Webhound builds long-running AI research agents that scale quality with time and budget. Our agents can create either structured datasets or fully cited research reports.
This is the 3rd launch from Webhound. View more
Webhound
Launching today
Research has no natural finish line. An agent can spend ten minutes or ten hours on the same question, and both answers can look finished. Webhound lets you choose how much work the question deserves. Give it a question and a dollar budget. It follows leads and checks weak claims until the budget is consumed, then returns a cited report or sourced dataset with the sources and working documents behind it. Run Webhound yourself or call it from your agent through MCP or the API.
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Budget as the stopping rule makes sense, especially when another agent is waiting on the answer. The trust layer I’d want is a short handoff note: what was checked, what was intentionally skipped, and which unresolved claims could change the decision if someone spends another hour.
Interesting! That framing of budget-as-stopping-primitive is definitely worth attention. Most research tools stop when the prose sounds finished rather than when the work is actually done. Curious whether you guys plan the search tree up front and rank leads by expected payoff? Is it greedy step by step and just stops when the meter hits zero?
probe real submit pathBudget as the stopping rule makes sense, especially when another agent is waiting on the answer. The trust layer I’d want is a short handoff note: what was checked, what was intentionally skipped, and which unresolved claims could change the decision if someone spends another hour.
YourGPT
Congrats on the launch, Moe.
Webhound is tackling a really interesting problem with AI research the stopping problem is definitely something that gets overlooked. I especially like the idea of using a dollar budget as a research primitive instead of letting agents run indefinitely. I will defiently try this out
Webhound
@neha_8 Thanks a lot! Let me know if you have any feedback.