You are helping me build the following AI agent workflow. ## Goal Autonomous Video Clip Search Agent: Searches video footage for keywords by reasoning about visual traits, identifying matching clips, and returning the indexed results. ## Specification - What it does: Find specific moments in your video library instantly by searching with keywords that trigger visual reasoning. The agent scans footage for matching subjects, indexes the relevant clips, and delivers only the results you need. - Trigger: Run manually (Manual ยท on keyword search) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - AI Vision Agent: Reasoning and identifying visual content in video footage - Video Footage Indexer: Storing identified clips for retrieval ## Known pitfalls, handle each one explicitly in your implementation 1. Large video files can cause memory overflow, so process them in chunks or use streaming APIs. 2. Keyword ambiguity may lead to irrelevant matches, add a confidence threshold to filter low-scoring results. 3. Indexing latency increases with dataset size, implement caching for frequently searched terms. 4. Vision models might misinterpret context without temporal cues, include surrounding frames in the analysis window. ## Reference implementation https://www.youtube.com/watch?v=FwOTs4UxQS4 (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/autonomous-video-clip-search-agent/ via usecasesforagents.com