If you’re still ordering two pizzas for your software development team expecting to feed the traditional 6-10 people, brace yourself for leftovers. The rise of agentic AI tools for software product development is shrinking the human headcount in two-pizza teams, with AI agents stepping in as virtual team members. At AKF Partners, we’ve long championed small, cross-functional teams that own their products end-to-end, but with AI’s growing capabilities, these teams are not only becoming leaner—potentially as small as 2-3 humans—but also more powerful, while still adhering to the cross-functional principles we advocate.

The Two-Pizza Team

AKF Partners has always emphasized that two-pizza teams should be small, autonomous, and cross-functional, encompassing all skills needed to deliver a product or feature—developers, testers, designers, and product managers as we describe in principles of high performing product teams. This structure minimizes dependencies, speeds up decision-making, and ensures accountability. Traditionally, these teams ranged from 6-10 people to balance capability and communication. Now, with agentic AI tools, the team size and composition are evolving.

The rise of AI agents significantly enhances the capabilities of software product development teams, and can allow smaller teams of 2 or 3 humans to achieve what larger teams once did, but it doesn’t universally mean such small teams are optimal for all products and projects. Here’s a breakdown of how this could work and the considerations involved, building on the cross-functional and two-pizza team principles discussed earlier:

How AI Agents Enable Smaller Teams

AI agents can take on substantial roles in software development, reducing the human headcount needed for certain tasks:

  • Coding and Prototyping: Tools like GitHub Copilot, Google Firebase Studio or custom AI coding agents can generate, review, and optimize code, potentially handling the workload of multiple developers. For example, a single human developer paired with an AI agent could produce code at the pace of a small team.
  • Testing and QA: AI-driven testing frameworks (e.g., testRigor or LambdaTest KaneAI) automate test case creation, execution, and defect detection, reducing the need for dedicated QA engineers. One human overseeing quality assurance could manage what previously required a larger QA team.
  • Scrum Master and Analytics: AI agents can track progress, allocate tasks, and analyze user feedback, minimizing the need for a dedicated scrum master or data analyst.
  • Cross-Functional Support: AI tools can assist with design, DevOps, and user experience tasks, such as generating UI mockups or automating deployment pipelines, further compressing the roles needed.

In this scenario, a team of 2 or 3 humans—say, a lead developer, a product manager, and a designer—could leverage AI agents to cover coding, testing, deployment, and analytics, effectively functioning as a cross-functional unit. For smaller projects or startups with limited scope, this lean setup could be highly effective, delivering features rapidly while maintaining AKF’s principle of end-to-end ownership.

Feasibility of a 2- or 3-Person Team with AI

  • For Small or Focused Projects: A team of 2 or 3 humans with AI agents is feasible for projects with well-defined scopes, such as building a minimum viable product (MVP) or iterating on a single feature. For instance, a SaaS startup could use AI to handle repetitive coding and testing, allowing a small human team to focus on product strategy and user alignment.
  • Scalability with AI: AI agents scale efficiently, handling increased workloads without the coordination overhead of larger human teams. This makes a small team viable for tasks that don’t require extensive human collaboration or domain-specific expertise beyond AI’s capabilities.
  • Cost Efficiency: Smaller teams reduce overhead, and AI tools, often subscription-based, are cost-effective compared to hiring additional software developers.

Limitations and Challenges

However, a team of 2 or 3 humans plus AI agents may not suffice for all scenarios:

  • Complex Projects: Large-scale or highly complex projects, like those involving intricate system architectures require diverse expertise that AI cannot fully replicate. Human oversight is critical for strategic decisions, system design, and handling edge cases.
  • Legacy Code Context: Existing large code bases require knowledge of the codebase and its architecture. Applying an agent today to a large code base may yield poor results as the agent may not be able to contextualize everything that’s needed. While this may be limited today, it will improve.
  • Human Expertise: AI agents excel at operational tasks but lack the creativity, empathy, and contextual judgment humans bring. For example, a product manager’s role in aligning with customer needs or a designer’s focus on nuanced user experience remains uniquely human.
  • Collaboration Dynamics: Teams of 2 or 3 risk burnout or bottlenecks if one member is unavailable. It looks good on paper but in reality, bottlenecks will occur.
  • AI Limitations: Current AI tools require human validation to ensure quality, security, and alignment with business goals. Over-reliance on AI without sufficient human oversight could lead to misaligned outputs or technical debt.

Cross-Functionality and AKF Principles

Even with a smaller team, AKF’s emphasis on cross-functionality remains essential. A team of 2 or 3 humans must still cover key domains—product vision, technical expertise, and user experience—augmented by AI for tasks like coding, testing, operating, and project coordination.

For example:

  • Human Roles: A product manager defines the vision and validates AI outputs, a developer oversees architecture and integration, and a third role (e.g., designer or DevOps) ensures UX or deployment quality. All of the human’s in the pipeline must guide the agents using prompts for UI, functional, and architecture design.
  • AI Roles: Coding agents, testing frameworks, and project management bots fill gaps, ensuring the team remains end-to-end responsible for the product.

This hybrid structure maintains the two-pizza team’s agility and autonomy, even if the human count drops below the traditional 6-10 range.

Practical Considerations

  • Team Size Sweet Spot: While 2 or 3 humans with AI agents can work for lean projects, a slightly larger team (e.g., 4-5 humans) often strikes a better balance for most software development efforts, providing redundancy and diverse perspectives, as we discuss in a prior blog, while leveraging AI for efficiency.
  • Upskilling: Humans must be trained to work with AI tools, from crafting effective prompts to interpreting outputs, as seen in discussions about QA automation tools like testRigor. Effective prompts, sometimes called “prompt engineering”, becomes important for UI desing, functional design and architecture design.
  • Iterative Adoption: Start with a small team and a few AI agents, measure performance, determine capabilities and the boundaries of practical use, and adjust. Considering the pace and speed of model advancement, this should be repeated. This aligns with AKF’s experimentation-driven approach.

The rise of AI agents makes it possible for software product development teams to operate with as few as 2 or 3 humans for small, focused projects, provided AI handles operational tasks like coding, testing, and project management. However, cross-functionality, human oversight, and project complexity dictate that slightly larger teams (4-6 humans) may be more robust for most scenarios. By integrating AI thoughtfully while adhering to AKF’s principles of autonomy and end-to-end ownership, organizations can build leaner, smarter teams that deliver high-quality software efficiently. In the end, human developers will be innovation directors overseeing and guiding the AI agent’s work product.