Ever noticed how reliably a rice cooker prepares a perfect batch of rice? It’s a simple, automated process: add the measured rice, add the measured water, set the timer, and walk away. This mundane household appliance holds a surprising lesson for software teams grappling with story point estimation and the dream of fully automated development.

The Rice Cooker Analogy: Estimation Made Easy

The complexity of boiling rice, getting the water-to-rice ratio and heat just right is boiled down (pun intended!) into a simple, predictable function by the rice cooker. What does this teach us about estimating story points for software tasks?

1. Fixed Inputs, Predictable Output

• Rice Cooker: The ratio of rice to water is a fixed, known input. The cooking time is an internal, calculated constant. The output (cooked rice) is highly predictable.
• Story Points: We often try to treat complex tasks (like “Build Feature X”) as if they have fixed inputs, leading to debates. The rice cooker suggests that for estimation to be reliable, we need to standardize the inputs as much as possible. This means clearly defined acceptance criteria, pre-selected tools/frameworks, and minimal external dependencies. When a task is truly standardized, its “story points” become less of an estimate and more of a known complexity factor.

2. Automation Removes Variance

Manual rice boiling introduces variance based on the chef’s skill, the stove’s heat, and the pot used. The rice cooker removes this variance.

• For software estimation, variance comes from:
  • Developer A is slower than Developer B.
  • “Hidden” technical debt in a code area.
  • Uncertainty about requirements.

The more we can automate the underlying processes (like continuous integration, standardized component libraries, and automated scaffolding), the more our estimates become reliable “knowns.”

From Rice Cooker to Code Agent: The Automation Dream

The second, more ambitious thought is: If a single pot can automate rice, can an AI agent automate the construction and deployment of a simple feature, like a login page?

This isn’t just about using a smart IDE; it’s about a fully autonomous agent workflow:

1. Task Decomposition and Execution

The Agent receives the requirement: “Build a simple user login page with email and password fields, validate input, and connect to the existing authentication service API.”

• Agent 1 (Frontend): Scaffolds the React/Vue component. Uses a standardized UI library. Writes the component code, adhering to existing style guides.
• Agent 2 (Testing): Automatically generates Unit Tests (e.g., Are email validation rules enforced?) and Integration Tests (e.g., Does the login button correctly call the API?) based on the code and requirements.
• Agent 3 (Backend/Ops): Reviews the code, runs the tests in a staging environment, and if all tests pass, executes the Continuous Deployment (CD) pipeline to release to production.

2. Learning and Self-Correction

The rice cooker’s logic is fixed. A code agent, however, could be adaptive.

• If the staging deployment fails due to a new dependency, the agent learns and updates its dependency management script for the next iteration.
• The agent could start by estimating the task as “5 Story Points” (complex), but after building and deploying 50 similar login pages, its next estimate becomes “1 Story Point”, it’s essentially a solved, automated problem. The estimate shifts from human effort to machine execution time.

Conclusion: The Path to Zero-Point Stories

The lesson from the humble rice cooker is that predictability precedes automation.

Instead of agonizing over whether building a login page is 3 or 5 story points, we should ask: “How can we standardize and automate this task until its story point value approaches zero?”

The future of story points may not be in better human guessing, but in the rise of specialized AI agents that execute standardized tasks so reliably that the only thing we need to estimate is the complexity of the prompt itself, not the code written by the machine.

What are your thoughts on Zero-Point Stories accomplished entirely by AI agents?