TOPIC: ASANA
From planning to production: Selected aspects of modern software delivery
9th March 2026
Software delivery has never been more interlinked across strategy, planning and operations. Agile practices are adapting to hybrid work, AI is reshaping how teams plan and execute, and cloud platforms have become the default substrate for everything from build pipelines to runtime security. What follows traces a practical route through that terrain, drawing together current guidance, tools and community efforts so teams can make informed choices without having to assemble the big picture for themselves.
Work Management: Asana and Jira
Planning and coordination remain the foundation of any delivery effort, and the market still gravitates to two names for day-to-day project management: Asana and Jira. Each can bring order to multi-team projects and distributed work, yet they approach the job from very different histories.
With a history rooted in large DevOps teams and issue tracking, Jira carries that lineage into its Scrum and Kanban options, backlogs, sprints and a reporting catalogue that leans into metrics such as time in status, resolution percentages and created-versus-resolved trends. Built as a more general project manager from the outset, Asana shows its intent in the way users move from a decluttered home screen to “My Tasks”, switch among Kanban, Gantt and Calendar views using tabs, and add custom fields or rules from within the view rather than navigating to separate screens. The two now look similar at a glance, but their structure and presentation differ, and that influences how quickly a team settles into a rhythm.
Dashboards and Reporting
Those differences widen when examining dashboards and reporting. Jira allows users to create multiple dashboards and fill them with a large range of gadgets, including assigned issues, average time in status, bubble charts, heat maps and slideshows. The designs are sparse yet flexible, and administrators on company-managed accounts can add custom reporting, while the Atlassian Marketplace offers hundreds of additional reporting integrations.
By contrast, the home dashboard in Asana is intentionally pared back, with reports placed in their own section to keep personal task management separate from project or portfolio-level tracking. Its native reporting is broader and more polished out of the box, with pre-built views for progress, work health and resourcing, together with custom report creation that does not require admin-level access.
Interoperability
How well each tool connects to other systems also sets expectations. Jira, as part of Atlassian's suite, has a bustling marketplace with over a thousand apps for its cloud product, covering project management, IT service management, reporting and more. Asana's store is smaller, with under 400 apps at the time of writing, though it continues to grow and offers breadth across staples such as Slack, Teams and Adobe Creative Cloud, as well as a strong showing for IT and developer use cases.
Both tools connect to Zapier, which has also published a detailed comparison of the two platforms, opening pathways to thousands of further automations, such as creating Jira issues from Typeform submissions or making Asana tasks from Airtable records without writing integration code. In practice, many teams will get what they need natively and then extend in targeted ways, whether through marketplace add-ons or workflow automations.
Plans and AI
Plans and AI are where the most significant recent movement has occurred. On the Asana side, a free Personal tier leads into paid Starter and Advanced plans followed by Enterprise, with AI tools (branded "Asana Intelligence") included across paid plans. Those features help prioritise work, automate repetitive steps, suggest smart workflows and summarise discussions to reduce time spent on status communication.
Over at Jira, the structure runs from a free tier for small teams through Standard, Premium and Enterprise plans. "Atlassian Intelligence" focuses on generative support in the issue editor, AI summaries and AI-assisted sprint planning, adding predictive insights to help with resource allocation and automation. It is worth noting that Jira's entry-level paid plan appears cheaper on paper, but real-world total cost of ownership often rises once Marketplace apps, Confluence licences and security add-ons are factored in.
Choosing between the two typically comes down to need. If you want a task manager built for general use with crisp reporting and strong collaboration features, Asana presents itself clearly. If your roadmap lives and breathes Agile sprints, backlogs and issue workflows, and you need deep extensibility across a suite, Jira remains a natural fit.
Scrum: Back to Basics
Method matters as much as tooling. Scrum remains the most widely adopted Agile framework, and it is worth revisiting its essentials when translating plans into delivery. The DevOps Institute tracks the human side of this evolution, noting that skills, learning and collaboration are as central to DevOps success as the toolchain itself. A Scrum Team is cross-functional and self-organising, combining the Product Owner's focus on prioritising a transparent, value-ordered Product Backlog with a Development Team that turns backlog items into a potentially shippable increment every Sprint.
