Stop Chatting with AI: How I Built an Autonomous RFP Response System for Business

From "Chatbot" to "Digital Worker": A Solo Experiment in Automating the Boring Stuff

TL;DR

1. The Problem: RFPs (Requests for Proposals) are tedious, repetitive, and require searching through hundreds of pages of technical docs.
2. The Solution: I built a multi-agent system that reads RFPs, finds technical answers, and drafts compliant responses automatically.
3. The Code: Open source Python project using LangChain concepts (but simplified) and a mock Vector DB.
4. The Result: A workflow that turns a PDF into a draft proposal in seconds, not days.
5. GitHub Repo: autonomous-rfp-agent

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Introduction

I recently found myself staring at a 150-page Request for Proposal (RFP) document. It was asking the same questions I've answered a dozen times: "How do you handle encryption?", "What is your uptime SLA?", "Describe your API documentation."

I thought to myself: Why am I manually typing this?

We often treat AI as a chat partner. We ask it a question, it gives an answer. But for real business problems, I realized I didn't need a chatbot; I needed a worker. I needed something that could read the document, understand specific requirements, go look up the "official" technical answer from my company's knowledge base, and then write a professional response.

In this article, I share my journey of building The Autonomous RFP Response System. It’s a Proof of Concept (PoC) designed to show how agentic workflows can solve boring, high-value business problems.

What's This Article About?

This isn't about "talking" to an LLM. It's about engineering a system where AI agents collaborate to finish a task.

I observed that most AI tutorials focus on generic "personal assistants." I wanted to build something for the enterprise context. Specifically, I wanted to automate the Bid Management process.

I built a system where:

1. Agent A (The Reader) digests the complex RFP.
2. Agent B (The Researcher) hunts down the facts.
3. Agent C (The Writer) drafts the persuasive text.
4. Agent D (The Manager) reviews it for compliance.

Tech Stack

For this experiment, I kept things lean and Pythonic:

1. Python 3.10+: The lingua franca of AI engineering.
2. Custom Agent Classes: I didn't use a heavy framework like CrewAI or LangGraph for this PoC because I wanted to show the logic clearly. I wrote simple Python classes to represent agents.
3. Mock Vector Database: To simulate RAG (Retrieval Augmented Generation) without needing an OpenAI key for embeddings in this demo, I built a keyword-based retrieval system.
4. Mermaid.js: For all the beautiful diagrams you see here.

Why Read It?

1. Practicality: This is a real use case. Every B2B company deals with RFPs.
2. Architecture: I break down exactly how to structure a multi-agent loop.
3. Code: You get full access to the source code to try it yourself.

Let's Design

Before writing a single line of code, I grabbed a digital whiteboard to map out the flow. I knew I needed a linear process with a "feedback loop" for quality control.

Here is the high-level architecture I came up with:

1. Input Processing: The system takes a raw file (simulated here with text extraction).
2. The "Brain": A central Orchestrator that passes state between agents.
3. The Feedback Loop: If the "Compliance Officer" agent rejects a draft, the "Proposal Drafter" must try again. This is crucial—autonomous agents need to be able to self-correct.

The sequence of operations looks like this:

I decided to design the system as a State Machine. The "State" is the current requirement being processed and the draft associated with it. Each agent modifies this state.

Let’s Get Cooking

Now, let's dive into the code. I'll walk you through the core components.

1. The Document Processor (The "Reader")

First, I needed a way to ingest the RFP. In a production system, I would use OCR (like AWS Textract). For this PoC, I simulated the extraction logic.

class DocumentProcessor:
    """
    Simulates the ingestion of comprehensive RFP documents.
    """
    def process_rfp(self, file_path: str) -> List[str]:
        self.logger.info(f"Processing RFP document: {file_path}")

        # Simulating extraction of complex requirements
        requirements = [
            "REQ-001: System must support 10,000 concurrent users.",
            "REQ-002: Data must be encrypted at rest using AES-256.",
            "REQ-003: Uptime SLA must be 99.99%."
        ]
        return requirements

My thought process: I kept this simple to focus on the agent interaction rather than PDF parsing libraries, which are notoriously finicky.

