Automating Handoff Docs with AI

For our latest 48-hour hackathon, our team focused on a persistent pain point: the creation of client handoff documentation. This essential, project-closing work, documenting the build, deployment, and operation, is often neglected. Our core challenge was simple: What if we could point an automated tool at our project assets and instantly generate a complete handoff document?

What We Built

We developed a system that generates a structured handoff document from key project assets. Users upload a Statement of Work (SOW) PDF and provide GitHub repository URLs. The system then synthesizes this information into a comprehensive document that includes an overview, architecture, deployment instructions, and a user guide.

The Pipeline:

• Frontend: A React application.
• Backend: An AWS Step Function pipeline that executes two parallel processing tracks:
1. SOW Document Analysis: Dedicated analysis of the Statement of Work.
2. GitHub Repo Exploration: Deep exploration of the codebase.
• Final Output: Both tracks feed into a final agent responsible for writing the structured document.

Key Architectural Insights

The parallel processing tracks and the multi-agent system design were the most interesting aspects of the build.

Multi-Agent Architecture (Code Repository Side):

We implemented a two-agent pattern using the Strands Agents SDK and Bedrock:

1. Triage Agent: Explores the repository with a constrained tool budget, forcing it to be highly selective about the code it reads.
2. Synthesis Agent: Takes the curated output from the Triage Agent and synthesizes the information into structured knowledge.

This focused, two-agent approach yielded significantly better results than a single, monolithic agent.

Document Processing (SOW Side):

To overcome common text extraction issues with complex PDF layouts and tables, we adopted a more robust method:

• We utilized Poppler to convert PDF pages into images.
• A vision model (Nova Omni via Bedrock) was then paired with the images to reason about the visual content, resulting in a quality leap for structured document processing.

Design & Alignment:

Mermaid diagrams were a lifesaver. When you're designing a pipeline with parallel branches and multiple AI handoffs, being able to sketch it in markdown and see it render instantly kept us aligned without slowing down. Virtual whiteboard sessions turned into "wait, what does that agent actually do?" moments, and the diagrams just answered it.

What We Learned

A few lessons stood out during the hackathon:

• LLMs are powerful but opinionated about their output. We spent real time building guardrails: strict JSON-only prompts, preamble stripping, retry logic. Without that, the agents would happily write you a paragraph of commentary before the JSON you actually asked for.
• The two-agent pattern (gather then synthesize) produced better results than a single agent trying to do both would. Constraints made the output better, not worse.
• And the big one: vision models for document processing. Sending page images instead of extracted text was a quality leap for structured documents like SOWs.

Next Steps

In classic hackathon fashion, some edges were rough, but the core idea proved itself. We'd want to tighten up repo authentication, handle more PDF edge cases, and let teams edit the output instead of treating it as final. But the foundation works, and we're already pulling techniques from this into our regular projects.

Hackathons are the right place to try things like this. You get to take a real annoyance, throw some new tools at it, and see what sticks. Not everything was polished, but we left with a proven concept and a few techniques worth keeping.