Architecting Agentic Workflows: Building a Full-Stack AI Video Processing Engine with Cursor, MCP, and Next.js

The paradigm of software engineering is shifting from manual line-by-line implementation to Agentic Orchestration. In this new era, the developer's primary role is no longer just writing syntax, but designing high-level architectures, configuring environment contexts, and managing specialized tool integrations for AI agents. This post explores a professional workflow used to build an automated "AI Shorts" application—a system capable of ingesting long-form 16:9 video, performing transcription via Whisper, identifying viral segments using LLMs, and executing complex video transformations (reframing, cropping, and caption burning) via ImageKit.

Phase 1: High-Level Architecture and Pre-Computation

The most common failure point in AI-assisted development is "prompt-and-pray" coding—starting a project without a defined technical roadmap. A robust workflow begins with deep research and the creation of an architectural blueprint.

Before writing any code, I utilized Claude Code to perform competitive analysis and tech stack selection. By feeding high-level requirements into the model, I generated a comprehensive slideshow that outlined:

1. The Core Logic: Identifying segments within a transcript using LLM reasoning.
2. The Transformation Pipeline: Utilizing URL-based transformations for video manipulation.
3. The Tech Stack: Selecting Next.js for the web framework, Grok's Whisper (large v3 turbo) for high-speed transcription, and ImageKit for edge-based video processing.

This phase ensures that when we move to the coding environment, the "context" is already established. We aren't just asking an AI to "build a tool"; we are providing it with a pre-validated architectural document.

Phase 2: Configuring the Agentic Environment

To build effectively, your IDE must be more than a text editor; it must function as an Agentic Controller. I utilize Cursor, specifically leveraging its advanced agentic features and coding harness.

Model Selection and Context Management

The choice of LLM is task-dependent. For initial scaffolding and complex reasoning, I deploy frontier models like Claude Opus 4.8 (or the latest available Claude model). However, for large-scale refactors or UI-specific adjustments, switching to faster, more specialized models like Composer 2.5 or even GPT-based models can optimize both latency and cost.

Key configuration parameters in my Cursor setup include:

• Context Window: Set to 1 million tokens to ensure the agent maintains a holistic view of the entire codebase.
• Mode Selection: Utilizing "Fast Mode" for rapid iterations and "Thinking Mode" for complex logic implementation.
• Agent Windows: Using side-by-side agent views that allow for real-time monitoring of file writes, rather than isolated chat interfaces.

The Power of MCP (Model Context Protocol)

The most significant advancement in this workflow is the integration of MCP Servers. Rather than manually copying documentation into a prompt, I use MCP to grant the AI "agent skills."

By modifying .cursor/mcp.json, I can inject specialized capabilities directly into the agent's toolset:

• ImageKit MCP Server: This allows the agent to interact with the ImageKit API, manage media libraries, and understand transformation parameters (like face alignment and adaptive bitrate streaming) without human intervention.
• GitHub MCP Server: Enables the agent to handle repository creation, branching, and automated commits.

By installing these skills via terminal commands, we transform the LLM from a passive text generator into an active participant in the development lifecycle.

Phase able: The Implementation Loop (Iterative Debugging)

The implementation phase is rarely linear; it is an iterative loop of Prompt $\rightarrow$ Execute $\rightarrow$ Observe $\rightarrow$ Debug.

Automated Rule Injection

To maintain code quality, I implement a rules/ directory within the project. Using Cursor's ability to follow project-specific instructions, I define rules that mandate:

• Atomic Commits: Every major feature or bug fix must be committed via the GitHub MCP server.
• Standardized Patterns: Ensuring all API routes in Next.js follow a specific structure for error handling and logging.

Debugging with Visual and Log-Based Feedback

When the agent encounters an error—for example, a failure in the transcription pipeline where the video uploads to ImageKit but the Whisper processing fails—the debugging process is highly structured.

I do not simply report "it's broken." I provide:

1. The Error Trace: Copying the exact stack trace from the terminal.
2. Contextual Verification: Confirming that the upstream task (e.g., ImageKit upload) was successful, which narrows the search space for the agent.
3. Visual Debugging: Taking screenshots of the UI and feeding them back to the model. Modern multimodal models can "see" layout shifts or missing captions, allowing them to identify CSS or transformation parameter errors that are invisible in raw code logs.

Advanced Video Transformations via URL Parameters

The technical "magic" of this application lies in leveraging ImageKit's transformation engine. Instead of complex server-side FFmpeg processing, the agent is instructed to manipulate URL query parameters. This allows for:

• Face Reframing: Using ai_face_detection to automatically crop 16:9 video into a 9:16 vertical format by centering on the subject.
• Caption Burning: Utilizing ImageKit's ability to overlay text layers onto the video stream.
• Adaptive Bitrate Streaming: Ensuring high-quality playback across different network conditions via automated optimization.

Conclusion

Building complex, production-ready applications with AI requires moving beyond simple prompting and into the realm of Environment Engineering. By focusing on robust planning, implementing MCP servers for tool integration, and maintaining a structured debugging loop, we can leverage agentic models to build sophisticated full-stack systems in a fraction of the traditional development time.