus steps that are already event-triggered and short-lived. The latter are natural serverless candidates.

2. Adopt Cloud Services Incrementally

You do not need to replace your entire toolchain on day one. Use managed cloud CI/CD services to fill specific gaps — for example, replacing a self-hosted build agent with AWS CodeBuild or Google Cloud Build — before committing to a full migration.

3. Investigate Jenkins X If You Already Use Jenkins and Kubernetes

Organizations that have invested in Jenkins and have containerized workloads running on Kubernetes should evaluate Serverless Jenkins X and Prow before committing to a full replacement. This path allows a gradual migration while reusing existing pipeline logic.

4. Consider CI/CD-as-a-Service for Greenfield Teams

Teams that are just starting to formalize their DevOps processes may find it faster to adopt a fully managed CI/CD-as-a-service product rather than assembling individual cloud services. Products such as CloudBees Codeship, CloudBees Rollout, and Harness provide opinionated, end-to-end pipelines without requiring teams to wire up individual cloud primitives.

5. Review Available Integrations

Cloud providers invest heavily in pre-built integrations between their serverless services and popular DevOps tools. Before writing custom glue code, review the integration marketplace for your cloud provider and check whether your existing source control, artifact registry, and monitoring tools already have native connectors.

Key Benefits of a Serverless DevOps Approach

• Cost efficiency — Pay only for the compute consumed during active pipeline runs, rather than maintaining always-on build infrastructure.
• Automatic scaling — Serverless platforms scale function invocations in response to demand, so a spike in commits does not queue behind a fixed pool of build agents.
• Reduced operational overhead — No servers to patch, resize, or monitor for the CI/CD infrastructure itself.
• Event-driven consistency — Aligning your pipeline with the same event-driven model used by your application functions reduces conceptual context-switching for developers.

Summary

Serverless computing and CI/CD pipelines share the same foundational model: code executes in response to events, not because a server is sitting idle waiting for work. The major cloud providers have built serverless-native services that can cover every stage of a DevOps pipeline, and projects like Jenkins X and Prow are bringing the same thinking to teams that prefer open source tooling on Kubernetes. The most pragmatic path forward is incremental adoption — identify the stages in your current pipeline where serverless provides the clearest benefit, migrate those first, and build from there.

Original source: searchitoperations.techtarget.com (2019)

Microsoft’s GitHub Actions for workflow automation from the beginning of an idea to production got an upgrade today that brings CI/CD to teams building and sharing code. Continuous integration and continuous delivery or CI/CD is a way for developers working on the same project to merge and integrate changes into a code base without conflict.

GitHub also announced today its code repository is now used by 40 million developers worldwide, up from 31 million in fall 2018 and 24 million in 2017.

The latest version of GitHub Actions in beta extends support to programming languages and frameworks like C/C++, Ruby, PHP, and Python. GitHub first introduced Actions in developer beta at GitHub Universe in fall 2018 with support for Java and .NET projects.

“The way that the initial launch for Actions worked was anything that you can basically run on Linux in a container was supported, OK? Now, we are broadening that support to virtually any programming language, because you can run on any of the big platforms, from Linux, Linux, containers, Windows, and then macOS,” GitHub head of product design Max Schoening told VentureBeat in a phone interview.

At launch, GitHub Actions was able to run on Linux in a container but the latest version also works on Windows and macOS computers and actions can now be written in JavaScript. GitHub Actions has been used thus far by tens of thousands of developers, Schoening said.

To create CI/CD compatible with a great range of programming languages and clouds, GitHub partnered with DevOps platforms with CI services like CircleCI and LaunchDarkly. The news comes a day after CircleCI made its services available to Microsoft programmers for the first time.

Live logs with real-time feedback and the ability to share a link to specific lines in a log are part of the update. Matrix builds for testing multiple apps at once will also be part of the latest version of Actions, as introduced in an event at GitHub headquarters in San Francisco.

Also new: Suggested workflows, a feature that draws on GitHub’s machine learning project and other teams within the company for workflow automation, Schoening said.

