I am sure you have tried to hire the 10x engineer and maybe have seen a few. Every organisation has some people who are considered indispensable and deliver the mystical 10x output, setting them apart from the rest. From the perspective of the organisation though, the output from this kind of setup will always be sub optimal.
If your team slows to a crawl every time a 10x is unavailable, what you really have is a 1x system with a single point of failure.
The challenges start right from hiring. They are already invested in what they do and their current workplace has no intention of letting them go. Once you hire them, the next challenge is that they are too thinly spread. This will lead to the reduction in output from the 10x already.
In the longer term, expect the rest of the team to become more dependent on the "hero" who has all the knowledge and the skills. When this happens another side effect is that only certain kinds of ideas / architecture are considered because of the one person being consulted for almost everything.
The 10x Team
The most obvious solution to these problems is to scale the 10x capability to the entire team. Up until recently that has not been possible. With the best efforts to have the best industry practices, documentation and processes, teams typically continue to show a wide range of performance levels. AI agents can change this completely.
The Definition
What does a 10x team really mean? With AI coding agents, do engineers become super human and with 5 engineers get as much as what 50 engineers used to do? The answer is no, not 10x of what they used to do.
There is a lot of difference between getting something done and getting it done well. Without unlimited money and resources, teams usually come up with some practices that they would definitely do, and let go of some others.
This can vary from team to team depending on the resources they have. Because of these compromises, tech debt increases, leading to long term issues like requirements for sudden upgrades, bugs leading to patches and more patches to patch those, duplicate work, difficulty to extend and maintain code, etc. All of this contributes to a general slowness in execution, difficult in estimations and ultimately missed release dates.
AI-assisted engineering helps conserve time that was previously spent writing code skeletons, allowing teams to focus on better designs and implementation patterns. That leads to 10x outcomes.
The Changes
If all the best practices and process were already followed, with the right AI agent use, you would see a 10x outcome improvement right away. But no one does it that well, so the outcome will not increase the number of delivered features by 10x, but will improve the quality of code that much, to offset the difference.
My team is going through a fundamental change in perspective while looking at problems, when working on projects. Earlier, the implementation was considered the most time consuming and challenging aspect. With AI agent assistance, not any more.
Implementation is easy, what we are focussing on now is whether the right alignment exists between different stakeholders, all engineering aspects have been researched properly, design documents are written well and reviewed, the implementation is scalable and extendable, effective automated tests are written for all features. This is 10x performance.
The Outcomes
10x does not mean shipping 10x more features. It means eliminating drag, preventing future cost, and compounding quality so the system performs 10x better.
I. Time and Cost
Faster product velocity | Lower cost per outcome | |
AI-enabled implementation shrinks the time spent on boilerplate, refactoring, and cross-repo discovery, letting engineers spend more time on architecture and problem-solving. | Engineering teams reduce the cost of both building and maintaining software because high-quality, AI-assisted code creates fewer regressions, fewer escalations, and drastically less rework. |
II. Sales and Retention
Faster sales cycles | Higher customer satisfaction | |
Engineering produces higher-quality, more reliable features and integrations with fewer bugs.. | Stable, consistent systems with fewer incidents, clearer APIs, and faster iteration cycles directly improve the end-user experience. |
Making Implementation Easy
The objective is to make coding and implementing features so easy, that teams move on to solving other problems that truly accelerate the business outcomes. This cannot be achieved however without considerable effort from the team. Just having an AI agent helping out individual developers code is not going to help much. That is because the effective use of these tools is also a skill, that will be again utilised based on the spectrum of performance already present in the team. Some will be better at it, some not so much.
It is important to understand from a team / company perspective, what are the things AI agents are good at, how those capability and skills can be integrated into the team, ultimately leading to process that make it seamless to actually implement features.
How do AI coding agents help?
Why would AI agents be more effective to help developers code better and faster, than all the time tested industry standard mechanisms already in place? The reason behind it is that it is easier to get better results than those traditional ways of ensuring code consistency, best practices and other internal knowledge.
AI agents don't invent a coding paradigm, they help implement existing best practices more effectively than ever before.
AI can now distribute the coding expertise to all the team members, leading to a big jump in the average output. This average improvement is the real multiplier.
As the organisation or the coach, you would want to help the developers with three kinds of information, for them to develop fast.
Boilerplate / specific tasks: AI is the replacement for scripting to do monotonous coding tasks.
Repo specific information: AI allows searching with logic and implementation with no need to repeatedly train developers with gotchas and contracts.
