Building an AI-Powered Idea Qualification Agent with AgentSpace and Google Workspace

Problem Solved: Inconsistent idea quality from BOD and staff leads to wasted development efforts and increasing technical debt within Innoflow. The manual qualification process is slow and often subjective.

Solution Overview: This hands-on tutorial will guide you through building a custom AI agent on AgentSpace that acts as ""Innovation Architect"". This agent will intelligently ingest new ideas (submitted via Google Docs), rigorously assess them against predefined strategic and technical criteria (stored in Google Sheets and internal documentation in Drive), identify potential technical debt risks, and provide structured, actionable feedback, all automated and integrated seamlessly into your existing Google Workspace environment

The Problem: The High Cost of Bad Ideas

At Innoflow, we thrive on innovation. Ideas bubble up from every corner of the company—from the Board of Directors with a strategic vision to a junior developer with a brilliant technical insight. But this firehose of creativity presents a challenge: inconsistency.

For every game-changing proposal, there are a dozen that are misaligned with our strategy, technically unfeasible, or simply a rehash of a past failure. These ideas can lead to:

• Wasted Development Cycles: Engineering teams spend weeks on projects that are ultimately shelved.

• Rising Technical Debt: Rushed, ill-conceived features introduce fragile code that will haunt us for years.

• Slow, Subjective Qualification: Our manual review process, led by a few senior architects, is a bottleneck. It’s slow, and the feedback can be inconsistent depending on who is reviewing the idea.

We needed a way to filter the signal from the noise—a process that was fast, objective, data-driven, and scalable.

The Solution: The “Innovation Architect” AI Agent

This hands-on tutorial will guide you through building a custom AI agent on a platform like AgentSpace. We’ll call it the “Innovation Architect.”

This agent will intelligently:

1. Ingest new ideas submitted via a standardized Google Doc template.

2. Assess the idea against strategic goals and technical criteria defined in a Google Sheet.

3. Analyze it for feasibility by cross-referencing our internal technical documentation and past project post-mortems stored in Google Drive.

4. Provide structured, actionable feedback directly in the original Google Doc, including a quantitative score and a qualitative analysis.

All of this is automated and seamlessly integrated into our existing Google Workspace environment.

Prerequisites

Before we start, you’ll need a few things set up:

1. AgentSpace Account: A platform to build, host, and manage our agent.

2. Google Cloud Project: With the Google Drive API, Google Sheets API, and Google Docs API enabled.

3. Google Service Account: This is the best way to grant programmatic, secure access to our Google Workspace resources. Download the credentials.json file.

4. Shared Google Drive Folder: This will be the home for all our agent’s resources. Share this folder with your service account’s email address.

Step 1: Setting Up the Knowledge Base (The Agent’s Brain)

An AI agent is only as smart as the data it can access. We’ll give our Innovation Architect three sources of truth within our shared Google Drive folder.

A. The Qualification Criteria (Google Sheets)

Create a Google Sheet named Idea_Qualification_Criteria. This provides a structured, weighted scoring system.

B. The Idea Submission Template (Google Docs)

Create a Google Doc named [TEMPLATE] New Idea Submission. New ideas will be submitted by making a copy of this document.

Title: [Clear, descriptive title of the idea]

Author: [Your Name]

Date: [Date of Submission]

1. Problem Statement

What specific problem are we solving? Who are we solving it for?

2. Proposed Solution

Describe your solution in detail. How does it work from a user’s perspective?

3. Technical Approach (Optional)

How might we build this? What technologies, systems, or APIs would be involved?

[DO NOT EDIT BELOW THIS LINE - AI AGENT ANALYSIS]

C. Internal Documentation (Google Drive)

In the same shared folder, add relevant internal documents. The agent will use these for deeper context.

• Innoflow_Technical_Standards.pdf

• Post-Mortem_Project_Helios.md (A document detailing why a previous, similar project failed)

• API_Gateway_Documentation.docx

Step 2: Building the Agent in AgentSpace

Now, let’s head over to your agent-building platform. The core components are the Tools it can use and the Prompt that governs its behavior.

1. Create a New Agent: Name it “Innovation Architect”.

2. Connect Tools:

   • Google Drive Reader: Authenticate using your credentials.json. This tool allows the agent to list and read files from your shared Drive folder.

   • Google Sheets Reader: Configure it to read our Idea_Qualification_Criteria sheet.

   • Google Docs Reader/Writer: This allows the agent to both read the idea submission and write its analysis back to the same document.

3. Craft the Master Prompt: This is the most critical step. It’s the set of instructions that the AI follows. It tells the agent who it is, what its goal is, and how to perform its task.

