CFO Cost Review Agent

• •Airtable API Key
• •OpenAI API Key
• •Pinecone API Key

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  "name": "CFO Expense Reviewer Agent",
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        "text": "=An employee submitted an expense:\nAmount: ${{ $json.fields.Amount }}\nSubmitted by: {{ $json.fields['Submitted By'] }}\nCategory: {{ $json.fields.Category }}\nDescription: {{ $json.fields.Description }}\nDate Submitted: {{ $json.fields['Date Submitted'] }}\nStatus: {{ $json.fields.Status }}",
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        "content": "## 🔁 **Section 1: Intake – Monitoring Expense Requests**\n\n### 1. **Watch New Expense Requests**\n\n**(Airtable Trigger)**\n\n* **Purpose:** Continuously monitors the Airtable `Expenses` table for new or updated entries with `Status = Pending`.\n* **What it does:** Triggers the entire workflow when a new expense is submitted or an existing one is updated for review.\n\n> 💡 Ensures real-time detection of incoming requests that need analysis."
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        "content": "🧠 Section 2: AI Analysis – CFO Reasoning Engine\n2. CFO Expense Review Agent\n(AI Agent - Tools Agent)\n\nPurpose: Acts as a virtual CFO. It receives the expense data and reasons through the submission using a language model.\n\nConnected To: OpenAI GPT-4 Model and Structured Output Parser\n\n3. OpenAI GPT-4 Model\n(OpenAI Chat Model)\n\nPurpose: Powers the logic behind the CFO's decision-making.\n\nPrompt Format: Given structured context about the expense, it determines whether it should be flagged and explains why.\n\n4. Parse CFO Agent Response\n(Structured Output Parser)\n\nPurpose: Transforms the unstructured response from GPT into a clean JSON object.\n\nOutput: Fields like decision, reason, amount, submitted_by, category, etc., which can be used for downstream processing.\n\n🧩 This section gives intelligence to the system, allowing it to make smart and explainable decisions."
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        "content": "🧬 Section 3: Audit Trail – Embedding & Storage\n5. Prepare Data for Pinecone\n(Default Data Loader)\n\nPurpose: Loads the document (usually the reasoning or description) for embedding.\n\nInput: Reasoning text or metadata generated by the AI agent.\n\n6. Split Reasoning Text\n(Recursive Character Text Splitter)\n\nPurpose: Splits long reasoning content into smaller chunks (if needed) to fit OpenAI embedding limits.\n\n7. Generate Embeddings\n(Embeddings OpenAI)\n\nPurpose: Converts the textual reasoning into a vector representation using OpenAI's embedding model.\n\n8. Store Decision in Pinecone\n(Pinecone Vector Store)\n\nPurpose: Saves the embedded vector along with metadata like decision, amount, and reason.\n\nGoal: Creates a searchable, auditable archive of past decisions for future reference and pattern learning.\n\n🧾 This section builds a scalable memory system for compliance and insights over time."
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        "content": "✅ Section 4: Output – Updating Records\n9. Update Airtable Record\n(Airtable Update Node)\n\nPurpose: Writes the final decision (Approved or Flagged) and the reasoning back to the original Airtable record.\n\nFields Updated: Status, Reason, and optionally an ReviewedAt timestamp.\n\n📥 Ensures that Airtable remains the source of truth, visibly updated with the CFO agent’s input."
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        "content": "=======================================\n            WORKFLOW ASSISTANCE\n=======================================\nFor any questions or support, please contact:\n    Yaron@nofluff.online\n\nExplore more tips and tutorials here:\n   - YouTube: https://www.youtube.com/@YaronBeen/videos\n   - LinkedIn: https://www.linkedin.com/in/yaronbeen/\n=======================================\n"
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        "content": "## 📊 **CFO Expense Approval Workflow Overview**\n\nThis workflow automates the process of monitoring, analyzing, and auditing expense requests using Airtable, OpenAI, and Pinecone. It is organized into the following four sections:\n\n---\n\n## 🔁 **Section 1: Intake – Monitoring Expense Requests**\n\n### 1. **Watch New Expense Requests**\n\n**(Airtable Trigger)**\n\n* **Purpose:** Continuously monitors the Airtable `Expenses` table for new or updated entries with `Status = Pending`.\n* **What it does:** Triggers the entire workflow when a new expense is submitted or an existing one is updated for review.\n\n> 💡 Ensures real-time detection of incoming requests that need analysis.\n\n---\n\n## 🧠 **Section 2: AI Analysis – CFO Reasoning Engine**\n\n### 2. **CFO Expense Review Agent**\n\n**(AI Agent - Tools Agent)**\n\n* **Purpose:** Acts as a virtual CFO. It receives the expense data and reasons through the submission using a language model.\n* **Connected To:** OpenAI GPT-4 Model and Structured Output Parser\n\n### 3. **OpenAI GPT-4 Model**\n\n**(OpenAI Chat Model)**\n\n* **Purpose:** Powers the logic behind the CFO's decision-making.\n* **Prompt Format:** Given structured context about the expense, it determines whether it should be flagged and explains why.\n\n### 4. **Parse CFO Agent Response**\n\n**(Structured Output Parser)**\n\n* **Purpose:** Transforms the unstructured response from GPT into a clean JSON object.\n* **Output:** Fields like `decision`, `reason`, `amount`, `submitted_by`, `category`, etc., which can be used for downstream processing.\n\n> 🧩 This section gives intelligence to the system, allowing it to make smart and explainable decisions.\n\n---\n\n## 🧬 **Section 3: Audit Trail – Embedding & Storage**\n\n### 5. **Prepare Data for Pinecone**\n\n**(Default Data Loader)**\n\n* **Purpose:** Loads the document (usually the reasoning or description) for embedding.\n* **Input:** Reasoning text or metadata generated by the AI agent.\n\n### 6. **Split Reasoning Text**\n\n**(Recursive Character Text Splitter)**\n\n* **Purpose:** Splits long reasoning content into smaller chunks (if needed) to fit OpenAI embedding limits.\n\n### 7. **Generate Embeddings**\n\n**(Embeddings OpenAI)**\n\n* **Purpose:** Converts the textual reasoning into a vector representation using OpenAI's embedding model.\n\n### 8. **Store Decision in Pinecone**\n\n**(Pinecone Vector Store)**\n\n* **Purpose:** Saves the embedded vector along with metadata like decision, amount, and reason.\n* **Goal:** Creates a searchable, auditable archive of past decisions for future reference and pattern learning.\n\n> 🧾 This section builds a scalable **memory system** for compliance and insights over time.\n\n---\n\n## ✅ **Section 4: Output – Updating Records**\n\n### 9. **Update Airtable Record**\n\n**(Airtable Update Node)**\n\n* **Purpose:** Writes the final decision (`Approved` or `Flagged`) and the reasoning back to the original Airtable record.\n* **Fields Updated:** `Status`, `Reason`, and optionally an `ReviewedAt` timestamp.\n\n> 📥 Ensures that Airtable remains the **source of truth**, visibly updated with the CFO agent’s input.\n"
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