Multi-Agent ESG Compliance Architecture on AWS Bedrock: Llama 4, Qwen 3.8 Max, and a Compliance Agent

Why Multi-Agent Systems for ESG? As of May 23, 2026, B2B operations leaders face mounting pressure to automate ESG data collection, carbon tracking, and compliance reporting. Manual processes for utility bills, supply chain invoices, and regulatory checks consume thousands of hours annually and are prone to errors. Single-vendor ESG platforms often lock organizations into proprietary data formats and limited model choices. A multi-agent architecture—built on a neutral orchestration layer such as AWS Bedrock—offers flexibility: each task is handled by the best-fit model, and components can be swapped as newer models emerge. This article presents a concrete three-agent design using Llama 4 for document parsing, Qwen 3.8 Max for carbon accounting, and a fine-tuned compliance agent for regulatory alignment with CSRD, SEC climate rules, and ISO 14064. We include cost-per-task benchmarks and a

case study that demonstrates a 60% reduction in manual audit hours for a mid-size manufacturing firm. Architecture Overview: Three Specialized Agents on AWS Bedrock The system runs entirely on AWS Bedrock , which provides managed access to multiple foundation models with a unified API. Each agent is designed for a specific ESG subtask: - Agent 1 – Document Parser (Llama 4) : Uses Meta's Llama-4-8B model to extract structured data from scanned utility bills and supply chain invoices. Llama 4's strong OCR capabilities and long context window (up to 128K tokens) make it ideal for handling multi-page PDFs with varied layouts. - Agent 2 – Carbon Accountant (Qwen 3.8 Max) : Alibaba Cloud's Qwen3.8-Max model processes the parsed data to calculate Scope 1, 2, and 3 emissions using built-in emission factors and custom conversion logic. Its reasoning capabilities allow it to handle missing data g

racefully. - Agent 3 – Compliance Checker (Fine-tuned model) : A lightweight model (e.g., fine-tuned Llama 3.2-3B) trained on public regulatory text from the EU CSRD, SEC climate disclosure rules (release no. 33-11275), and ISO 14064 standards. It reviews carbon reports for completeness and flags potential non-compliance. These agents communicate through AWS Bedrock's built-in orchestration via a simple queue-based pipeline. The architecture is vendor-neutral: you can replace any model with alternatives like Amazon Nova or Claude as long as they meet similar benchmarks. Step-by-Step Data Pipeline Setup for Utility Bills and Supply Chain Invoices 1. Ingestion Layer Upload PDFs to an Amazon S3 bucket , triggering an AWS Lambda function that extracts metadata and queues each document for parsing. 2. Document Parsing with Llama 4 Call the Llama-4-8B model on Bedrock with a prompt instructing

it to extract: - Vendor name, invoice date, total amount, consumption (kWh, gallons, etc.), tariff code. - For supply chain invoices: line-item details, material types, and origin. The model returns a JSON structure. A validation script cross-checks required fields and stores the output in a DynamoDB table. 3. Data Cleaning A Python function runs regex-based deduplication and unit conversion (e.g., therms to kWh). This step runs serverless on Lambda. 4. Storage Cleaned data is pushed to Amazon RDS (PostgreSQL) for long-term retention and audit trail. Note: Llama 4 is available in AWS Bedrock regions us-east-1 and eu-west-1 as of May 2026. For latency-sensitive pipelines, keep the Bedrock endpoint in the same region as your S3 bucket. Carbon Accounting with Qwen 3.8 Max: From Invoices to Emissions Once the parser has structured data, the carbon accounting agent uses Qwen3.8-Max to: - Map

each invoice to an emission category (electricity, natural gas, transportation, raw materials). - Apply emission factors from publicly available databases (e.g., EPA eGRID, DEFRA). - Calculate Scope 1, 2, and 3 totals for the reporting period. Qwen 3.8 Max supports function calling, which we use to fetch real-time emission factors from an API if needed. The model outputs a summary report in JSON format that includes per-source emission breakdowns and confidence scores. Qwen3.8-Max is available in AWS Bedrock by request for enterprise customers. For this architecture, it is accessed via Bedrock’s API with temperature=0.1 to ensure consistency. Automating Regulatory Compliance: CSRD, SEC Climate Rules, and ISO 14064 The compliance agent is a fine-tuned model (we used a base Llama 3.2-3B) trained on: - CSRD (EU) : ESRS E1 climate-related disclosures, including double materiality requiremen

ts. - SEC Climate Rule : registrant disclosure of Scope 1 and 2 emissions assurance requirements. - ISO 14064 : greenhouse gas quantification, monitoring, and reporting principles. Fine-Tuning Process We collected 5,000 pairs of carbon reports (with known compliance gaps) and their corrected version