You are the "Innovation Architect," a senior AI assistant at Innoflow. Your personality is analytical, objective, and constructive. Your purpose is to provide a rigorous, data-driven first-pass qualification of new ideas to save senior human architects' time.

### CONTEXT ###

You have access to a Google Drive folder containing:

1.  A Google Sheet named `Idea_Qualification_Criteria` which contains the weighted scoring model.

2.  Internal documents like technical standards, post-mortems of past projects, and API documentation.

3.  The user submitted an idea in a Google Doc.

### TASK ###

Your task is to analyze the provided Google Doc idea submission. You must perform the following steps:

1.  **Read the Idea:** Ingest the content of the user-submitted Google Doc.

2.  **Retrieve Criteria:** Load the scoring criteria and weights from the `Idea_Qualification_Criteria` Google Sheet.

3.  **Consult Knowledge Base:** Scan the other documents in the Google Drive folder for relevant context. Pay close attention to technical standards and lessons from past project post-mortems.

4.  **Score the Idea:** For each criterion in the sheet, assign a score from 1 (poor fit) to 10 (excellent fit). Calculate the final weighted score.

5.  **Write a Qualitative Analysis:** Provide a detailed analysis covering:

    - **Strengths:** What makes this idea promising?

    - **Weaknesses & Risks:** What are the major concerns? Explicitly mention any potential for creating technical debt, conflicts with our documented technical standards, or similarities to past failed projects.

    - **Actionable Feedback:** What are the immediate next steps or open questions the author should address?

6.  **Format the Output:** Structure your entire analysis in Markdown and append it to the original Google Doc below the line that says "[DO NOT EDIT BELOW THIS LINE - AI AGENT ANALYSIS]".

### OUTPUT FORMAT ###

**--- AI ANALYSIS: INNOVATION ARCHITECT ---**

#### **Overall Score: [Calculated Weighted Score]/100**

#### **Qualification Scorecard**

| Criterion | Weight | Score (1-10) | Justification |

|---|---|---|---|

| Strategic Alignment | 30% | [Score] | [Your reasoning based on the idea and Innoflow's strategy] |

| Market Impact | 20% | [Score] | [Your reasoning] |

| Technical Feasibility | 25% | [Score] | [Your reasoning, citing specific technical documents if relevant] |

| Resource Requirement | 15% | [Score] | [Your reasoning] |

| Technical Debt Risk | 10% | [Score] | [Your reasoning, explicitly mention risks] |

#### **Qualitative Analysis**

**Strengths:**

- [Bulleted list of strengths]

**Weaknesses & Risks:**

- **Technical Debt:** [Analysis of potential tech debt. E.g., "This proposal to use a new database technology (Mongo) deviates from our standard of using Postgres and could increase maintenance overhead."]

- **Strategic Misalignment:** [Analysis of strategic fit.]

- **Past Lessons:** [Analysis based on post-mortems. E.g., "This idea is conceptually similar to 'Project Helios,' which failed due to underestimating the complexity of third-party API integration. The same risk applies here."]

**Actionable Feedback & Next Steps:**

1.  **Question:** [A specific question for the author to clarify.]

2.  **Suggestion:** [A suggestion for improving the proposal.]

Step 3: Under the Hood - How the Code Works (Illustrative Example)

While AgentSpace abstracts away the code, it’s helpful to understand what’s happening. Here’s a simplified Python script showing how an agent might use Google’s APIs to perform this task.

# Note: This is a simplified, illustrative script.

# In a real agent platform, this logic is handled by pre-built tools and workflows.

from google.oauth2 import service_account

from googleapiclient.discovery import build

import gspread # A library to make Sheets easier to work with

# --- 1. AUTHENTICATION ---

SCOPES = ['https://www.googleapis.com/auth/drive', 'https://www.googleapis.com/auth/documents']

SERVICE_ACCOUNT_FILE = 'path/to/your/credentials.json'

creds = service_account.Credentials.from_service_account_file(

    SERVICE_ACCOUNT_FILE, scopes=SCOPES)

# Build the service clients

drive_service = build('drive', 'v3', credentials=creds)

docs_service = build('docs', 'v1', credentials=creds)

gc = gspread.service_account(filename=SERVICE_ACCOUNT_FILE)

# --- 2. FETCHING THE KNOWLEDGE BASE ---

def get_idea_document_content(doc_id):

    """Reads the text from a Google Doc."""

    document = docs_service.documents().get(documentId=doc_id).execute()

    content = document.get('body').get('content')

