In product development, we live by a simple mantra: test everything. We A/B test landing pages, feature placement, button colors, and email copy. We run experiments to find the most effective way to drive user engagement and achieve our goals. So why, when it comes to the complex, high-stakes process of managing the project itself, do we revert to guesswork and gut feelings?
Traditional project management often relies on rigid methodologies or historical habits. We choose a framework like Agile or Waterfall and hope for the best, with no real way to measure if our process itself is optimal. What if you could apply the same data-driven rigor of A/B testing to your project workflows?
Welcome to the concept of workflow experiments. With an agentic, code-based approach to project management, you can stop guessing and start engineering your path to success.
A workflow experiment is the practice of defining and running two or more variations of a project plan to determine which process is more effective at achieving a specific goal. Instead of testing a UI element, you're testing a process variable.
Think about the variables in your own projects:
By running these variations as controlled experiments, you can gather empirical data on what actually makes your projects succeed.
Running a true workflow experiment in a tool like Jira, Asana, or Trello is a logistical nightmare. The reason is simple: they are passive systems of record, not active systems of execution.
To run a real experiment, you need a platform where the project plan is not just a checklist, but executable code.
This is where Agentic Project Management changes the game. At projects.do, we believe that project plans should be defined, version-controlled, and executed just like software. This "Projects-as-Code" paradigm is the key to unlocking workflow experiments.
Your entire project—tasks, goals, dependencies, and all—is defined in a structured, machine-readable format.
import { Agent } from 'projects.do';
const projectAgent = new Agent();
// Base project definition
const baseProject = {
name: 'Q4 Product Launch',
description: 'Coordinate all activities for the new feature launch.',
goals: [ { metric: 'User Adoption Rate', target: '15%' } ],
dependencies: ['legal.review.completed', 'dev.sprint.finalized']
};
// Variation A: Milestone-based notifications
const planA = {
...baseProject,
name: 'Launch - Plan A (Milestones)',
notificationStrategy: 'onMilestoneCompletion'
};
// Variation B: Granular task-based notifications
const planB = {
...baseProject,
name: 'Launch - Plan B (All Tasks)',
notificationStrategy: 'onTaskCompletion'
};
Your project plans are now versionable in Git, open to review via pull requests, and easily templated for experimentation.
Once defined, AI agents take over. They interpret the project code and execute the workflow with perfect consistency. The agent handles task assignments, dependency checks, progress monitoring, and stakeholder notifications precisely as defined in each variation. This removes human execution bias and ensures a clean, controlled experiment.
// Execute both project variations simultaneously
await projectAgent.start(planA);
await projectAgent.start(planB);
Because an agent is running the show, it's also collecting precise, structured data at every step. At the end of the project, you don't have to dig through ticket histories. You can simply query the API to compare the performance of each variation against your key metrics.
Ready to become a project management scientist? Here’s your guide.
Project management shouldn't be an art form based on intuition. It should be an engineering discipline that is measured, optimized, and continuously improved. By treating your projects as code, you transform them from static checklists into dynamic, executable assets.
A/B testing your workflows is the next frontier in operational excellence. It allows you to build a library of proven, data-backed processes that make your organization faster, more efficient, and more predictable.
Ready to transform your project management into an executable science? Explore projects.do and see how AI agents can turn your plans into measurable results.