# Agentic AI

Autism resting-state fMRI classifiers rarely generalize across acquisition sites, and many reported accuracies are inflated by data leakage. ASD Neuro Agentic (ANA) has language-model agents propose features from the literature and researcher input while fixed tools perform every calculation; across 1,028 participants from 17 ABIDE I centers it searched pipelines and selected a ComBat-harmonized ensemble of tangent-space connectivity models. In leave-one-center-out testing the ensemble reached an AUC of 0.814 (95% CI, 0.773–0.841), above ASD-DiagNet and ASD-SAENet.

Autism resting-state fMRI classifiers rarely generalize across acquisition sites, and many reported accuracies are inflated by data leakage. ASD Neuro Agentic (ANA) has language-model agents propose features from the literature and researcher input while fixed tools perform every calculation; across 1,028 participants from 17 ABIDE I centers it searched pipelines and selected a ComBat-harmonized ensemble of tangent-space connectivity models. In leave-one-center-out testing the ensemble reached an AUC of 0.814 (95% CI, 0.773–0.841), above ASD-DiagNet and ASD-SAENet.

Meta's Intelligent Delivery routes compute across the ads serving stack through an ML-driven adaptive optimization layer that targets the marginal return of each request, model, hardware fleet and ranking stage. From 2017 through the first half of 2026 it contributed about 10% additional ads ranking performance on top of model and system advancements. A shared Resource Core Model reads each request, layered decision engines place traffic, hardware and per-host capacity, and AI agents propose and test configuration changes that engineers approve before shipping.

Meta's Intelligent Delivery routes compute across the ads serving stack through an ML-driven adaptive optimization layer that targets the marginal return of each request, model, hardware fleet and ranking stage. From 2017 through the first half of 2026 it contributed about 10% additional ads ranking performance on top of model and system advancements. A shared Resource Core Model reads each request, layered decision engines place traffic, hardware and per-host capacity, and AI agents propose and test configuration changes that engineers approve before shipping.

At GTC Berlin, NVIDIA introduced DSX POD, a reference design for complete Vera Rubin AI factories that partners integrate and validate inside modular containers before delivery, cutting onsite deployment from six months to 14 days once site utilities are ready. A companion OpenShell reference architecture places a policy-controlled runtime with kernel-level sandboxing between an agent and the systems it can affect, since each launched agent carries different privileges.

At GTC Berlin, NVIDIA introduced DSX POD, a reference design for complete Vera Rubin AI factories that partners integrate and validate inside modular containers before delivery, cutting onsite deployment from six months to 14 days once site utilities are ready. A companion OpenShell reference architecture places a policy-controlled runtime with kernel-level sandboxing between an agent and the systems it can affect, since each launched agent carries different privileges.

Intrusion alerts no longer trigger remediation on a single model's word: independent notary agents re-check each verdict and co-sign it on a Hyperledger Fabric ledger only after a 2-of-3 quorum. On the class-balanced CICIDS2017 test set the calibrated ensemble reaches 96.60% accuracy over all decisions, and the certified subset reaches 99.65% accuracy at a 0.04% false-positive rate with 87.3% coverage; on the independently generated GeNIS 2025 corpus the same stack certifies 99.86% of flows at 99.98% accuracy.

Intrusion alerts no longer trigger remediation on a single model's word: independent notary agents re-check each verdict and co-sign it on a Hyperledger Fabric ledger only after a 2-of-3 quorum. On the class-balanced CICIDS2017 test set the calibrated ensemble reaches 96.60% accuracy over all decisions, and the certified subset reaches 99.65% accuracy at a 0.04% false-positive rate with 87.3% coverage; on the independently generated GeNIS 2025 corpus the same stack certifies 99.86% of flows at 99.98% accuracy.

Breakthrough Listen and the SETI Institute have brought AI agents into the daily search at the Allen Telescope Array, where a single natural-language request lets agents steer the instrument, calibrate and focus it, correct data errors and apply AI models to the incoming stream. On NVIDIA's Holoscan platform, the DAQIRI library pulls terabits per second of sensor data straight into GPU memory so observations are processed in flight, and the system handles 2.4 terabits per second on RTX PRO 6000 Blackwell GPUs. The pipeline has achieved the first real-time AI detection of a pulsar, in the Crab Nebula, and the team is now evaluating open models including Nemotron for settings where data locality and turnaround matter most.