“We look at the languages that are predominantly used in repositories and therefore make certain predictions. Over time, because we want to make it as easy as possible to onboard, we are going to look at even more detailed and nuanced analysis of a repo,” he said. “So for example, instead of just saying, ‘Hey, you’re clearly using a node project, do you want to get started with our node workflows?’ diving one layer deeper and say, ‘Oh, you’re using this type of node project.’ And so those are things that we’re looking into for the future, because it makes it just so much more of an easier onboarding.”

GitHub Actions will be made generally available November 13 at GitHub Universe, a company spokesperson told VentureBeat in an email.

Once generally available, Actions will remain free for public repositories and open source projects, and private repositories will be charged on a pay-as-you-go basis.

Generally available Actions will include features like the ability to host actions on your own virtual machine and actions for GitHub Enterprise Server customers.

What Changed in Moogsoft AIOps 7.2

Moogsoft released version 7.2 of its AIOps platform with several enhancements aimed at reducing alert fatigue, improving workflow automation, and making AI-driven recommendations more transparent for IT operations teams. The update touches three distinct areas: workflow automation, alert clustering visibility, and individual-level performance analytics.

Workflow Engine: Automating Incident Response

The headline addition in version 7.2 is a dedicated Workflow Engine. According to Gabe Coelho-Kostolny, senior product management and product marketing leader at Moogsoft, the engine lets teams trigger automated actions directly from within the platform or in connected external systems. Supported actions include:

• Push notifications to communication and collaboration tools
• Automated ticket creation in ITSM platforms
• Custom remediation tasks that slot into existing incident management workflows

The goal is to close the gap between an AI-generated insight and the operational action that follows—without requiring a human to manually initiate each step.

Expanded Integration Ecosystem

Version 7.2 extends Moogsoft’s integration library with new connectors and proxy support for a broad range of monitoring and collaboration platforms. New additions include:

• New Relic Insights — pulling observability data directly into the AIOps correlation engine
• Microsoft Teams — enabling notifications and workflow triggers within team chat
• Proxy support for polling integrations covering: Zenoss, Zabbix, VMware vCenter, VMware vSphere, SolarWinds, Broadcom Spectrum, and SevOne

The proxy-based polling approach allows Moogsoft to collect data from these platforms in environments where direct agent installation may not be feasible—a practical concern in large enterprise estates with strict network segmentation.

Real-Time Alert Clustering Visualization

One of the persistent criticisms of AIOps platforms is the opacity of their machine learning models—teams are asked to act on recommendations they cannot inspect or tune. Moogsoft addresses this directly in 7.2 by adding tooling that lets IT operations staff:

• Visualize alert clustering algorithms as they run in real time
• Fine-tune clustering parameters when the default behavior does not match operational expectations

Coelho-Kostolny positioned this as a trust-building feature. When operators can see why alerts are being grouped together—and can intervene if the grouping looks wrong—they are more likely to act on the platform’s recommendations rather than second-guessing them.

Individual Statistics Dashboard

The release also ships an analytics dashboard called Individual Statistics. The dashboard is aimed at engineering managers and team leads who need to move beyond team-level aggregate metrics. It provides drill-down views that surface each team member’s workload and key performance indicators—useful for capacity planning, workload balancing, and identifying burnout risk before it becomes a retention problem.

Why DevOps Teams Are the Core AIOps Audience

Coelho-Kostolny noted a strong correlation between organizations that have adopted mature DevOps practices and those actively evaluating or deploying AIOps. The connection is structural rather than coincidental: modern DevOps pipelines—with continuous delivery, microservices, containers, and cloud-native infrastructure—generate an order of magnitude more telemetry events than traditional IT environments. The volume and velocity of those alerts quickly exceeds what human operators can triage manually, creating the conditions where AI-assisted correlation becomes operationally necessary rather than just aspirational.

The concern in these environments is not whether AI will replace IT administrators, but whether IT administrators can keep pace with the rate of change without AI assistance. The practical framing is augmentation: AIOps handles noise reduction and pattern recognition at scale so that human engineers can focus on decisions that actually require judgment.

