You are helping me build the following AI agent workflow.

## Goal
Identify Key Decision Makers: Analyzes input data with an LLM to identify key decision makers like heads of sales or VPs of revenue for outreach.

## Specification
- What it does: You instantly identify key decision makers like heads of sales and VPs of revenue from your input data using Cloud Code and an LLM. This gives you a clear score breakdown to target the right people for outreach without manual searching.
- Trigger: Run manually (Manual · on demand)
- Autonomy: You stay in control
- Expected setup effort: about an afternoon
- Tools/services involved:
  - Cloud Code: Processing and scoring data
  - LLM: Analyzing text to identify roles like VP of Revenue or Head of Sales

## Known pitfalls, handle each one explicitly in your implementation
1. Ambiguous job descriptions can lead to misclassification of seniority so provide clear examples of what constitutes a decision maker.
2. Large text inputs may exceed token limits so truncate or summarize non-essential biographical details before processing.
3. Inconsistent naming conventions across sources require robust normalization logic so map varied titles to standard roles.

## Reference implementation
https://www.youtube.com/watch?v=JqXkPlX_4gQ (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/identify-key-decision-makers/ via usecasesforagents.com