Institute of Product Leadership

Using Google Antigravity

Author : Rohan Mitra – Product Manager at Phonepe

For the past few years, AI tools have mostly been used as assistants for small productivity tasks. They helped generate content, summarize information, answer questions, and speed up research. The interaction model was straightforward: ask a question, get a response, refine the output through follow-up prompts.

That model is now evolving into something far more execution-oriented.

AI is gradually moving from being a conversational interface to becoming a system that can execute structured work. Instead of just generating responses, newer agent-based systems can interpret objectives, plan execution steps, create deliverables, test outputs, and suggest improvements. The shift may appear subtle at first, but its implications for product development are significant.

The real change is not that AI can now write code. It is that AI can participate across multiple stages of the product lifecycle from discovery to prototyping to validation. This reduces the gap between an idea and a tangible prototype, allowing faster experimentation and learning.

Key Takeaways:

  • AI agents are shifting AI usage from answering questions to executing end-to-end product workflows.
  • The biggest advantage of AI agents is the ability to research, build, test, and plan in parallel.
  • Clear problem definition and structured specs matter more than technical expertise when working with agents.
  • AI-built outputs should be treated as fast prototypes, not production-ready solutions.
  • The real competitive advantage is no longer using AI, but knowing how to direct it effectively.
In this article
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    Understanding the difference between chat AI and agent AI

    Traditional AI tools function primarily as response engines. They depend heavily on user direction and require continuous prompting to move work forward. The user remains responsible for structuring the workflow and connecting outputs into something usable.

    AI agents operate differently because they are task-driven rather than response-driven.

    Instead of waiting for the next instruction after every response, agents can:

    • Interpret a product brief and break it into logical steps
       This includes identifying research needs, architecture decisions, and execution stages rather than simply answering questions.
    • Generate structured outputs such as technical plans or workflows
       Rather than fragmented responses, agents tend to produce documents that resemble actual work artifacts like implementation plans or strategy notes.
    • Execute tasks such as file creation, testing, and validation
       Agents can create working files, run test environments, and check whether expected behavior matches actual behavior.
    • Identify issues and suggest improvements
       Instead of stopping after execution, agents can review their own work or even critique outputs generated by other agents.

    This makes the interaction model closer to assigning work to a contributor rather than consulting a knowledge source.

    Why this matters in product development?

    Product development rarely happens as a single linear activity. It involves problem discovery, prioritization, technical feasibility discussions, design tradeoffs, testing, and eventual market positioning. Traditionally, these steps involve multiple teams, which introduces delays between ideation and execution.

    AI agents can compress some of these delays by allowing parallel progress across different aspects of product work.

    For example, instead of waiting for research to finish before starting technical exploration, different agents can simultaneously work on:

    • Market research and competitive analysis to understand how similar problems are currently being solved and what gaps exist.
    • Prototype development to test whether a proposed solution can actually be implemented within defined constraints.
    • Validation workflows to test whether the prototype behaves as expected under normal usage scenarios.
    • Positioning thinking to explore how the product might be differentiated if taken forward.

    This parallelism does not replace teams, but it does allow earlier validation before formal development cycles begin.

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    The continuing importance of planning

    Despite the automation capabilities of AI agents, planning remains the most important stage of the workflow. Poorly defined instructions tend to produce overcomplicated or misaligned outputs. Clear instructions, on the other hand, tend to produce structured results.

    Effective agent specifications usually benefit from three qualities:

    Clear scope definition

    A good specification should define what the product includes and what it deliberately excludes. This helps prevent unnecessary complexity.

    For example, defining that an application should be a single-page interface without backend infrastructure immediately narrows implementation choices and avoids unnecessary dependencies.

    Concrete requirements

    Providing clarity around expected features, constraints, or technology choices usually improves output quality. Even when exact technologies are not known, describing expected behavior helps guide implementation.

    For instance, specifying that user inputs should persist locally immediately informs how data storage should be handled.

    Context where possible

    Agents tend to perform better when given reference points. Providing examples of similar products or describing expected user experiences gives direction that improves alignment.

    The key takeaway is that agents reward structured thinking. The more clearly a problem is framed, the more effectively execution tends to follow.

    Moving from an idea to a working prototype

    One of the most practical applications of agent workflows is the ability to move from a simple idea to a functional prototype without traditional manual coding. Consider a simple example of a feature request voting application.

    The concept is straightforward. Users should be able to submit ideas, vote on existing ones, and see the most popular ideas ranked first. The technical constraints might include keeping the solution lightweight, avoiding backend infrastructure, and storing information locally.

    When a structured brief like this is provided, agents typically begin by expanding the problem into an execution plan. This may include identifying comparable tools, suggesting interface structure, outlining file organization, and defining verification steps.

    Once the plan is established, the execution phase begins. This can include:

    • Creating the required interface structure: This includes generating the layout, styling components, and interaction elements required for the application.
    • Implementing interaction logic: This involves writing the logic that allows users to submit ideas, vote, and see updates reflected immediately.
    • Managing data storage: Even in simple applications, the agent may structure how user inputs are stored and retrieved.
    • Running validation tests: Some agents can launch local environments, simulate user interactions, and verify whether the workflow behaves as intended.

    The most important outcome is not just that a prototype is created, but that validation becomes part of the process rather than an afterthought.

    The advantage of parallel agents

    One of the more powerful capabilities of agent workflows is the ability to assign different agents to different responsibilities within the same project environment. Instead of relying on a single AI instance to perform all tasks, work can be distributed.

