Act I: The Shift in Readership
It all starts with a core question: "Who is reading the Task?"
For the past 15 years, tools like Jira, Trello, and Monday.com have dominated the world of project management. The design assumption of these tools was very clear: the reader of the task ticket is a human (PM, Engineer, QA). To please humans, these tools feature flashy interfaces, full of drag-and-drop interactions and rich visual feedback.
But now (2024-2026), we are facing a historic turning point: the reader of the task ticket has changed.
The new readers are no longer humans sitting in front of screens, but AI Agents (Copilot, Cursor, ChatGPT, Devin). These new readers have completely different characteristics:
- Cannot understand GUI: They don't care where the buttons are.
- Need structured text: They crave deterministic formats like JSON, YAML, and Markdown.
- Extremely dependent on Context: This is the most critical point.
This shift explains why text-based management is making a strong comeback. This isn't for nostalgia, but for Interoperability. Text is the only universal interface in computer science. It eliminates the friction of the human-machine interface, allowing Agents to directly read requirements and execute them, without humans acting as a "human router" ferrying information between Jira and the IDE.
Act II: Context Gravity
Since the reader has become an AI Agent, we must solve a fatal problem: the Context Gap.
In traditional workflows, task definitions live in the cloud (Jira), while the execution environment is local (IDE/Git). Jira doesn't know what your code looks like, and the IDE doesn't know what your task is. This gap is unbridgeable for AI. Without Context, even the smartest model is blind.
To let the Agent understand the task, we must perform an action: Pull the Task Down.
This is the concept of Context Gravity. Following the principle of Data Gravity, the task (TASK.yaml) should live right next to the code (src/) it describes.
When the task and code live together at "zero distance" in the Git repository:
- The Agent (like Copilot/Cursor) automatically scans the task definition.
- You don't need to explain: "Hey, please look at Jira #1234".
- The Agent directly sees the Objective and the relevant File Context.
This maximizes the efficiency of RAG (Retrieval-Augmented Generation). We no longer throw the entire Wiki at the AI, but precisely provide the context it needs. Tightly coupling Intent with Implementation is the key to filling the vacuum in the execution layer.
Act III: Prompt as Code & Task as Code
When we pull the Task down for the Agent and describe it with structured text, we have actually realized two powerful concepts: Prompt as Code and Task as Code.
This is like the Terraform Moment for the AI era.
Recall how Infrastructure as Code (IaC) changed operations: we no longer manually click through the AWS Console, but write HCL code to define architecture. This brought automation, reproducibility, and version control capabilities.
Now, we are treating AI tasks with the same logic:
- Chat Era: We manually input prompts in the ChatGPT web interface, with no version control, no reproducibility, and inconsistent quality.
- POG Era: We use YAML to define AI behavior, just like using HCL to define infrastructure.
POG (Prompt Orchestration Governance) is the concrete implementation of this concept:
- Declarative: You tell POG what you want (Objective), not how to do it.
- Modular: You can reference prompt fragments just like referencing modules.
- Auditable: All changes are in Git;
git blamecan tell you who introduced the prompt that caused hallucinations.
This is not just swapping one tool for another; it is a shift in mindset. We are no longer people who "operate" chatbots; we are engineers who "define" tasks and context.
By pulling the Task down to the code level and standardizing it as Code, we finally prepare for the AI Agent to become a member of the team. This is not retro; this is the necessary path to Scale.
Practical Guide: https://dev.to/enjtorian/pog-task-03-deep-dive-into-pog-task-the-missing-layer-and-the-pog-task-moment-2oe1
Quick Start: https://enjtorian.github.io/pog-task/quickstart/
Top comments (0)