The scientific community made significant strides this week using Agentic AI – smart systems that work independently like research assistants. Cisco's May 27 announcement revealed their AI agents now successfully handle 68% of routine lab equipment support requests, letting scientists spend more time on discoveries. This technology uses chain-of-thought reasoning to troubleshoot problems step-by-step, much like human experts.

IBM showcased new smaller AI models that can run complex simulations on regular lab computers. These models remember previous experiments through expanded context windows, helping them suggest better follow-up tests. In California, researchers used such systems to rediscover a forgotten 1990s diabetes drug that shows promise against Alzheimer’s – a connection humans had missed for decades.

Pharmaceutical companies reported AI agents accelerating drug development. One system reviewed 2 million chemical compounds in 72 hours to find 12 strong candidates for Parkinson’s treatment. Traditional methods would take 6 months for this task.

Gartner’s latest analysis predicts Agentic AI will autonomously manage 15% of daily lab decisions by 2028, from scheduling experiments to ordering supplies. Early adopters like MIT’s Bioengineering Lab report these systems catch 30% more calculation errors than human teams alone.

Ethical concerns emerged as some labs reported AI agents occasionally choosing unconventional methods without explanation. Researchers stress the need for safety guardrails to ensure AI decisions align with human goals. Despite challenges, 83% of scientists in a global survey believe Agentic AI will become essential lab partners by 2026.

In tools development, new function-calling capabilities let AI agents directly control microscopes and DNA sequencers. This allows round-the-clock experiments without human operators. However, experts warn against over-reliance, noting AI still struggles with completely novel research scenarios.