You are helping me build the following AI agent workflow.

## Goal
Carbon Compliance Reporting: Automates monthly emissions validation against GHG Protocol and EPA standards using parallel AI agents to generate audit-ready reports.

## Specification
- What it does: You receive monthly audit-ready reports that validate your emissions data against GHG Protocol and EPA standards without manual bottlenecks. The process automatically verifies accuracy, reviews accounting methodology, and handles any compliance exceptions so you can submit confident records on schedule.
- Trigger: On a schedule (Scheduled · monthly or quarterly)
- Autonomy: Fully hands-off
- Expected setup effort: about an afternoon
- Tools/services involved:
  - OpenAI Chat Model: Data validation and compliance assessment via GPT-4o
  - Google Sheets: Storing compliant reports and ingesting data
  - Code: Orchestrating logic and routing outcomes

## Known pitfalls, handle each one explicitly in your implementation
1. Complex regulatory updates can render static prompts obsolete so design the system to allow easy swapping of prompt templates without redeploying code.

## Reference implementation
https://n8n.io/workflows/13427 (workflow template)
Fetch and inspect this before building. If it matches my stack, adapt it;
if not, rebuild the pattern with my tools.

## Process requirements
1. Before building: ask me which of the listed tools I actually use and
   what my platform is (n8n / Make / code / other). Do not assume.
2. Adapt the pattern to my answers; do not force the reference stack.
3. Address every pitfall above; tell me how you handled each.
4. Provide a test plan I can run before letting this touch real data.
5. Ask before any step that sends messages, modifies data, or spends money.

Source: https://usecasesforagents.com/use-case/automate-carbon-compliance-reporting/ via usecasesforagents.com