Writing comprehensive review articles is a cornerstone of knowledge work across academia, industry, and research teams everywhere. The process typically involves extensive research—reading dozens of papers, articles, and sources across the web—followed by the challenging task of synthesizing everything into a coherent narrative. It’s time-intensive work that often requires entire teams and weeks of effort.

This seems like an ideal candidate for multi-agent AI systems, and indeed, solutions are rapidly emerging in this space—from research tools like Elicit and Consensus that help with literature discovery and synthesis, to AI writing assistants like Notion AI and Gamma that can generate structured content from research inputs, and specialized academic tools like SciSpace and Scholarcy that focus on research paper summarization.

However, building such a system reveals layers of complexity that aren’t immediately apparent. This series documents the challenges, nuances, and design decisions involved in developing a complete AI review article writer, with the understanding that the most effective solution will ultimately be deeply personal—tailored to your specific needs, sources, topics, and workflow.

The Problem

Given: A research topic, target audience, and page limit Goal: Generate a complete review article using only web searches as the information source Output: A properly formatted LaTeX document with bibliography, ready for compilation to PDF

This seemingly straightforward problem statement conceals layers of complexity that make it a fascinating case study in multi-agent AI systems.

Why This Problem Matters

Review articles serve a critical function in academia and industry:

• Knowledge Synthesis: They distill vast literature into accessible summaries
• Gap Identification: They highlight areas needing further research
• Educational Value: They provide comprehensive introductions to complex topics
• Decision Support: They inform policy makers, researchers, and practitioners

Automating this process could democratize access to high-quality literature reviews, particularly valuable for emerging fields where comprehensive reviews may not yet exist.

The Complexity Challenge

What makes this problem particularly challenging:

1. Information Discovery

• Web search results vary significantly in quality and relevance
• Academic sources require different search strategies than general web content
• Temporal relevance matters - recent developments often supersede older work
• Source credibility evaluation is subjective and context-dependent

2. Content Organization

• Logical flow requires understanding conceptual relationships
• Section organization must serve the target audience’s needs
• Subsection granularity affects readability and comprehensiveness
• Cross-references and connections between topics add complexity

3. Quality Control

• Academic writing standards are strict and domain-specific
• Citation accuracy is non-negotiable
• LaTeX formatting must be syntactically correct
• Bibliography entries require consistent formatting

4. Scalability Constraints

• Page limits constrain information density
• Word count allocation across sections affects balance
• Processing time must remain reasonable for practical use
• Token limits in LLMs create natural boundaries

Real-World Requirements

From analyzing the implementation, several key requirements emerge:

Functional Requirements:

• Extract topic, audience, and constraints from natural language input
• Generate structured table of contents through web research
• Write sections sequentially with proper academic style
• Compile valid LaTeX with proper bibliography formatting
• Support human feedback loops for quality control

Quality Requirements:

• Citations must be accurate and properly formatted
• Content should be factually correct and up-to-date
• Writing style must match target audience expectations
• LaTeX output must compile without errors

Performance Requirements:

• Reasonable processing time (minutes, not hours)
• Efficient use of LLM tokens and API calls
• Caching to avoid redundant work
• Graceful handling of search failures

The Multi-Agent Approach

The solution employs a multi-agent architecture using LangGraph, with specialized agents for:

• Topic Extraction: Understanding user requirements
• TOC Generation: Structuring the review through web research
• Section Writing: Producing content with citations
• Quality Assurance: LaTeX and bibliography validation
• Compilation: Final document generation

This decomposition allows each agent to focus on a specific aspect of the problem while maintaining coordination through a shared state.

Success Metrics

How do we know if the system succeeds?

Technical Metrics:

• LaTeX compilation success rate
• Citation accuracy percentage
• Processing time per page
• Cache hit ratios

Quality Metrics:

• Content coherence (human evaluation)
• Appropriate depth for target audience
• Logical section organization
• Bibliography completeness

User Experience Metrics:

• Time from request to final PDF
• Number of human interventions required
• User satisfaction with final output

What’s Coming Next

In the following posts, we’ll explore the journey of building such a system:

1. Overall Strategy: How to break down this complex problem into manageable pieces
2. Building the Skeleton: Creating structure through research and planning
3. The Research Engine: Managing autonomous information gathering
4. Content Generation: Writing comprehensive sections with proper quality control
5. Quality Assurance: Ensuring academic standards and proper formatting
6. Final Assembly: Bringing everything together into publication-ready output

The goal isn’t to present the “perfect” solution, but rather to illuminate the challenges you’ll encounter and the trade-offs you’ll need to make when building your own version. Whether you’re a researcher looking to automate literature reviews, a consultant synthesizing industry reports, or a team lead creating comprehensive documentation, understanding these complexities will help you design a system that truly serves your specific needs.

Every organization has different sources they trust, different formats they prefer, and different quality standards they require. This series aims to give you the foundation to build something that works for your unique context.