Structured AI Coding with Task Context: A Better Way to Work with AI Agents

Are you using AI agents to assist with feature development or bug fixing—only to find yourself stuck in an unstructured back-and-forth chat session?

Of course, you could wait for the next generation of more powerful LLMs or tools, or even try stuffing more context into your prompts. But there’s a much simpler and far more effective approach that we demonstrate in the video below: Task Context.

Task Context introduces a structured, reproducible way to work with AI agents—ensuring clearer intent, better planning, and far more accurate results. Rather than relying on a loose chat, this method treats prompts and plans as first-class citizens in your workspace, allowing you to iterate and collaborate more efficiently with both humans and AI. While this concept is natively supported in the AI-powered Theia IDE, the principles can be transferred to any AI coding tool to improve your development workflow.

Watch the video for a full walkthrough, then continue reading to learn more about the core concepts, benefits, and how to apply them in your own workflow.

From Unstructured Chat to Structured Context

Most AI-assisted development today happens through chat prompts inside the IDE. This can work—but only to a point. If the AI misunderstands your request or lacks context, you often end up in a vague, inefficient loop of clarifications. The process resembles developing with a chat group and lacks the structure needed for serious development work.

Task Context solves this in a simple way by externalizing your intent into dedicated prompt files

These files become persistent, editable records of what you want the AI to do. They serve as the starting point for AI sessions inside the Theia IDE, where you can run a command like Initiate Task Context Session, linking the file directly to the AI agent.

This approach makes AI interactions reproducible, shareable, and version-controlled—just like any other part of your codebase.

The Architect Agent: Planning Before Coding

For complex tasks, the real power of task context is unlocked by involving a planning agent first—rather than jumping straight into code generation.

In the Theia IDE, this role is handled by the Architect Agent, which:

• Reads your task context file
• Analyzes the workspace and relevant code
• Produces a detailed task plan covering:
  • Problem and scope
  • File-specific implementation plan
  • Test strategy (unit + manual)
  • Deliverables
  • Pull request description

This extra step may seem like overhead—but it saves enormous amounts of time. Instead of endlessly debugging poorly generated code, you can catch problems at the planning stage. For example, if the plan suggests adding a “button” when you really need a “toolbar item,” you can correct that before a single line of code is written.

Structured Output and Editable Plans

Once the Architect Agent has created a plan, the Theia IDE allows you to summarize and structure the plan into a formal task description, using LLM support behind the scenes.

This task plan is:

• Fully editable and user-controlled
• Easy to refine for clarity or changes in scope
• Reusable for coding agents or even full automation

You can also update the original task context file to reflect refinements, and re-run the summarization to regenerate the structured plan.

A Better Input for AI Agents—and for Humans

Once finalized, the task plan can be handed over to Theia Coder (or any other coding agent) for implementation. Because the plan is specific and detailed, the resulting code is far more accurate, and the review process is much shorter.

Task Context files are also a perfect foundation for autonomous AI agents that need all relevant information up front.

In short, Task Context provides:

✅ Structured, reliable AI interactions

✅ Higher-quality, reviewable AI-generated plans

✅ Built-in PR descriptions and test strategies

✅ Reusability across sessions, developers, and tools

Get Started with Task Context Today

If you’re tired of vague chat-based development workflows and want to unlock the full potential of AI assistants, Task Context offers a clear, structured, and powerful alternative.

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