Data Quality Remains the Hidden Prerequisite

Every AIOps platform depends on large volumes of accurate, well-structured data about the IT environment. Coelho-Kostolny acknowledged that data collection often turns out to be a harder problem than organizations anticipate when they begin an AIOps implementation. Incomplete or inconsistent monitoring coverage upstream will degrade the quality of AI-generated recommendations downstream—no platform can compensate for fundamentally poor input data.

Evaluating AIOps Claims: True AI vs. Rules Engines

With AIOps adoption approaching what Coelho-Kostolny describes as a tipping point in the enterprise, vendor marketing has intensified. He warned buyers to scrutinize what is actually under the hood. A meaningful distinction exists between:

• Genuine machine learning and AI — systems that learn from historical event data, adapt to environmental changes, and surface non-obvious patterns
• Rules-based engines dressed as AI — traditional ITSM correlation rules marketed under an AIOps label, which do not learn and do not generalize beyond what an administrator explicitly programs

The practical test: does the platform’s behavior improve over time with more data and without manual rule updates? If not, it is likely rules-based regardless of the marketing language used.

The Confidence Problem in AI-Driven Operations

Even well-implemented AIOps platforms introduce a risk that is qualitatively different from human error: when an AI system acts on a flawed assumption, it tends to do so consistently and at scale. A single human administrator making a wrong call affects one incident; an AI acting on a bad model can affect every incident simultaneously. This makes explainability and human override capability—exactly what Moogsoft’s clustering visualization feature addresses—essential requirements rather than optional conveniences.

IT teams evaluating AIOps platforms should ask vendors directly: what controls exist to inspect, override, and retrain the model when its recommendations diverge from operational reality?

Summary

For further reading on how AIOps intersects with observability and incident management, see the coverage of AppDynamics’ AIOps approach on devops.com.

GitHub Desktop 2.0: What Changed and Why It Matters

GitHub Desktop 2.0 marked a significant step forward for developers who prefer a graphical interface over the command line. The release focused on two highly requested Git operations — stashing and rebasing — while also adding co-author attribution, emoji support in commit messages, and tighter pull request integration with GitHub.com.

According to GitHub’s Billy Griffin, the update was driven directly by user feedback: “We’re focused on listening to our users and supporting the workflows you need to be most successful when building software. Whether you’re a seasoned developer or brand new to concepts like version control, GitHub Desktop puts the things you need most front and centre.”

Stashing: Save Work-in-Progress Without Committing

One of the most common friction points in a Git-based workflow is needing to switch branches before your current changes are ready to commit. Previously, this often meant either making a premature commit or discarding changes temporarily — neither of which is ideal.

GitHub Desktop 2.0 surfaces Git’s stash functionality through a user-friendly interface. When switching branches with uncommitted work, developers are now prompted to either:

• Bring the changes to the new branch — useful when the work is relevant to the branch you’re switching to.
• Leave the changes on the current branch — stashing them so you can return later without losing progress.

As Griffin explains: “It’s a common situation: you’re in the middle of reproducing and fixing a bug, and you need to switch context temporarily… In Desktop 2.0, if you’re not ready to commit your work, you can choose to bring your changes to the new branch or keep them on your current branch.”

For teams practicing trunk-based development or juggling multiple feature branches, this reduces interruption and makes context-switching significantly less disruptive.

Rebasing: Keep Commit History Clean

Many teams enforce a linear commit history to make git log and code review easier to follow. Before Desktop 2.0, developers who wanted to rebase rather than merge had to drop to the command line — a barrier for less experienced contributors.

Desktop 2.0 adds native rebase support, giving teams the choice of how to integrate branches without leaving the application. Griffin notes:

“Developers have also shared that many teams prefer a clean commit history without merge commits… If you’re used to merging branches normally, you can still continue using the same workflow. But if you work in a repository where you don’t want those merge commits, Desktop 2.0 now supports rebasing to help you keep that commit history clean.”

Importantly, the merge workflow remains available — this is an opt-in capability, not a replacement. Teams can standardise on whichever strategy suits their project without forcing individuals to the CLI.