The Scrum Master keeps the framework alive, removes impediments, and coaches both the team and the wider organisation. Sprints run for no longer than four weeks and bundle Sprint Planning, Daily Scrums, a Sprint Review and a Retrospective, with online whiteboards increasingly used to run those ceremonies effectively across distributed and hybrid teams. The Sprint Goal provides a unifying target, and the Sprint Backlog breaks selected Product Backlog items into tasks and steps owned by the team.
Scrum Versus Waterfall
That cadence stands in deliberate contrast to classic waterfall approaches, where specification, design, implementation, testing and deployment proceed in long phases with significant hand-offs between each. Scrum replaces upfront specifications with user stories and collaborative refinement using the "three Cs" of Card, Conversation and Confirmation, so requirements can evolve alongside market needs. It places self-organisation ahead of management directives in deciding how work is done within a Sprint, and it raises transparency by making progress and problems visible every day rather than at phase gates.
Teams feel the shift when they commit to delivering a working increment each Sprint rather than aiming for a distant release, and when they see the cost of change flatten because feedback arrives through Reviews and Retrospectives rather than months after decisions have been made.
The State of Agile
Richer context for these shifts appears in longitudinal views of industry practice. The 18th State of Agile Report, published by Digital.ai in late 2025, observes that Agile is adapting rather than fading, with adoption remaining widespread while many organisations rebuild from the ground up to focus on measurable outcomes. The report, drawing on responses from approximately 350 practitioners, notes that AI and automation are accelerating change while introducing fresh expectations around data quality, decision-making and governance, and it emphasises that outcomes have become the currency connecting strategy, planning and execution.
That aligns with the Agile Alliance's ongoing work to re-examine Agile's core values for enterprise settings, as well as with the joint Manifesto for Enterprise Agility initiative with PMI{:target="_blank"}, which argues for adaptability as a strategic advantage rather than a team-level method choice. Significantly, the 18th report found that only 13% of respondents say Agile is deeply embedded across their business, and that only 15% of business leaders participate meaningfully in Agile practices, suggesting that leadership alignment remains one of the most persistent blockers to realising the framework's full potential.
Continuous Delivery and CI/CD Tooling
Getting from plan to production relies on engineering foundations that have matured alongside Agile. Continuous Delivery reframes deployment as a safe, rapid and sustainable capability by keeping code in a deployable state and eliminating the traditional post-"dev complete" phases of integration, testing and hardening. By building deployment pipelines that automate build, environment provisioning and regression testing, teams reduce risk, shorten lead time and can redirect human effort towards exploratory, usability, performance and security testing throughout delivery, not just at the end.
The results can be counterintuitive. High-performing teams deploy more frequently and more reliably, even in regulated settings because painful activities are made routine and small batches make feedback economical.
CI/CD in Practice
Contemporary CI/CD tools express that philosophy in developer-centred ways. Travis CI can often be described in minutes using minimal YAML configuration, specifying runtimes, caching dependencies, parallelising jobs and running tests across multiple language versions. Azure Pipelines, GitHub Actions and Azure DevOps provide similar capabilities at broader scale, with managed runners, gated releases, integrated artefact feeds, security scanning and policy controls that matter in larger enterprises.
The emphasis across these platforms is on speed to first pipeline, consistency across environments and adding guardrails such as signed artefacts, scoped credentials and secret management, so that velocity does not undercut safety.
Cloud Native Architecture
Architecture and platform choices amplify or constrain delivery flow. The cloud native ecosystem, curated by the Cloud Native Computing Foundation (CNCF) under the Linux Foundation, has become the common bedrock for organisations standardising on Kubernetes, service meshes and observability stacks. Hosting more than 200 projects across sandbox, incubating and graduated maturity levels, it spans everything from container orchestration to policy and tracing, and brings together vendors, end users and maintainers at events such as KubeCon + CloudNativeCon.
Sitting higher up the stack, Knative is a recent CNCF graduate that provides building blocks for HTTP-first, event-driven serverless workloads on Kubernetes. It unifies serving and eventing, so teams can scale to zero on demand while routing asynchronous events with the same fluency as web requests, and was created at Google before joining the CNCF as an incubating project and subsequently reaching graduation status. For teams that need to manage the underlying cluster infrastructure declaratively, Cluster API provides a Kubernetes-native way to provision, upgrade and operate clusters across cloud and on-premises environments, bringing the same declarative model used for application workloads to the infrastructure layer itself.
