Power Up Your Software Development Lifecycle with AI

Most software engineers are already using AI coding assistants and agents to enhance their workflow. While these can deliver benefits, the real opportunity lies in using AI across the entire Software Development Lifecycle (SDLC). In this article, we'll look at how AI agents can be used throughout the process, to deliver better software faster.

It’s all about Context

Every SDLC process includes phases such as: Planning & Analysis, Design, Development, Testing, Deployment, Support & Maintenance. Each phase of the process is dependent on contextual information being available, about the business domain, the users, the technologies and the software being developed. This information is generated within and moves through the process, in written and verbal formats.

The key to maximising the power of AI is to make all of this context readily available to AI agents. It can be in various text based formats including: documents, web pages, markdown files, system APIs (JSON) etc.

If the context is written with AI in mind, it effectively becomes prompts and instructions for AI. The higher quality the context, the higher quality the AI’s output.

The high level steps to make this happen are:

• Identify existing sources of contextual information
  • review and fill any gaps in those sources, such as implicit company knowledge and conventions
• Provide AI agents with access to the context
• Upskill the team, to write and reference the appropriate context during each phase of the process

Model Context Protocol

Model Context Protocol (MCP) is a powerful open protocol that acts like universal plumbing between existing enterprise systems and AI agents, avoiding custom API integrations.

A MCP server is a simple adapter service that (typically) runs in the context of a user, on their laptop and provides consistent interfaces that AI agents can interact with. These interfaces provide the means to request contextual information (resources) and to execute tools that will perform actions.

Many of the concepts discussed below can be achieved by manually providing contextual information to an AI chatbot or agent, however MCP servers provide a way to automate the flow, avoiding a lot of copying and pasting. For example, an AI agent can automatically retrieve the details of a task from a work management system when prompted with a ticket number.

MCP servers are available (or becoming available) for most enterprise systems that are used within a SDLC process. If an existing MCP server does not exist, any experienced software engineer can create a MCP server to wrap an existing API.

A note of caution, some MCP servers expose too much functionality, giving the AI agent excessive agency or access to sensitive data. This is an example of the risk described in the OWASP Top 10 for LLMs: LLM08: Excessive Agency. Either use a proxy or implement your own MCP server to only expose the capabilities that you really need.

AI Agents Across SDLC Phases

Now that we understand the importance of context and we have a means of providing it to AI agents, let’s look at how to power up each phase of the SDLC.

Note: example prompts are in quotes below.

Planning & Analysis Phase

A product team can utilise AI in multiple ways during the Planning & Analysis phase.

• Automated research: use a deep research agent to perform industry analysis or ideation on product development.
  • “create a report describing how industry [...] is evolving it's use of [...] products and services
  • “explore ways to enhance the value delivered by the [...] product/service
• Rapid prototyping: use an AI prototyping platform to create a UI prototype from an idea (AKA vibe-coding).
  • “create a simple web page using HTML, to demonstrate how users can manage/process [...] records/data”
• Reverse engineering: reverse engineer an existing codebase, to document previously undocumented requirements from legacy systems. This can be a collaboration between a product owner and a software engineer.
• Requirements documentation: draft a Product Requirements Document (PRD) from an initial idea.
  • “Create a PRD based on the [...] template. The PRD is for a new application feature that [...]"
• Work breakdown: draft Epics, Stories & Tasks from the PRD.
  • “Create a work breakdown based on the [...] PRD. Create an Epic, followed by stories and related tasks, including detailed steps and acceptance criteria."
• Draft content: draft product documentation, presentations, marketing copy etc.

These artefacts are used as context in the subsequent phases.

Design Phase

UX Design

• Automated UX Design: generate UX designs from prompts, modern UX design tools can generate wireframes, user flows, and clickable prototypes from descriptive prompts.
  • "Create a dashboard interface for an analytics web app. Include a sidebar for navigation and filters, and main area for charts and metrics. Use material design."

