You are helping me build the following AI agent workflow.

## Goal
Brand Voice Content Checker: Checks written content against a specified brand voice, flagging sections that don't fit the desired tone.

## Specification
- What it does: You maintain your brand's consistent voice by having this agent automatically check written content and flag any sections that don't match your desired tone. This allows you to ensure every piece fits your style without needing constant direct involvement from yourself.
- Trigger: Run manually (Manual · on demand)
- Autonomy: You stay in control
- Expected setup effort: under an hour
- Tools/services involved:
  - LLM: Check content against brand tone

## Known pitfalls, handle each one explicitly in your implementation
1. Long documents can exceed context windows so chunk the input and process sections sequentially.
2. Subjective tone checks vary by model version so benchmark results with human reviewers before deployment.
3. Over-editing can strip unique author voice so allow users to accept or reject individual suggestions easily.

## Reference implementation
https://www.youtube.com/watch?v=sIugzOQz7Vk (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/ai-brand-voice-content-checker/ via usecasesforagents.com