APIs and Developer Ecosystems
API-driven integration is part of the cloud native picture rather than an afterthought. The API Landscape compiled by Apidays shows the sheer diversity of stakeholders and tools across the programmable economy, from design and testing to gateways, security and orchestration. Developer ecosystems such as Cisco DevNet bring this to ground level by offering documentation, labs, sample code and sandboxes across networking, security and collaboration products, encouraging infrastructure as code with tools like Terraform and Ansible.
Version control and collaboration sit at the centre of modern delivery, and GitHub's documentation, spanning everything from Codespaces to REST and GraphQL APIs, reflects that centrality. The breadth of what is available through a single platform, from repository management to CI/CD workflows and AI-assisted coding, illustrates how much of the delivery stack can now be coordinated in one place.
Security: An End-to-End Discipline
Security threads through every layer and is increasingly treated as an end-to-end discipline rather than a late-stage gate. The Open-Source Security Foundation (OpenSSF) coordinates community efforts to secure open-source software for the public good, spanning working groups on AI and machine learning security, supply chain integrity and vulnerability disclosure, and offering guides, courses and annual reviews.
On the cloud side, a Cloud-Native Application Protection Platform (CNAPP) consolidates capabilities to protect applications across multi-cloud estates. Core components typically include Cloud Infrastructure Entitlement Management (to rein in excessive permissions), Kubernetes Security Posture Management (to maintain container orchestration best practices and flag misconfigurations), Data Security Posture Management (to classify and monitor sensitive data) and Cloud Detection and Response (to automate threat response and connect to security orchestration platforms).
Increasingly, AI-driven Security Posture Management sits across these layers to spot anomalies and predict risks from historical patterns, though this brings its own challenges around false positives and model bias that require careful adoption planning. Vendors such as Check Point offer CNAPP products including CloudGuard with unified management and compliance automation. While such examples illustrate what is available commercially, it is the architecture and functions described above that define the category itself.
Site Reliability Engineering
Reliability is not left to chance in well-run organisations. Site Reliability Engineering (SRE), pioneered and documented by Google, treats operations as a software problem and asks SRE's to protect, provide for and progress the systems that underpin user-facing services. The remit ranges from disk I/O considerations to continental-scale capacity planning, with a constant focus on availability, latency, performance and efficiency.
Error budgets, automation, toil reduction and blameless post-mortems become part of the vocabulary for teams that want to move fast without eroding trust. The approach complements Continuous Delivery by turning operational quality into something measurable and improvable, rather than a set of aspirations.
Code Quality, Testing and Documentation
For all the automation and platform power now available, the basics of code quality and testing still count. The Twelve-Factor App methodology remains relevant in encouraging declarative automation, clean contracts with the operating system, strict separation of build and run stages, stateless processes, externalised configuration, dev-prod parity and treating logs as event streams rather than files to be managed. It was first presented by developers at Heroku and continues to inform how teams design applications for cloud environments.
Documentation practices have also evolved, from literate programming's argument that source should be written as human-readable text with code woven through, to modern API documentation standards that keep codebases easier to change and onboard. General-purpose resources such as the long-running Software QA and Testing FAQ remind teams that verification and validation are distinct activities, that a spectrum of testing types is available and that common delivery problems have known countermeasures when documentation, estimation and test design are taken seriously.
AI in Software Delivery
No survey of modern software delivery can sidestep artificial intelligence. Adoption is now near-universal: the 2025 DORA State of AI-Assisted Software Development report, drawing on responses from almost 5,000 technology professionals worldwide, found that around 90% of developers now use AI as part of their daily work, with the median respondent spending roughly two hours per day interacting with AI tools. More than 80% report feeling more productive as a result. The picture is not straightforward, however. The same research found that AI adoption correlates with higher delivery instability, more change failures and longer cycle times for resolving issues because the acceleration AI brings upstream tends to expose bottlenecks in testing, code review and quality assurance that were previously hidden.
The report's central conclusion is that AI functions as an amplifier rather than a remedy. Strong teams with solid engineering foundations use it to accelerate further, while teams carrying technical debt or process dysfunction find those problems magnified rather than resolved. This means the strategic question is not simply which AI tools to adopt, but whether the underlying platform, workflow and culture are ready to benefit from them. The DORA AI Capabilities Model, published as a companion guide, identifies seven foundational practices that consistently improve AI outcomes, including a clear organisational stance on AI use, healthy data ecosystems, working in small batches and a user-centric focus. Teams without that last ingredient, the report warns, can actually see performance worsen after adopting AI.
