Introduction
Creating a Statement of Work (SoW) is a critical step in project management. It defines the project's scope, deliverables, and timeline, ensuring clarity between all parties involved. However, writing an SoW from scratch is time-consuming and requires a lot of manual effort.
This is where Agentic AI comes in. By leveraging advanced AI agents, we automate and streamline the SoW creation process, improving accuracy, efficiency, and consistency. In this blog, we’ll explain what Agentic AI is, how we use it to generate SoWs, and the benefits it brings to businesses.
Understanding Agentic AI in Document Generation
What is Agentic AI?
Agentic AI refers to autonomous systems that can set goals, make decisions, and execute tasks with minimal human intervention. Unlike traditional AI models that only generate responses, Agentic AI takes action - it can adapt, persist through challenges, and break complex tasks into manageable steps.
An individual AI agent is a self-contained unit that performs a specific function. Multiple agents can work together, collaborating to complete complex workflows efficiently.
How our AI System works: an overview
The core goal of our AI system is automated SoW generation. Given a project description and a predefined template, the system creates a structured document with accurate details.
If a project lacks a detailed description or a template, our AI can generate them automatically, ensuring consistency across documents.
The AI Agents involved
Our system consists of multiple specialized AI agents, each responsible for a specific part of the SoW creation process.
Project Description Agent
Many projects do not have a single, well-defined document that describes all aspects of the work. Our Project Description Agent scans various sources - emails, scanned documents, meeting notes - and generates a structured project summary.
This allows us to create a complete and accurate project overview, regardless of the format of the original data.
SoW Generation Agent
This process involves two AI agents working together:
Placeholder Detection Agent: Identifies placeholders in the provided template. Customers may use different strategies to indicate placeholders in their templates. Some may use explicit tags such as [placeholder] or {insert here}, while others may underline text, leave blanks or use subtle clues such as incomplete sentences. In some cases, they may not tag placeholders at all, expecting missing sections to be deduced from the structure and context of the document.
Instead of relying on simple text markers, our agent analyzes the document as a whole, recognizing missing content based on language patterns, section structure and project-specific details. This ensures a reliable completion process, regardless of how replacement sections are indicated.
Content Generation Agent: Fills in the placeholders with relevant project details, ensuring the document remains structured and professional.
We do not generate the entire SoW from scratch to maintain the original document formatting and ensure compliance with client expectations.
3. Template Generation Agent
If a client does not provide a template, our Template Generation Agent creates a standardized format based on past project documents.
This template can then be reused in the same way as a client-provided template, ensuring consistency across different projects.
4. Document Retrieval Agent (RAG)
To improve accuracy and quality, we use a Retrieval-Augmented Generation (RAG) agent that finds relevant documents.
It analyzes past project documents by converting them into numerical representations using embeddings. These embeddings are stored in a vector database, allowing for efficient semantic search and retrieval.
When creating a Statement of Work (SoW), the system compares the current project description with past documents and retrieves the most relevant ones.
This ensures that the generated SoW is aligned with previous work, reducing errors and improving consistency.
5. Review Agent
After generating the SoW, a Review Agent checks for:
Inconsistencies
Formatting issues
Missing or incorrect details
It generates a short report highlighting potential errors, allowing users to review and refine the document before finalization.
Enhancing SoW Generation with context and data
Our AI-driven approach ensures high-quality SoWs by leveraging various data sources:
Internal Management Systems: Pulling project details from company databases.
Client documents: Identifying and filling placeholders with accurate information.
Previous work: Ensuring consistency with past projects for the same client or similar scopes.
By integrating all these elements, our AI ensures the generated SoW is accurate, comprehensive, and aligned with business needs.
Benefits of an AI-Driven approach
Using Agentic AI for SoW creation provides multiple advantages:
Increased efficiency – Automating repetitive tasks reduces manual effort and speeds up document creation.
Improved accuracy – AI ensures consistent and error-free documentation.
Faster turnaround – Clients receive ready-to-use SoWs much quicker than traditional methods.
Adaptability – The AI adapts to different industries and project types without extensive retraining.
Integration - plans and design
Our AI-powered SoW generator is not just an experiment or a research project - it’s a fully functional product designed to deliver real value to businesses.
To ensure seamless deployment and scalability, we use FastAPI, Kubernetes, Docker, and MLOps as the foundation of our architecture. This allows us to offer our model-as-a-service (MaaS) solution, enabling multiple companies to integrate and benefit from automated SoW generation with minimal setup.
Our first implementation is within our own internal management system, HULA, demonstrating the solution's practicality in real-world scenarios. Additionally, our model supports multi-tenancy, ensuring that different organizations can securely and efficiently utilize the service without conflicts.
Challenges and considerations
While AI-driven SoW generation is highly effective, there are some challenges to consider:
Handling sensitive data – Ensuring compliance with data security regulations.
Balancing automation with human oversight – AI generates the document, but human review is still essential.
Dealing with ambiguous input – AI may struggle with incomplete project descriptions, requiring human intervention.
Future of Agentic AI in Document Automation
As we continue to enhance our AI-driven SoW generation, our next step is to expand automation beyond document creation and into intelligent project planning. We are developing systems to automatically propose resource allocation, project timelines, and rate estimates tailored to each client.
AI agent can analyze client history, past projects and current trends to:
Recommend resource allocation, selecting right team members with skill sets aligned with project requirements.
Generate estimated project timelines, taking into account key milestones and workload capacity.
Estimate pricing, considering historical client agreements.
Conclusion
Key takeaways:
Agentic AI transforms SoW creation, reducing manual effort and improving accuracy.
Specialized AI gents handle different tasks, ensuring high-quality documents.
The system retrieves relevant project data, maintaining consistency with past work.
Businesses can benefit from faster turnaround times and greater efficiency.
As AI continues to evolve, automated document generation will become a standard practice, allowing professionals to focus on higher-value tasks rather than manual paperwork.