Open-source agent checks technical docs for outdated claims using live web search

I built a document agent with a live web-grounded decision engine! Decision models like Jev are powerful, but they don’t have live context by default. They can only reason over what they learned during training and the context you give them at inference time. That becomes a problem when you’re working with documents containing information that can change over time. An API referenced in a document could have been deprecated, a package or model recommendation could be outdated, or an instruction that was correct when the document was written may no longer work today. So I built an agent that checks these time-sensitive claims against the live web. I first use Agentic Document Extraction to parse the uploaded document while preserving its structure and content. From that extracted content, the agent identifies claims that are actually freshness-sensitive rather than sending every sentence for verification. For each claim, it keeps the original statement and the surrounding document context, then turns the verification into a decision task for Parallel’s Decisions API. Parallel searches the live web for evidence relevant to that specific claim and returns a grounded decision with supporting sources. The agent then maps the result into one of three states: - VALID: current evidence still supports the claim - OUTDATED: newer evidence contradicts or supersedes it - NEEDS REVIEW: the available evidence is not strong enough to make a reliable call For example, if an older document says: “Use API v1 for this integration.” the verification request includes that claim and its document context. Parallel can search the current API docs, check whether v1 is still supported, and ground the decision in the latest sources it finds. The final output keeps both sides together: the claim extracted from the original document and the current web evidence used to evaluate it. So instead of treating a document as static context, the agent can reason about which parts of it may have drifted since it was written. Github Repo: https://github.com/Sumanth077/Hands-On-AI-Engineering/tree/main/ai_agents/technical_documentation_freshness_checker This can be useful across technical docs, research reports, internal knowledge bases, implementation guides, product documentation, policies, and other documents where some claims remain stable while others age quickly.