The data poisoning angle is interesting. Models trained on scraped web data inherit whatever biases, errors, and manipulation exist in that data. If bad actors can inject corrupted data at scale, it creates a malign incentive structure where model training becomes adversarial. The real solution is probably better data provenance -- models trained on licensed, curated datasets will eventually outcompete those trained on the open web.
The staleness problem mentioned here is real. For agentic systems, a markdown-based DAG of your codebase is more practical than a traditional graph because agents work within context windows. You can selectively load relevant parts without needing a complex query engine. The key is making updates low-friction -- maybe a pre-commit hook or CI job that refreshes stale nodes.