Software Architecture

• Software design: draft a software design document from a PRD, based on defined architecture standards and conventions.
  • "Based on the [...] PRD, create a high-level software architecture document. Follow our company's architecture standards and non-functional requirements in the [...] file. Describe system components, data flows, and technology choices."
• Create architecture diagrams: generate diagrams to communicate the software architecture clearly.
  • "Generate component and data flow diagrams (Mermaid diagrams in markdown) to represent the [...] system defined in the software architecture document."
• NFR analysis: analyse a software design to review Non-Functional Requirements (NFRs) and identify potential gaps.
  • "Review the [...] software design and highlight any missing aspects or unclear NFRs. Suggest additions based on common software quality attributes."

Development Phase

Software engineers can utilise MCP servers from within their coding editors to retrieve the contextual information generated in the previous phases, from UX design systems, software architecture models, wikis and task management tools.

Instruction files (Markdown files) should also be created in code repositories to define coding standards, project conventions, business domain etc.

• Software Architecture: the ArchiScribe MCP Server can be used to provide context about the software architecture.
  • "#archiscribe Get the details from the future state [...] architecture view, analyse the gaps from the current state, and create a development plan"
• Task Assignment: assign a task to an agent, the task should have a detailed description and references to related context sources such as PRD, UX designs and software design documents.
  • "Get the details of the task described in ticket: [...], review the current project and implement the required changes."
• Defect Fix Automation: reference a defect ticket number and ask the agent to implement the required changes to the code.
  • "Get the details of the defect described in ticket: [...], review the current project and implement the required fix. Include unit tests."
• Learning a Codebase: new team members can use an AI agent to learn about an existing codebase.
  • "I'm a new developer on the team, explain how this business logic works"

Testing Phase

• Automated Test Creation: create (unit, integration, performance, security) tests by analysing the existing code repository and defect patterns. This is an effective way to increase test coverage and catch future defects.
  • "Analyse the code in the current project and generate unit tests, cover typical usage scenarios and edge cases. Create mock objects where needed."
  • "Write tests to cover the defects in the following tickets: [..., ...]"
• Test Data Generation: create synthetic data for testing.
  • "Create 1000 realistic test records for the [...] data model, output as SQL INSERT statements"

A MCP server can also be used to interact with test case management systems, to identify gaps in test coverage etc.

Deployment Phase

• CI/CD Pipeline Generation: create and execute CI/CD pipelines, including performance analysis, anomaly detection and cloud cost optimisation.
  • "Create a CI/CD pipeline for the [...] application, include steps for build, test, security scan, and deployment to [staging/production] environments."
  • "Review the current cloud infrastructure and identify opportunities to reduce costs."
• Script Generation: generate deployment or rollback scripts based on the latest system changes.
  • "Generate a deployment script for the latest release, including database migrations and service restarts. Also generate a rollback script in case the deployment fails."

Support & Maintenance Phase

• Root Cause Analysis: write up Root Cause Analysis documents based on tickets and logs.
  • "Generate a Root Cause Analysis report based on the following incident ticket and log files. Include summary, impact, timeline, root cause, and remediation steps."
• Improvement Analysis: historical support tickets can be analysed to suggest fixes and improvements.
  • "Analyze the support tickets from the past 6 months. Identify recurring issues or common pain points, and suggest improvements to reduce ticket volume."

Team Upskilling & Organisational Change

The objective of the examples above is to augment your team, to make their lives easier, it's not about replacing people with AI. The output from these examples will not be perfect, there will be inaccuracies that require human review and correction. Even with these imperfections, it’s much better than starting from scratch.

Some training and up-skilling will be required to get people onboard to adopt these new ways of working. See Driving AI Adoption, From Resistance to Results for more information on change management.

AI Governance

Don’t forget about AI governance! Your SDLC process is handling your company's intellectual property, therefore robust governance is vital. More details on AI governance here: AI Governance & the Journey To ISO 42001

Conclusion

AI agents are rapidly changing how software is developed and delivered. Using AI at specific touchpoints of the SDLC is a practical way to start, however, a more holistic approach will deliver even more positive results.

Start by introducing changes to the Planning & Analysis phase, then progressively work through each subsequent phase. By ensuring that the context generated is high-quality, relevant, and reusable, it will create a compounding effect: each phase becomes more efficient and better aligned with the next.

The result? A cohesive, AI-powered SDLC that empowers teams and accelerates the delivery of high quality software.