Breakthrough Listen and the SETI Institute have brought AI agents into the daily search at the Allen Telescope Array, where a single natural-language request lets agents steer the instrument, calibrate and focus it, correct data errors and apply AI models to the incoming stream. On NVIDIA's Holoscan platform, the DAQIRI library pulls terabits per second of sensor data straight into GPU memory so observations are processed in flight, and the system handles 2.4 terabits per second on RTX PRO 6000 Blackwell GPUs. The pipeline has achieved the first real-time AI detection of a pulsar, in the Crab Nebula, and the team is now evaluating open models including Nemotron for settings where data locality and turnaround matter most.

vibe-qc is an open-source electronic-structure code for molecules and solids whose code was written almost entirely by AI agents under the direction of one domain scientist. It puts molecular and periodic calculations behind a common Python interface and C++17 numerical core, and ships a reusable validation set: molecular Hartree–Fock energies agree with independent programs at the nanohartree scale, and conventional CCSD(T) is compared on small molecules. The set records independent-program comparisons, published benchmarks and internal consistency separately, and keeps comparisons that miss tolerance.

vibe-qc is an open-source electronic-structure code for molecules and solids whose code was written almost entirely by AI agents under the direction of one domain scientist. It puts molecular and periodic calculations behind a common Python interface and C++17 numerical core, and ships a reusable validation set: molecular Hartree–Fock energies agree with independent programs at the nanohartree scale, and conventional CCSD(T) is compared on small molecules. The set records independent-program comparisons, published benchmarks and internal consistency separately, and keeps comparisons that miss tolerance.

A consortium of 18 institutions, C-BRAIN, has begun feeding Alzheimer's data that AI tools and human researchers could not previously reach, including unpublished findings, negative results and governed proprietary data, into model training. That data had stayed inside individual institutions, where it could not be synthesized into usable knowledge. A hybrid centralized and federated design lets models learn where the data sits, and two open-source agentic tools have already come out of it; in published case studies OpenScientist finished in minutes analyses that domain scientists estimated would take weeks to months, at under $10 per run.

A consortium of 18 institutions, C-BRAIN, has begun feeding Alzheimer's data that AI tools and human researchers could not previously reach, including unpublished findings, negative results and governed proprietary data, into model training. That data had stayed inside individual institutions, where it could not be synthesized into usable knowledge. A hybrid centralized and federated design lets models learn where the data sits, and two open-source agentic tools have already come out of it; in published case studies OpenScientist finished in minutes analyses that domain scientists estimated would take weeks to months, at under $10 per run.

A multi-agent workflow under investigator direction carried a large electronic health record study from cohort construction and executable statistics through data-quality checks, literature retrieval and reporting, using sequential sacituzumab govitecan (SG) and trastuzumab deruxtecan (T-DXd) orders in breast cancer as the clinical case. From 4.95 million patients, 106 had orders for both drugs, 73 with T-DXd first and 33 with SG first; median second-drug record spans were 63 and 49 days, a median difference of −14 days (95% CI, −41 to 21), descriptive and unadjusted.

A multi-agent workflow under investigator direction carried a large electronic health record study from cohort construction and executable statistics through data-quality checks, literature retrieval and reporting, using sequential sacituzumab govitecan (SG) and trastuzumab deruxtecan (T-DXd) orders in breast cancer as the clinical case. From 4.95 million patients, 106 had orders for both drugs, 73 with T-DXd first and 33 with SG first; median second-drug record spans were 63 and 49 days, a median difference of −14 days (95% CI, −41 to 21), descriptive and unadjusted.

At the 2026 OCP Global Summit, AMD showed open full-stack infrastructure for agentic AI, with the Helios rack ramping production a year after its 2025 OCP debut and customer deliveries, deployments and workload validations underway, including initial systems installed and running at OpenAI, Meta, Microsoft and Oracle; the Helion rack lets customers tailor CPU core density, performance and power to agentic workloads in CPU-only and integrated CPU/GPU configurations, is planned for November 2026, and AMD plans to contribute its architecture to OCP.

At the 2026 OCP Global Summit, AMD showed open full-stack infrastructure for agentic AI, with the Helios rack ramping production a year after its 2025 OCP debut and customer deliveries, deployments and workload validations underway, including initial systems installed and running at OpenAI, Meta, Microsoft and Oracle; the Helion rack lets customers tailor CPU core density, performance and power to agentic workloads in CPU-only and integrated CPU/GPU configurations, is planned for November 2026, and AMD plans to contribute its architecture to OCP.