# Agentic orchestration

NVIDIA has introduced Vera, a CPU aimed at AI agents, seeking to meet both requirements in one chip: fast execution of agent steps and high concurrent agent counts. Previously AI factories had to choose between the fastest x86 processor and the most energy-efficient one to balance speed and capacity. In a 100-megawatt data center comparison, NVIDIA reports Vera supports 1.6x more agents per watt than the fastest x86 CPU and delivers 2x faster agent speed than the most energy-efficient x86 CPU; early adopters including Perplexity and DeepInfra report speedups in production-style tests, though Perplexity's deployment is still planned.

NVIDIA has introduced Vera, a CPU aimed at AI agents, seeking to meet both requirements in one chip: fast execution of agent steps and high concurrent agent counts. Previously AI factories had to choose between the fastest x86 processor and the most energy-efficient one to balance speed and capacity. In a 100-megawatt data center comparison, NVIDIA reports Vera supports 1.6x more agents per watt than the fastest x86 CPU and delivers 2x faster agent speed than the most energy-efficient x86 CPU; early adopters including Perplexity and DeepInfra report speedups in production-style tests, though Perplexity's deployment is still planned.

IoT device authentication is shifting from a one-time check of fixed credentials to continuous checking of device behavior. Static credentials and centralized trust miss anomalies after a device is compromised and keep exposing identifiable attributes. A combined scheme uses hardware behavioral fingerprints, federated learning with differential privacy, a lightweight permissioned chain and agentic orchestration; on 11 Raspberry Pi devices in the PP-IDE dataset, differential privacy lowered membership inference attack success from 48.2% to 12.4%, with 99.2% authentication accuracy.

IoT device authentication is shifting from a one-time check of fixed credentials to continuous checking of device behavior. Static credentials and centralized trust miss anomalies after a device is compromised and keep exposing identifiable attributes. A combined scheme uses hardware behavioral fingerprints, federated learning with differential privacy, a lightweight permissioned chain and agentic orchestration; on 11 Raspberry Pi devices in the PP-IDE dataset, differential privacy lowered membership inference attack success from 48.2% to 12.4%, with 99.2% authentication accuracy.

POrchestra lets sub-agents proactively send structured requests to the orchestrator during execution, so the orchestrator can revise their subgoal, context, tool set, or execution budget on the spot instead of waiting for the sub-agent to finish or fail. Earlier re-orchestration methods fixed each sub-agent's configuration until its lifecycle ended, delaying adaptation to dynamic environments. Across GAIA, GAIA2, and SWE-Bench Verified, task success improves by an average of 9.1% over the strongest baseline, and the companion OPOD turns that feedback into process supervision, raising small-orchestrator success by an average of 10.0% over training baselines.

POrchestra lets sub-agents proactively send structured requests to the orchestrator during execution, so the orchestrator can revise their subgoal, context, tool set, or execution budget on the spot instead of waiting for the sub-agent to finish or fail. Earlier re-orchestration methods fixed each sub-agent's configuration until its lifecycle ended, delaying adaptation to dynamic environments. Across GAIA, GAIA2, and SWE-Bench Verified, task success improves by an average of 9.1% over the strongest baseline, and the companion OPOD turns that feedback into process supervision, raising small-orchestrator success by an average of 10.0% over training baselines.

The work presents PRAXIS-VirtualCell, a modular framework that organizes biological data, predictive models, perturbations, adapters, execution environments, and validation evidence to enable reproducible and auditable virtual experiments; the system supports cross-species tasks spanning Escherichia coli, Saccharomyces cerevisiae, and human K562 cells, uses biological contracts and evidence-aware execution to distinguish supported predictions from extrapolation and abstention, and integrates agentic orchestration to translate natural-language questions into traceable virtual experiments.

The work presents PRAXIS-VirtualCell, a modular framework that organizes biological data, predictive models, perturbations, adapters, execution environments, and validation evidence to enable reproducible and auditable virtual experiments; the system supports cross-species tasks spanning Escherichia coli, Saccharomyces cerevisiae, and human K562 cells, uses biological contracts and evidence-aware execution to distinguish supported predictions from extrapolation and abstention, and integrates agentic orchestration to translate natural-language questions into traceable virtual experiments.