Co-Author Attribution: Giving Credit Where It’s Due

Pair programming and mob programming sessions often result in commits that represent the work of multiple people, but only the person who pushes gets credited. GitHub Desktop 2.0 addresses this by allowing developers to tag co-authors directly in the commit UI.

To add a co-author, you mention their GitHub username in the commit dialog. GitHub then resolves the attribution and includes it in the commit metadata, ensuring contribution graphs and pull request history accurately reflect everyone involved. This is especially valuable for teams that track individual contributions for performance reviews or open-source recognition.

Emoji Support in Commit Messages

GitHub Desktop 2.0 also added support for emoji in commit messages — a small but popular quality-of-life improvement. Projects that follow conventions like Gitmoji use emoji prefixes to communicate the nature of a change at a glance (e.g., 🐛 for bug fixes, ✨ for new features). Desktop 2.0 makes composing these messages easier without copy-pasting from external sources.

Pull Request Integration: Bridging GitHub.com and Your Local Machine

A key theme of Desktop 2.0 is reducing the number of times a developer needs to switch between the desktop application and the browser. The release introduced tighter pull request integration:

• Open any pull request from GitHub.com directly in Desktop — the branch is checked out locally and ready for review or testing without manual setup.
• Suggested next steps after a commit — once a commit is complete, the application surfaces contextual actions, such as creating a pull request, reducing the cognitive overhead of remembering what to do next.
• Easier pull request creation — the workflow from local branch to pull request is now more streamlined, reducing the number of manual steps required.

Griffin described the direction: “Looking forward, you can expect to see more in GitHub Desktop that surfaces context from GitHub.com to help make you even more productive on your local machine.”

The Broader Goal: Lowering the Barrier to Entry

GitHub Desktop 2.0 reflects GitHub’s stated goal of making professional-grade version control workflows accessible to developers at all skill levels. Features like stashing and rebasing have long been available via the Git CLI, but their complexity makes them intimidating for newcomers. By surfacing these capabilities with clear UI prompts and descriptive language, GitHub Desktop 2.0 helps teams standardise workflows without requiring every team member to have deep Git expertise.

For DevOps teams onboarding junior developers or contributors from non-engineering backgrounds, this kind of tooling can meaningfully reduce training overhead and improve consistency in how code changes are managed.

Summary of GitHub Desktop 2.0 Features

GitHub Desktop is a free, open-source application available for macOS and Windows. The source code is available on GitHub.

The answer lies in making the CI/CD workflow bidirectional—not just a path for shipping code, but a feedback system for learning how code actually performs. That principle is called continuous reliability.

A Feedback Loop Between Everyone

The most common feedback loop in software delivery runs between development and QA. Developers write code, QA tests it in a staging environment, defects are filed, and the cycle repeats. This loop is valuable, but it is also incomplete.

Production environments routinely surface behavior that staging never revealed. Edge-case traffic patterns, real user data volumes, and unexpected infrastructure conditions can all expose issues that passed every pre-production check. QA and Site Reliability Engineering (SRE) teams therefore need feedback that extends into production—accurate, timely signal about release quality after deployment.

Continuous reliability treats code quality gates and contextual feedback loops as first-class CI/CD components. The goal is to give QA and SRE teams the ability to identify critical anomalies as they move from staging toward production, and ideally to gate those anomalies before they affect the application or end users.

Defining the Anomalies That Matter

Anomaly detection is the technical foundation of continuous reliability. In most applications, errors and performance degradations fall into three distinct categories:

1. New defects — Errors that did not exist in the previous release, typically introduced by code changes.
2. Increasing errors — A rise in the relative volume of existing errors (measured against throughput), usually triggered by a code or infrastructure change.
3. Slowdowns — A measurable decrease in the response time of critical code paths, caused by inefficient logic, improper resource provisioning, or misconfiguration.

Any of these anomaly types can cascade into secondary symptoms: elevated CPU usage, garbage-collection pressure, and increased I/O wait are all common downstream effects. Catching the root anomaly early is therefore more efficient than diagnosing the cascade after the fact.