At the tooling level, the landscape has moved quickly. Coding assistants such as GitHub Copilot have gone from novelty to standard practice in many engineering organisations, with newer entrants including Cursor, Windsurf and agentic tools like Claude Code pushing the category further. The shift from "copilot" to "agent" is significant: where earlier tools suggested completions as a developer typed, agentic systems accept a goal and execute a multistep plan to reach it, handling scaffolding, test generation, documentation and deployment checks with far less human intervention. That brings real efficiency gains and also new governance questions around traceability, code provenance and the trust that teams place in AI-generated output. Around 30% of DORA respondents reported little or no trust in code produced by AI, a figure that points to where the next wave of tooling and practice will need to focus.
Putting It Together
Translating all of this into practice looks different in every organisation, yet certain patterns recur. Teams choose a work management tool that matches the shape of their portfolio and the degree of Agile structure they need, whether that is Asana's lighter-weight task management with strong reporting or Jira's DevOps-aligned issue and sprint workflows with deep extensibility, then align on a Scrum-like cadence if iteration and feedback are priorities, or adopt hybrid approaches that sustain visibility while staying compatible with regulatory or vendor constraints.
Build, test and release are automated early so that pipelines, not people, become the route to production, and cloud native platforms keep environments reproducible and scalable across teams and geographies. Instrumentation ensures that security posture, reliability and cost are visible and managed continuously rather than episodically, and deliberate investment in engineering foundations, small batches, fast feedback and strong platform quality, creates the conditions that the evidence now shows are prerequisites for AI to deliver on its promise rather than amplify existing dysfunction.
If anything remains uncertain, it is often the sequencing rather than the destination. Few organisations can refit planning tools, delivery pipelines, platform architecture and security models all at once, and there is no definitive order that works everywhere. Starting where friction is highest and then iterating tends to be more durable than a one-shot transformation, and most of the resources cited here assume that change will be continuous rather than staged. Agile communities, cloud native foundations and security collaboratives exist because no single team has all the answers, and that may be the most practical lesson of all.
An Overview of MCP Servers in Visual Studio Code
29th August 2025
Agent mode in Visual Studio Code now supports an expanding ecosystem of Model Context Protocol servers that equip the editor’s built-in assistant with practical tools. By installing these servers, an agent can connect to databases, invoke APIs and perform automated or specialised operations without leaving the development environment. The result is a more capable workspace where routine tasks are streamlined, and complex ones are broken into more manageable steps. The catalogue spans developer tooling, productivity services, data and analytics, business platforms, and cloud or infrastructure management. If something you rely on is not yet present, there is a route to suggest further additions. Guidance on using MCP tools in agent mode is available in the documentation, and the Command Palette, opened with Ctrl+Shift+P, remains the entry point for many workflows.
The servers in the developer tools category concentrate on everyday software tasks. GitHub integration brings repositories, issues and pull requests into reach through a secure API, so that code review and project coordination can continue without switching context. For teams who use design files as a source of truth, Figma support extracts UI content and can generate code from designs, with the note that using the latest desktop app version is required for full functionality. Browser automation is covered by Playwright from Microsoft, which drives tests and data collection using accessibility trees to interact with the page, a technique that often results in more resilient scripts. The attention to quality and reliability continues with Sentry, where an agent can retrieve and analyse application errors or performance issues directly from Sentry projects to speed up triage and resolution.
The breadth of developer capability extends to machine learning and code understanding. Hugging Face integration provides access to models, datasets and Spaces on the Hugging Face Hub, which is useful for prototyping, evaluation or integrating inference into tools. For source exploration beyond a single repository, DeepWiki by Kevin Kern offers querying and information extraction from GitHub repositories indexed on that service. Converting documents is handled by MarkItDown from Microsoft, which takes common files like PDF, Word, Excel, images or audio and outputs Markdown, unifying content for notes, documentation or review. Finding accurate technical guidance is eased by Microsoft Docs, a Microsoft-provided server that searches Microsoft Learn, Azure documentation and other official technical resources. Complementing this is Context7 from Upstash, which returns up-to-date, version-specific documentation and code examples from any library or framework, an approach that addresses the common problem of answers drifting out of date as software evolves.
