AGNTCon + MCPCon Japan 2026

Automating scientific research is one of the most important uses of AI agents, and as agents grow more autonomous, it is quickly becoming realistic. We can already let an agent run research that takes hours or even days.
The main challenge is not whether the agent finishes, but how much we can trust the results it reports. A result that looks successful may come from a misunderstood setup or a manipulated metric. Checking it by hand is hard, and asking another agent to check it only moves the problem one step further.
Being too strict is also risky. A good idea does not always give results quickly, and it is easy to find reasons to call a result a failure. A system that rejects too easily throws away promising research, while ideas that lead nowhere should be stopped early. Balancing proper rejection and reliable acceptance is the hardest part of automated research.
Based on my experience running agentic research-automation systems, I will explain how these problems appear in long-running tasks and the approaches we are developing to keep research reliable.
You will leave with a practical framework for trusting and using long-running autonomous agents in research and beyond.