The practical challenge is that large-scale distributed systems generate enormous volumes of log and telemetry data. The noise-to-signal ratio can be so high that teams effectively rely on user complaints rather than instrumentation to detect problems. Addressing this requires an objective, automated measure of release quality—one that combines code-level data with machine learning to distinguish meaningful regressions from background noise, and to surface only the anomalies that warrant action.

Continuous Reliability Through Quality Gates

Once you have a working definition of what constitutes an anomaly for your application, the next step is encoding that definition into automated quality gates—checkpoints in the pipeline that evaluate a release before it is promoted to the next environment.

Mapping the three anomaly types to actionable gates produces four practical checks:

1. Error volume — The normalized error rate (errors relative to call volume) must not increase between releases. An uptick here is a reliable signal that something regressed.
2. Unique error count — The number of distinct error signatures, particularly in critical services, must not grow between releases. New unique errors are a stronger indicator of a defect than a spike in an existing error.
3. New errors — Any new error that is classified as critical severity, or that appears at a high rate immediately after deployment, should automatically block promotion.
4. Increasing errors and slowdowns — Severe regressions in error rate or response time should also block promotion until they are investigated and resolved.

These four gates are designed to be broadly applicable without becoming unwieldy. They give QA and SRE teams a clear, consistent benchmark against which every release is evaluated—whether it is destined for a staging environment, a canary deployment, or full production rollout.

Putting It Into Practice

Implementing continuous reliability is less about adopting a single tool and more about embedding a quality-measurement mindset into your existing pipeline. A few practical starting points:

• Instrument at the code level. Application Performance Monitoring (APM) and distributed tracing give you the granular, per-service data you need to detect the three anomaly types reliably.
• Automate gate evaluation. Quality gates that require a human to manually review dashboards before every promotion will be skipped under deadline pressure. Integrate gate evaluation directly into your CI/CD pipeline so that a failed gate blocks the pipeline automatically.
• Prioritize by severity. Not every anomaly warrants a rollback. Build a severity model so that critical-path regressions block deployment while lower-severity issues are queued for the next sprint.
• Close the loop into production. Use feature flags and staged rollouts to extend gate evaluation beyond staging. Canary deployments, blue-green switches, and automated rollback mechanisms let you treat production itself as a final quality gate with limited blast radius.
• Share signal across QA and SRE. Continuous reliability works best when QA owns pre-production quality gates and SRE owns the production feedback loop, but both teams share the same data and definitions of “acceptable.”

The underlying goal is straightforward: every code change that moves through your pipeline should leave behind measurable evidence that it improved—or at minimum did not degrade—the reliability of the system. Quality gates make that evidence actionable, and feedback loops make it continuous.

The Comment That Started the Debate

Not long ago, a reader left this comment on a “making code faster” series:

…this “making code faster” series is pretty useless for the average developer working on the usual application.

It’s a sentiment you’ll hear often in developer communities—that topics like performance internals, database engine design, or systems programming are too niche to matter to someone building a typical enterprise application. The argument is understandable on the surface, but it doesn’t hold up under scrutiny.

Who Is the “Average Developer,” Really?

The so-called average developer is often described as someone perpetually heads-down, coding another login page under a deadline, with no time to look up. This framing appears regularly in discussions about professional development expectations—including arguments that expecting developers to learn on their own time is somehow offensive or unrealistic.

That view is worth taking seriously. Life is busy. Many developers are balancing demanding jobs, families, and other responsibilities. No one is suggesting that you neglect your family to chase the latest technology trend.

But there is a real difference between balance and stagnation.

