Why We Rebuilt Microsoft Office From Scratch for AI Agents - Yijia Liang, Compound AI
Summary
The talk details the necessity of rebuilding Microsoft Office (Excel, PowerPoint, Word) from scratch to support advanced, reliable, and parallel AI agents for institutional finance. Current single-player, off-the-shelf AI tools fail to meet the demands of complex deal-team workflows, which require multiplayer collaboration, processing massive data rooms (15,000+ files), and handling large, complex models. The solution involves developing a headless engine that enables parallel execution, owns the entire 'write, render, check, fix' loop, and processes data far faster and more accurately than traditional methods.
Key takeaways
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Shift from Personal to Multiplayer Agents
5:12
Personal agents (like those used for booking flights) are insufficient for finance. Deal-team workflows require multiplayer agents that can share context across all involved parties, necessitating a built-in inbox structure for seamless collaboration within platforms like Outlook.
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Need for Parallel, Headless Processing
10:04
Existing off-the-shelf solutions (including desktop plugins and VMs) cannot scale to run multiple tasks in parallel. The custom headless engine is required to achieve significant performance gains (e.g., recalculating a 20MB Excel file 4x faster) and maintain reliability.
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Owning the Full Workflow Loop
12:18
By building a proprietary engine, Compound owns the entire 'write, render, check, and fix' loop. This ensures pixel-perfect fidelity (using Rust renders) and allows for rapid, iterative refinement of complex outputs, which is critical for financial trust.
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Handling Massive Context and Data Rooms
The system addresses data breadth (data rooms of 15,000+ files) using agent swarms and coding agents (via grep or similar methods) to slice contents and ground findings with citations. For data depth (e.g., 30MB models), the custom engine processes the data directly, providing a richer API set than open-source packages like OpenPyXL.
Technical details
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Engine Architecture
604s
The solution involves rebuilding Office from scratch using a headless engine approach. This allows for parallel task execution, bypassing the limitations of CPU-bound desktop applications or virtual machines.
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Development Effort
620s
The rebuild required running agents in many loops, spending 1.5 trillion input-output tokens and over 8 million lines of code to achieve full fidelity.
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Implementation Details
640s
The process involves using agents to recreate Office in full fidelity, treating the existing Office product as an 'oracle' and running iterative loops. The use of Rust renders ensures pixel-perfect parity with the downloaded Office product.
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Data Processing
For large data sets, the system utilizes agent swarms and coding agents to read files (e.g., via grep) and slice directory contents, ensuring every finding is grounded with source citations. For deep data processing, the custom engine exposes a richer set of APIs than open-source alternatives.
Mentioned resources
Channel & topics
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