Coding practices: With AI , no need to repeat train all developers with the company specific coding practices.
With the understanding and implementation capabilities that AI coding agents have, all these have become easy to setup. AI does most of the monotonous work along with assisting in coding so that knowledge and skill is not restricted to an interested few.
How to leverage AI coding agents
To be able to leverage AI to distribute 10x capabilities across the team takes an organised effort in just three dimensions. Planning to improve in these areas and implementing strategic changes to the functioning of the team helps boost productivity and outcome massively.
Let me introduce you to the 10x Team Stack that includes the three top level constructs that enable a team to deliver 10x.
Team Output = Human Creativity × AI Leverage × Process Intelligence
The overall output of a team depends on these three factors, which combined provide a multiplicative effect towards the overall outcome.
I. Developers as AI-Augmented Problem Solvers
Before AI came in typically (except talented senior engineers), a bulk of all engineering team members spent time actually writing code. This included boilerplate, defining contracts, the code to implement the design, testing at various layers, etc. This was considered to be the most expensive and critical part of software development. With AI that should not be the case. The parts that human developers are still critical for, though, becomes that much more important now.
The architecture, implementation design, systems coordination and scaling, etc like factors are not something that AI agents will invent by themselves. Yes, they will help write these into a document, research individual factors when called out and give suggestions around those. But the big picture needs human developers.
To be able to deliver in such a scenario will require human creativity for software engineers to evolve to good use of AI (with prompt precision, multi agent orchestration, etc) and evolving into being software architects who can review code and design systems efficiently.
II. Documentation to making the Codebase Agent-Ready
Imagine AI agents is just another Software Engineer for you to onboard into your engineering team. The question to ask is "What really makes anyone Your Company engineer?" It is a combination of:
Best practices followed by your company
Knowing how to navigate the repository
Gotchas and tribal knowledge around services / team owned code
So engineers are basically the cache layer which can identify company specific coding actions to quickly decide on what to do, given a feature to implement.
The second question to ask is "How to new engineers become Your Company engineers?" If this is entirely or partially with experience or talking to other engineers, its the wrong answer.
Your cache is being built by a very slow underlying database. This needs to be solved by adding all relevant documentation. Not only will this help AI agents to become good engineers for your company, but also help in general for human developers as well.
III. Building AI-First Workflows
This is the only aspect where direct integration with AI agents will surface. With an organised documentation already available for the repository, the trick is to now build all the right connections with the AI agent that will help it understand where to start from.
Documentation can explain the details of a certain part of code, but it is a little too verbose to actually be of any use. Imagine being given a 1000 page book to read to be able to understand how to build anything in the repository. Instead of having to read and recollect everything, if the book had a table of contents, you could quickly refer it to understand which parts of the book needs to be read, to be able to write code for that area or to build that specific thing.
Such workflows are critical for AI agents to identify and load the right documentation to read and to put things together to be able to generate a part of a feature. Multiple such workflows can be stitched together to deliver a full feature.
Building these workflows can and should be incremental, with supporting documentation being written and improved while also adding on to the definition of workflows and creating new ones. The continuous feedback loop between AI agents and human engineers make it faster and easier to deliver the features.
The Manager's Role
The engineering managers role towards enabling the 10x team is foundational. Being able to recognise the immense potential in orchestrating AI agents to help the team build at a much improved speed without compromising and even improving the teams comfort almost sounds too good to be true. But it is possible and engineering managers must lead the team towards this direction.
The evolution curve for teams adopting AI agents for assisted coding goes from casual usage to being integrated into the team's execution pipelines.
1️⃣ AI Curious (Individual Experimentation)
2️⃣ AI Augmented (Coding help adoption)
3️⃣ AI Native (Workflows and systems designed for agents)
The evolution of an AI Native team takes time and sustained effort. It requires building a culture of engineering architecture, careful and maintained documentation, curation and composition of skill and workflows. The requirement of this process to be setup and maintained gives EMs an amazing opportunity to build entirely new and exciting process with the team, leading to 10x outcomes.
Closing Thoughts
You will lose a 10x engineer as soon as you hire one. They are hard to find and over dependence will lead to reduced effect of the 10x engineer in the big picture. The aim should be to get the team to 10x productivity and this is now achievable with human skill multiplied by AI agents usage.
However, only getting the AI agents for personal engineer use will not get the team there. That is because the usage of this tool is still dependent on the individual skills of engineers. The 10x Team Stack is a high level principles set that will help average out the engineering principles, processes and specific technical knowhow, leading to actually enhance the team outcomes to multiples of what is possible currently.
