AI-assisted engineering in era of GenAI

In today’s fast-paced, highly competitive tech environment, building exceptional products has become increasingly challenging. Every phase of product development ranging from initial ideation to final launch requires significant investment of time, energy, and resources. Despite the best efforts of engineers and teams, there is often a feeling of constraint, limited by the physical boundaries of human capacity and productivity. 

Creating software products involves juggling multiple tasks, from writing and reviewing code to creating wireframes and drafting requirements. The question arises, what if there was a way to enhance their efficiency and overcome some of the barriers that limit their abilities? 

This is where Generative AI (GenAI) becomes a game-changer, enabling teams to achieve significant productivity gains by integrating AI tools into engineering activities. These tools help streamline workflows, automate repetitive tasks, and allow teams to concentrate on high-value work, ultimately contributing to the creation of superior products.

Overcoming human limitations in product development 

Developing great products is undoubtedly a challenging and resource-intensive process. Product developers often have ideas that could improve their products, yet unable to execute due to limited time and resources available. The advent of GenAI offers a way to maximise productivity and enhance performance therefore overcome many of these constraints. 

By incorporating AI tools into daily engineering practices, development teams can become more efficient and accomplish more in less time. GenAI is not just about enhancing productivity by getting more work done in less time, it’s also about empowering engineers to tackle complex problems with more insight, creativity, and precision. This is perceived as enhancing cognitive capabilities beyond human limitations. 

The role of GenAI in engineering 

When integrating GenAI into engineering workflows, it’s essential to understand how different AI tools can be used to optimise various aspects of product development. These tools can be broadly categorised into three types: Copilots, Expert Advisors, and Autonomous Agents.

Copilots: Enhancing developer productivity 

GenAI copilots such as GitHub Copilot and Cursor AI serve as virtual pair programmers, assisting engineers with real-time code suggestions, completions, and bug fixes to enhance overall efficiency. By reducing the time spent on routine tasks and offering valuable recommendations and reusable code snippets, these tools help accelerate development workflows. Seamlessly integrating into development environments, they have been shown to improve engineering productivity by 15 to 40%, enabling developers to shift their focus from repetitive work to solving higher-order, complex problems.

Expert advisors: Leveraging AI for smarter decision-making

 AI-driven expert advisors such as ChatGPT, Claude, and Gemini function as intelligent assistants that support engineers in solving complex problems. Rather than spending hours researching solutions, engineers can rely on these tools for best practices, debugging assistance, and optimisation suggestions. Engaging in natural dialogue with these AI systems enables engineers to gain quick, actionable insights that elevate decision-making and enhance code quality. Acting as an extension of an engineer’s knowledge base, these tools offer real-time expertise precisely when it’s needed most.

Autonomous agents: Automating tasks for greater efficiency

 Autonomous agents represent one of the most powerful applications of GenAI, with the ability to perform entire engineering tasks independently and significantly reduce the need for manual intervention. These tools can take over repetitive activities such as code reviews, documentation, and performance testing, freeing engineers to focus on more strategic and creative aspects of product development. In addition to task automation, autonomous agents can analyse large volumes of data, identify bottlenecks, and ensure that system architecture aligns with key fitness goals such as performance and scalability. Delegating specific responsibilities to these agents enables teams to streamline workflows and operate more efficiently.

Scaling ambitions with autonomous agents 

The potential of autonomous agents extends well beyond boosting productivity, they can help scale the ambitions of development teams, particularly smaller ones. Startups and lean teams often face limitations in resources, which can, in turn, constrain their aspirations. The introduction of autonomous agents helps these teams overcome such barriers, enabling outcomes that might otherwise be out of reach due to team size or capacity. For example, a small development team with just a few engineers can harness the capabilities of these agents to increase output and deliver high-quality products more efficiently. Acting as virtual teammates, autonomous agents enable teams to achieve more and pursue larger, more ambitious projects.

Practical applications of GenAI in engineering 

At 99x, we have embraced GenAI tools across a wide range of engineering activities, using them to optimise workflows, enhance code quality, and minimise time spent on repetitive tasks. The integration of these tools has not only improved efficiency but also elevated the overall development experience. Below are some examples of how GenAI has been woven into our processes: 

• Code reviews: Autonomous agents can handle code reviews by automatically identifying potential issues, suggesting improvements, and ensuring that best practices are followed. 
• Architectural fitness functions: These functions assess whether the architecture of a product meets the necessary standards for performance, scalability, and reliability. GenAI tools help ensure that these goals are met by providing continuous feedback. 

Examples of fitness goals that can be enforced using agents include observability and resilience. Observability agents review system artefacts during development to ensure the code adheres to defined observability standards. Likewise, resilience agents detect potential issues such as improper error handling that could compromise system stability. By identifying and addressing these challenges early in the development process, agents help ensure that systems meet expected goals well before deployment.

• Goal oriented workloads: Agentic tools can archive software development goals, such as porting an existing codebase to a different tech stack or updating code for a version upgrade. 
• Developer documentation: Keeping documentation up-to-date is a constant challenge as codebases grow. Autonomous agents can automatically generate or update documentation, ensuring it remains accurate without requiring constant human oversight. 
• User feedback and support ticket analysis: Analysing large volumes of user feedback and support tickets can be overwhelming. Autonomous agents can assist in categorising and prioritising issues, allowing teams to respond to critical problems more efficiently. 

Creating an AI-assisted development methodology: go-vibe.dev 

Being early adaptors of GenAI for product engineering, we have identified that using advanced tooling alone will not yield the best results. Based on our learnings, we have created a methodology that allows engineers to maximise use of AI with an early public version available at https://go-vibe.dev.

The future of GenAI in engineering 

The role of GenAI in engineering is evolving rapidly, opening a wide range of possibilities for the future. As AI tools continue to advance, their ability to deliver meaningful value to engineering teams will only increase. Protocols such as the Model Context Protocol (MCP) enable seamless integration of a broad array of tools with AI, amplifying their overall impact. As autonomous agents become more prevalent, developers will be able to shift their focus toward creative problem-solving and strategic decision-making, while AI takes on the routine tasks that typically consume valuable time.

In conclusion

 At 99x, we are excited about the potential of GenAI to transform the future of product engineering. Incorporating these tools into our workflows allows us to build products faster, deliver higher quality, and place greater emphasis on user needs. GenAI helps us move beyond human limitations, scale our ambitions, and develop innovative solutions that create meaningful impact.

Looking ahead, the future of product development will be shaped by the ability to harness GenAI to elevate engineering practices. These tools empower teams to work more intelligently, streamline routine tasks, and explore new frontiers of possibility. Embracing GenAI enables development teams to navigate the demands of today’s fast-paced environment and consistently deliver products that exceed user expectations.

(The writer is the Chief Developer Experience Officer, 99x. He brings over 15 years of experience in the software industry, having designed and developed solutions across a wide range of domains and platforms.)