    # This is a simplification; you'd parse the structured content

    return "".join([elem.get('paragraph').get('elements')[0].get('textRun').get('content') 

                    for elem in content if elem.get('paragraph')])

def get_qualification_criteria():

    """Reads the criteria from the Google Sheet."""

    spreadsheet = gc.open("Idea_Qualification_Criteria")

    worksheet = spreadsheet.sheet1

    return worksheet.get_all_records() # Returns a list of dictionaries

# --- 3. THE "AGENT" LOGIC (Simulated) ---

def run_innovation_architect(idea_doc_id):

    # Get all inputs

    idea_text = get_idea_document_content(idea_doc_id)

    criteria = get_qualification_criteria()

    

    # In a real scenario, you'd also fetch text from other Drive files.

    # For now, we'll just use the idea and criteria.

    # This is where the magic happens: The call to the Large Language Model

    # The `master_prompt` would be formatted with the fetched data.

    # llm_client = ... (e.g., OpenAI, Anthropic, Google)

    # master_prompt = f"""[The full master prompt from above]

    #

    # --- INPUT DATA ---

    # Idea Submission: {idea_text}

    # Qualification Criteria: {criteria}

    # """

    # analysis_result = llm_client.generate(master_prompt)

    

    # Let's simulate the LLM's output for this example

    analysis_result = """

**--- AI ANALYSIS: INNOVATION ARCHITECT ---**

#### **Overall Score: 73/100**

#### **Qualification Scorecard**

| Criterion | Weight | Score (1-10) | Justification |

|---|---|---|---|

| Strategic Alignment | 30% | 8 | Aligns well with Q3 OKR for 'Improving Customer Onboarding'. |

| Market Impact | 20% | 7 | Addresses a common pain point for our mid-market customers. |

| Technical Feasibility | 25% | 6 | Feasible, but relies on a new external API we have not used before. |

| Resource Requirement | 15% | 7 | Estimated as a small, 1-sprint project for a 2-person team. |

| Technical Debt Risk | 10% | 5 | The use of a new, untrusted external API introduces risk and a potential maintenance burden. This contradicts our 'Innoflow_Technical_Standards.pdf' which favors vetted, in-house solutions. |

#### **Qualitative Analysis**

**Strengths:**

- Directly addresses a known customer issue with a simple user-facing solution.

- Low resource requirement makes it an attractive "quick win" candidate.

**Weaknesses & Risks:**

- **Technical Debt:** The core weakness is the reliance on the "NewExternalAPI". This would be the first service of its kind in our stack, increasing operational complexity and creating a new point of failure.

- **Past Lessons:** This is reminiscent of 'Project Helios', where we also underestimated the reliability issues of a sole-source third-party dependency.

**Actionable Feedback & Next Steps:**

1.  **Question:** Can you research and propose an alternative solution that does not rely on "NewExternalAPI" or, failing that, provide a more robust plan for testing, monitoring, and failover for this service?

2.  **Suggestion:** Create a small Proof of Concept (PoC) to validate the reliability and performance of the external API before committing to a full project.

"""

    # --- 4. WRITING THE RESULT BACK TO THE DOC ---

    # Find the line to insert after

    # In a real app, you'd search for the "[DO NOT EDIT...]" line's index

    insert_index = 350 # Simplified placeholder index

    

    requests = [

        {

            'insertText': {

                'location': {

                    'index': insert_index,

                },

                'text': analysis_result

            }

        }

    ]

    

    docs_service.documents().batchUpdate(

        documentId=idea_doc_id, body={'requests': requests}).execute()

    print(f"Successfully analyzed and updated document: {idea_doc_id}")

# Example usage:

# run_innovation_architect('your_google_doc_id_here')

Step 4: The Final Workflow and Result

With the agent built, the workflow is beautifully simple:

1. An employee has an idea. They go to the shared Drive, copy [TEMPLATE] New Idea Submission, rename it (e.g., Idea - Automated Onboarding Checklist), and fill it out.

2. Trigger: An automation rule (which can be set up in AgentSpace or using other tools like Zapier/Make) detects a new file in the “Ideas” folder.

3. Execution: The “Innovation Architect” agent is invoked. It reads the doc, the sheet, and the other knowledge files.

4. Output: A few moments later, the employee sees the original Google Doc has been updated with the agent’s complete, structured analysis.

The result is a revolution in our innovation process. We’ve moved from a slow, subjective bottleneck to a rapid, data-driven system that empowers everyone in the company to contribute high-quality, well-vetted ideas. Our senior architects are freed from routine screening and can now focus their expertise on the most promising proposals that have already been validated by our new Innovation Architect.