Visual assets and code health have their own role. ImageSorcery by Sunrise Apps performs local image processing tasks, including object detection, OCR, editing and other transformations, a capability that supports anything from quick asset tweaks to automated checks in a content pipeline. Codacy completes the developer picture with comprehensive code quality and security analysis. It covers static application security testing, secrets detection, dependency scanning, infrastructure as code security and automated code review, which helps teams maintain standards while moving quickly.
Productivity services focus on planning, tracking and knowledge capture. Notion’s server allows viewing, searching, creating and updating pages and databases, meaning an agent can assemble notes or checklists as it progresses. Linear integration brings the ability to create, update and track issues in Linear’s project management platform, reflecting a growing preference for lightweight, developer-centred planning. Asana support provides task and project management together with comments, allowing multi-team coordination. Atlassian’s server connects to Jira and Confluence for issue tracking and documentation, which suits organisations that rely on established workflows for governance and audit trails. Monday.com adds another project management option, with management of boards, items, users, teams and workspace operations. These capabilities sit alongside automation from Zapier, which can create workflows and execute tasks across more than 30,000 connected apps to remove repetitive steps and bind systems together when native integrations are limited.
Two Model Context Protocol utilities add cognitive structure to how the agent works. Sequential Thinking helps break down complex tasks into manageable steps with transparent tracking, so progress is visible and revisable. Memory provides long-lived context across sessions, allowing an agent to store and retrieve relevant information rather than relying on a single interaction. Together, they address the practicalities of working on multi-stage tasks where recalling decisions, constraints or partial results is as important as executing the next action. Used with the productivity servers, these tools underpin a systematic approach to projects that span hours or days.
The data and analytics group is comprehensive, stretching from lightweight local analysis to cloud-scale services. DuckDB by Kentaro Tanaka enables querying and analysis of DuckDB databases both locally and in the cloud, which suits ad hoc exploration as well as embedded analytics in applications. Neon by neondatabase labs provides access to Postgres with the notable addition of natural language operations for managing and querying databases, which lowers the barrier to occasional administrative tasks. Prisma Postgres from Prisma brings schema management, query execution, migrations and data modelling to the agent, supporting teams who already use Prisma’s ORM in their applications. MongoDB integration supports database operations and management, with the ability to execute queries, manage collections, build aggregation pipelines and perform document operations, allowing front-end and back-end tasks to be coordinated through a single interface.
Observability and product insight are also represented. PostHog offers analytics access for creating annotations and retrieving product usage insights so that changes can be correlated with user behaviour. Microsoft Clarity provides analytics data including heatmaps, session recordings and other user behaviour insights that complement quantitative metrics and highlight usability issues. Web data collection has two strong options. Apify connects the agent with Apify’s Actor ecosystem to extract data from websites and automate broader workflows built on that platform. Firecrawl by Mendable focuses on extracting data from websites using web scraping, crawling and search with structured data extraction, a combination that suits building datasets or feeding search indexes. These tools bridge real-world usage and the development cycle, keeping decision-making grounded in how software is experienced.
The business services category addresses payments, customer engagement and web presence. Stripe integration allows the creation of customers, management of subscriptions and generation of payment links through Stripe APIs, which is often enough to pilot monetisation or administer accounts. PayPal provides the ability to create invoices, process payments and access transaction data, ensuring another widely used channel can be managed without bespoke scripts. Square rounds out payment options with facilities to process payments and manage customers across its API ecosystem. Intercom support brings access to customer conversations and support tickets for data analysis, allowing an agent to summarise themes, surface follow-ups or route issues to the right place. For building and running sites, Wix integration helps with creating and managing sites that include e-commerce, bookings and payment features, while Webflow enables creating and managing websites, collections and content through Webflow’s APIs. Together, these options cover a spectrum of online business needs, from storefronts to content-led marketing.