Why Breadth of Knowledge Pays Off

If you limit your learning strictly to what you need for next week’s sprint—the hottest JavaScript library, the design pattern your team just adopted—you are optimizing for short-term delivery at the cost of long-term capability. Here’s why that matters:

• Recognition of known patterns: When you encounter a tough problem, a broad knowledge base lets you say “I’ve seen this class of problem before” and reach for a better solution faster.
• Avoiding reinvented wheels: Understanding foundational concepts—memory allocation, concurrency, query planning, caching strategies—helps you avoid mistakes that have already been made and documented in the field.
• Genuine versatility: Technology stacks change. Developers who understand underlying principles adapt more easily than those who only know a specific framework’s API surface.
• Better judgment in design decisions: Performance characteristics, trade-offs in data modeling, or failure modes in distributed systems are relevant even in “average” applications once they reach any meaningful scale.

Cycling Through Versions Is Not the Same as Learning

There is a trap that many developers fall into: mistaking framework churn for genuine growth. Moving from one version of a web framework to the next—adopting each new release as it ships—can feel like continuous learning. It isn’t. If the underlying concepts are the same and you’re only tracking the API changes, you’re running on a treadmill.

Genuine learning means expanding into adjacent or foundational areas: how the runtime manages memory, how the database executes your queries, how the network stack behaves under load, how operating systems schedule threads. These topics may not pay off immediately. They will pay off eventually—often at the exact moment you need them most.

How Much Time Does It Actually Take?

The good news is that continuous improvement does not require heroic sacrifice. For most people, two to four hours per week is achievable without disrupting family life or work commitments. That translates to roughly 100–200 hours per year—enough time to work through a substantial book, complete a focused online course, or build a meaningful side project.

Practical ways to find that time:

• Treat one or two evenings per week as deliberate learning time, not passive screen time.
• Use commute time for technical podcasts, talks, or audiobooks where possible.
• Replace low-value browsing (social media, news cycling) with focused reading or practice.
• Pick topics that genuinely interest you—motivation sustains the habit better than obligation.

The Bottom Line

Valuing your career means investing in it consistently, not just when a new job forces you to. Advanced topics—performance engineering, systems design, compiler internals, distributed systems theory—are not the exclusive concern of people building database engines. They are the foundation that separates developers who can only execute someone else’s design from developers who can reason about problems from first principles.

That investment is not always easy. It is, however, worth making.

Originally sourced from a discussion on DZone.com. The views reflect a perspective on developer professional growth and continuous learning.

We call it DevOps but much of the time there’s a lot more discussion about the needs and concerns of developers than there is about other groups.

There’s a focus on improved and less isolated developer workflows. There are many discussions around collaboration, continuous integration and delivery, issue tracking, source code control, code review, IDEs, and xPaaS – and all the tools that enable those things. Changes in developer practices may come up – such as developers taking ownership of code and pulling pager duty.

We also talk about culture a great deal in the context of developers and DevOps. About touchy-feely topics like empathy, trust, learning, cooperation, and responsibility. It can all be a bit kumbaya.

What about the Ops in DevOps? Or, really, about the other constituencies who should be part of the DevOps process, workflow, and even culture? Indeed, DevSecOps is gaining some traction as a term. DevOps purists may chafe at “DevSecOps” given that security and other important practices are supposed to already be an integral part of routine DevOps workflows. But the reality is that security often gets more lip service than thoughtful and systematic integration.

But what’s really going on here is that we need to get away from thinking about Ops-as-usual (or Security-as-usual) in the DevOps context at all. This is really what Adrian Cockcroft was getting at with the NoOps term; he didn’t coin it but his post about NoOps while he was at Netflix kicked off something of an online kerfuffle. Netflix is something of a special case because they are so all-in on Amazon Web Services, but Adrian was getting at something that’s more broadly applicable. Namely that, in evolved or mature DevOps, a lot of what Ops does is put core services in place and get out of the way.

Ironically, this runs somewhat counter to the initial image of breaking down the wall between Dev and Ops. Yes, DevOps does involve greater transparency, collaboration, and so forth to break down siloed behaviors but there’s perhaps an even stronger element of putting infrastructure, processes, and tools in place so that Devs doesn’t need to interact with Ops as much while being (even more) effective. One of the analogies I like to use is that I don’t want to streamline my interactions with a bank teller. For routine and even not so routine transactions, I just want to use an ATM or even my smartphone.

