I Switched from Manus to Codex — The Data Context Stayed the Same | AI Agent Workflow Demo

This demo showcases how AI agents can seamlessly switch between different frameworks (Manus to Codex) while maintaining consistent data context. The key insight is that agent architecture can be abstracted from the underlying implementation, allowing developers to swap frameworks without losing access to shared data structures. This approach enables flexible, interchangeable agent systems that prioritize context preservation over framework lock-in.

Key Points

• •Agent frameworks can be abstracted to allow switching between implementations (Manus → Codex) without losing functionality
• •Data context must be decoupled from agent framework to enable portability across different systems
• •Shared context layers enable multiple agents to work collaboratively with the same information state
• •Framework-agnostic context design reduces technical debt and vendor lock-in for agent systems
• •Consistent data structures across agents improve interoperability and reduce integration complexity
• •Context preservation during framework migration is critical for maintaining agent behavior and reliability
• •Modular agent architecture allows testing and swapping different implementations without rewriting core logic

Workflow Diagram

Concepts