Cloud and infrastructure operations are often the backbone of modern projects, and the MCP catalogue reflects this. Convex provides access to backend databases and functions for real-time data operations, making it possible to work with stateful server logic directly from agent mode. Azure integration supports management of Azure resources, database queries and access to Azure services so that provisioning, configuration and diagnostics can be performed in context. Azure DevOps extends this to project and release processes with management of projects, work items, repositories, builds, releases and test plans, providing an end-to-end view for teams invested in Microsoft’s tooling. Terraform from HashiCorp introduces infrastructure as code management, including plan, apply and destroy operations, state management and resource inspection. This combination makes it feasible to review and adjust infrastructure, coordinate deployments and correlate changes with code or issue history without switching tools.
These servers are designed to be installed like other VS Code components, visible from the MCP section and accessible in agent mode once configured. Many entries provide a direct route to installation, so setup friction is limited. Some include specific requirements, such as Figma’s need for the latest desktop application, and all operate within the Model Context Protocol so that the agent can call tools predictably. The documentation explains usage patterns for each category, from parameterising database queries to invoking external APIs, and clarifies how capabilities appear inside agent conversations. This is useful for understanding the scope of what an agent can do, as well as for setting boundaries in shared environments.
In day-to-day use, the value comes from combining servers to match a workflow. A developer investigating a production incident might consult Sentry for errors, query Microsoft Docs for guidance, pull related issues from GitHub and draft changes to documentation with MarkItDown after analysing logs held in DuckDB. A product manager could retrieve usage insights from PostHog, review session recordings in Microsoft Clarity, create follow-up tasks in Linear and brief customer support by summarising Intercom conversations, all while keeping a running Memory of key decisions. A data practitioner might gather inputs from Firecrawl or Apify, store intermediates in MongoDB, perform local analysis in DuckDB and publish a report to Notion, building a repeatable chain with Zapier where steps can be automated. In infrastructure scenarios, Terraform changes can be planned and applied while Azure resources are inspected, with release coordination handled through Azure DevOps and updates documented in Confluence via the Atlassian server.
Security and quality concerns are woven through these flows. Codacy can evaluate code for vulnerabilities or antipatterns as changes are proposed, surfacing SAST findings, secrets detection problems or dependency risks before they progress. Stripe, PayPal and Square centralise payment operations to a few well-audited APIs rather than bespoke integrations, which reduces surface area and simplifies auditing. For content and data ingestion, ImageSorcery ensures that image transformations occur locally and MarkItDown produces traceable Markdown outputs from disparate file types, keeping artefacts consistent for reviews or archives. Sequential Thinking helps structure longer tasks, and Memory preserves context so that actions are explainable after the fact, which is helpful for compliance as well as everyday collaboration.
Discoverability and learning resources sit close to the tools themselves. The Visual Studio Code website’s navigation surfaces areas such as Docs, Updates, Blog, API, Extensions, MCP, FAQ and Dev Days, while the Download path remains clear for new installations. The MCP area groups servers by capability and links to documentation that explains how agent mode calls each tool. Outside the product, the project’s presence on GitHub provides a route to raise issues or follow changes. Community activity continues on channels including X, LinkedIn, Bluesky and Reddit, and there are broadcast updates through the VS Code Insiders Podcast, TikTok and YouTube. These outlets provide context for new server additions, changes to the protocol and examples of how teams are putting the pieces together, which can be as useful as the tools themselves when establishing good practices.
It is worth noting that the catalogue is curated but open to expansion. If there is an MCP server that you expect to see, there is a path to suggest it, so gaps can be addressed over time. This flows from the protocol’s design, which encourages clean interfaces to external systems, and from the way agent mode surfaces capabilities. The cumulative effect is that the assistant inside VS Code becomes a practical co-worker that can search documentation, change infrastructure, file issues, analyse data, process payments or summarise customer conversations, all using the same set of controls and the same context. The common protocol keeps these interactions predictable, so adding a new server feels familiar even when the underlying service is new.
As the ecosystem grows, the connection between development work and operations becomes tighter, and the assistant’s job is less about answering questions in isolation than orchestrating tools on the developer’s behalf. The MCP servers outlined here provide a foundation for that shift. They encapsulate the services that many teams already rely on and present them inside agent mode so that work can continue where the code lives. For those getting started, the documentation explains how to enable the tools, the Command Palette offers quick access, and the community channels provide a steady stream of examples and updates. The result is a VS Code experience that is better equipped for modern workflows, with MCP servers supplying the functionality that turns agent mode into a practical extension of everyday work.