2. The Context Retriever (The "Researcher")

This is the RAG (Retrieval Augmented Generation) component. When the system sees "Encryption," it shouldn't hallucinate an answer. it should look up our standard security protocols.

class ContextRetriever:
    """
    Simulates a Vector Database retrieval system (RAG).
    """
    def __init__(self):
        # Mock knowledge base representing company "facts"
        self.knowledge_base = {
            "scale": "Our architecture uses Kubernetes auto-scaling...",
            "security": "We implement AES-256 GCM encryption...",
        }

    def retrieve(self, query: str) -> str:
        # In prod, this would be a vector similarity search
        if "users" in query: return self.knowledge_base["scale"]
        if "encrypt" in query: return self.knowledge_base["security"]
        return "Generic context."

In my experience, this step is where most "AI Writers" fail. They write beautiful text that is factually wrong. By forcing a retrieval step, I ground the AI in reality.

3. The Orchestrator (The "Boss")

This is where the magic happens. The Orchestrator ties everything together. It loops through requirements and manages the "Review -> Revise" cycle.

class RFPOrchestrator:
    def run(self, rfp_path: str):
        requirements = self.loader.process_rfp(rfp_path)

        for req in requirements:
            # 1. Retrieve Facts
            context = self.retriever.retrieve(req)

            # 2. Draft Response
            draft = self.drafter.draft_section(req, context)

            # 3. Compliance Loop
            approved = False
            while not approved:
                is_valid, feedback = self.compliance.review_draft(draft)
                if is_valid:
                    approved = True
                else:
                    # Self-Correction happens here
                    draft = self.drafter.revise(draft, feedback)

I designed this loop specifically to mimic how human teams work. A junior writer drafts, a senior manager reviews, and corrections are made before the client sees it.

Let's Setup

If you want to run this experiment on your machine, I made it very easy.

1. Clone the Repo:
   

   git clone https://github.com/aniket-work/autonomous-rfp-agent.git
   cd autonomous-rfp-agent
   

2. Install dependencies:
   

   pip install -r requirements.txt
   

3. Run the demo:
   

   python main.py
   

Let's Run

When I ran the system, watching the logs was incredibly verifying. It felt like watching a digital assembly line.

Here is the output from my terminal:

[Orchestrator] 🚀 Starting RFP Process for sample_rfp.pdf
[DocumentProcessor] Extracted 5 requirements.
--- Processing Requirement 1: 10,000 concurrent users ---
[ContextRetriever] Searching knowledge base... found 'Kubernetes auto-scaling'
[ProposalDrafter] Drafting response...
[ComplianceOfficer] Reviewing draft...
[ComplianceOfficer] Draft approved.

And in one case, I saw the self-correction in action:

[ComplianceOfficer] Warning: Forbidden term 'guarantee 100%' found.
[Orchestrator] Iterating on draft due to feedback...
[ComplianceOfficer] Draft approved.

The system caught a liability ("guarantee 100% uptime") and fixed it automatically. That is the power of agentic workflows.

Closing Thoughts

Building this Autonomous RFP system taught me a few things:

1. Structure > Prompting: Defining the workflow (Reader -> Researcher -> Writer) was more important than the specific prompt I gave the LLM.
2. Business Logic Matters: The "Compliance Officer" agent was just a set of business rules, but it added immense value by preventing hallucinations.
3. Agents are the future of work: Moving from "chatting" to "delegating" is a mindset shift that unlocks massive productivity.

I hope this inspires you to stop just chatting with AI and start building your own digital workforce.

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Tags: ai, python, machinelearning, architecture

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The views and opinions expressed here are solely my own and do not represent the views, positions, or opinions of my employer or any organization I am affiliated with. The content is based on my personal experience and experimentation and may be incomplete or incorrect. Any errors or misinterpretations are unintentional, and I apologize in advance if any statements are misunderstood or misrepresented.