It’s up to Ops to build and operate the infrastructure supporting those streamlined transactions. Provide core services through a modern container platform. Enable effective automated developer workflows. Mitigate risk and automate security. But largely stay behind the scenes. Of course, you still want to have good communication flows between developers and operations teams; you just want to make those communications unnecessary much of the time.

(At the same time, it’s important for Dev and Ops teams to understand how they can mutually benefit by using appropriate container management and other toolchains. There’s still too much of a tendency by both groups to think of something as an “ops tool” or a “dev tool.” But that’s a topic for another day.)

Modern container platform

A DevOps approach can be applied just about anywhere. But optimizing the process and optimizing cloud-native applications is best done on a modern platform. Take it as read that most IT is going to be brownfield and DevOps may even be a good bridge between existing systems, applications, and development processes and new ones. But here I’m focusing on what’s optimized for new apps and new approaches.

You need scale-out architectures to meet highly elastic service requirements. Application designs with significant scale-up components simply aren’t able to accommodate shifting capacity needs.

Everything is software-defined because software functions, such as network function virtualization and software-defined storage, are much more flexible than when the same functions are embedded in hardware.

The focus is on applications composed of loosely-coupled services because large monolithic applications can be fragile and can’t be updated quickly.

A modern container platform enables lightweight iterative software development and deployment in part because modern applications are often short-lived and require frequent refreshes and replacements.

As I wrote about in The State of Platform-as-a-Service 2016, a PaaS like Red Hat’s OpenShift has evolved to be this modern container platform, embracing and integrating docker-format containers, kubernetes for orchestration and using Red Hat CloudForms (based on the ManageIQ upstream project) for open source hybrid cloud management.

Automated developer workflows

When thinking about the toolchain associated with DevOps, a good place to start is the automation of the continuous integration/continuous delivery (CICD) pipeline. The end goal is to make automation pervasive and consistent using a common language across both classic and cloud-native IT. For example, Ansible allows configurations to be expressed as “playbooks” in a data format that can be read by both humans and machines. This makes them easy to audit with other programs, and easy for nondevelopers to read and understand.

A typical automated workflow begins with operations provisioning a containerized development environment, whether a PaaS or more customized environment. This provides an example of how a mature DevOps process separates operations and developer concerns; by providing developers with a dynamic self-service environment, operations can focus on deploying and running stable, scalable infrastructure while developers focus on writing code.

Automation then ensures that the application can be repeatedly deployed. Many different types of tools are integrated into the DevOps workflow at this point. For example:

• Code repositories, like Git
• Container development tools to convert code in a repository into a portable containerized image that includes any required dependencies
• Vagrant for creating and configuring lightweight, reproducible, and portable development environments¥IDEs like Eclipse
• CICD software like Jenkins

Mature DevOps systems may even push the code directly to production once it has passed automated testing. But this isn’t about removing Ops from its role of ensuring stable and robust production systems. Rather, it’s about automating the processes to ensure that deployed code meets set criteria without ops needing to be directly involved with each deployment.

Mitigate risk and automate security

Ops is also ultimately chartered with protecting the business. This doesn’t mean eliminating all risk – which can’t be done. But it does mean mitigating risk which is accomplished in part by managing the software supply chain and by automating away sources of manual error.

That’s not to say that security is purely an ops concern – hence the aforementioned DevSecOps term. Creating a mindset in which everyone is responsible for security is key as is the practice of building security into development processes. Security must change from a defensive to an offensive posture that is both automated and constant.

Among the practices to be followed in such a proactive environment are:

• Components built from source code using a secure, stable, reproducible build environment
• Careful selection, configuration, and security tracking of packages
• Automated analysis and enforcement of security practices
• Active participation in upstream and community involvement
• Thoroughly validated vulnerability management process

I think this quote from Gartner captures the required dynamic well: “Our goal as information security architects must be to automatically incorporate security controls without manual configuration throughout this cycle in a way that is as transparent as possible to DevOps teams and doesn’t impede DevOps agility, but fulfills our legal and regulatory compliance requirements as well as manages risk.”