    This approach has several advantages:

    • Specialization improves quality
       When different agents focus on different tasks such as development, review, or strategy, the outputs tend to be more structured because each workflow remains focused.
    • Cross-verification reduces errors
       Having one agent review another agent’s work introduces an additional layer of quality control similar to peer review in real teams.
    • Context remains cleaner
       Long conversations with a single AI system can sometimes lead to confusion or context dilution. Dividing work helps maintain clarity.
    • Work progresses faster
       Parallel execution means that research, development, and strategic thinking can move forward simultaneously rather than sequentially.

    This structure mirrors how well-organized product teams operate, with multiple contributors working toward a shared outcome from different perspectives.

    How this changes the role of product thinking?

    There is often a concern that if AI can build prototypes, the importance of product thinking might decrease. In practice, the opposite appears to be true.

    Execution is becoming faster, but direction remains critical. Someone still needs to define what problem is worth solving, what tradeoffs are acceptable, and what success looks like. AI accelerates execution capacity, but it does not determine priorities.

    In fact, faster execution makes clear thinking more important because unclear decisions can now scale faster as well.

    This shifts the emphasis of product work toward:

    • Defining problems clearly rather than documenting them extensively
    • Validating assumptions quickly rather than debating them theoretically
    • Guiding direction rather than managing tasks

    The nature of the work changes, but the core thinking responsibilities remain intact.

    Important limitations to keep in mind

    Despite the advantages, AI agent outputs should be treated carefully. These systems are excellent at accelerating early exploration but are not yet reliable as standalone production systems.

    There are several practical limitations worth noting:

    • Production readiness is not guaranteed
       While agents can produce working prototypes, these may not meet security, scalability, or performance requirements needed for real-world deployment.
    • Verification remains essential
       The speed of generation can create a temptation to trust outputs without review. This introduces risk, especially in complex applications.
    • Edge cases may be missed
       Agents are strong at implementing defined workflows but may not always anticipate unusual usage scenarios unless explicitly instructed.

    Because of these limitations, the most effective way to use agents is as accelerators for exploration rather than replacements for engineering rigour.

    Practical ways to begin experimenting

    For those looking to explore agent workflows, starting with manageable projects is usually more effective than attempting complex builds immediately. Smaller projects help develop intuition about how to structure instructions and iterate effectively.

    Some practical starting points include:

    • Personal portfolio websites
       These are useful because they involve layout design, content structure, and interaction logic without complex infrastructure requirements.
    • Simple productivity tools
       Tools such as idea trackers or task organizers help demonstrate how agents handle interaction workflows.
    • Internal dashboards
       Basic visualization tools can help explore how agents structure data display and filtering.

    These projects provide enough complexity to be meaningful while remaining manageable enough to understand the workflow clearly.

    What this means for speed of learning?

    Perhaps the most meaningful change introduced by AI agents is not simply faster building, but faster learning cycles. Traditionally, moving from idea to validation involved multiple handoffs across teams, each introducing delays.

    Agent workflows compress this cycle by allowing early experimentation without waiting for full development bandwidth. This enables faster testing of assumptions, which is often more valuable than faster execution alone.

    Teams that can test ideas faster tend to learn faster. Teams that learn faster tend to build better products.

    The advantage therefore shifts toward those who can structure experiments effectively rather than those who can simply execute tasks quickly.

    The emerging advantage: clarity of thinking

    As these workflows become more common, basic AI usage will likely become a baseline skill. The differentiator will not be whether someone can use AI, but how effectively they can structure problems for AI to execute.

    The emerging advantage is likely to belong to individuals who can:

    • Frame problems clearly so that execution systems can act meaningfully
    • Provide enough context without overcomplicating instructions
    • Evaluate outputs critically rather than accepting them blindly
    • Iterate based on outcomes rather than assumptions

    In that sense, the most valuable skills remain surprisingly consistent with traditional product thinking. Understanding users, defining problems, prioritizing tradeoffs, and validating outcomes continue to matter more than tool familiarity alone.

    AI agents represent an evolution in how early-stage product work can be executed. They do not remove the need for teams, technical expertise, or decision making. What they change is how quickly ideas can become testable.

    This affects how quickly assumptions can be validated, how early feedback can be gathered, and how efficiently learning cycles can operate.

    The professionals who benefit most from this shift are unlikely to be those who simply use these tools occasionally. The advantage will likely go to those who treat them as structured execution systems, integrate them into workflows thoughtfully, and maintain strong evaluation discipline.

    As execution becomes easier, the real differentiator may simply become the ability to think clearly about what is worth building and why.

    Frequently Asked Questions

    AI agents are autonomous AI systems that can plan, reason, and execute multi-step tasks to achieve a goal, while chatbots mainly respond to prompts and require continuous user direction.

    AI agents can support product development by conducting research, generating technical plans, building prototypes, testing workflows, and suggesting improvements, helping teams move faster from idea to validation.

    No, product managers do not need deep coding skills to use AI agents, but they do need strong problem definition, structured thinking, and the ability to review outputs critically to get the best results.

    Most AI agent outputs should be treated as prototypes or MVPs, as they may require engineering review for scalability, security, and performance before production use.

    The most important skills include clear problem framing, writing structured specifications, evaluating outputs, and understanding product workflows rather than just technical expertise.

    Institute of Product Leadership is Asia’s First Business School providing accredited degree programs and certification courses exclusively in Product Management, Strategy, and Leadership.
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