Cognizant links ServiceNow AI Agents to its multi-agent platform

What changed: Cognizant announced interoperability between ServiceNow AI Agents and its Neuro AI Multi-Agent Accelerator, aiming to orchestrate agents across different enterprise systems from a single cross-platform layer. The update is framed as an expansion of Cognizant's agentic AI portfolio, targeting workflow automation that spans IT, HR, and customer service workloads within ServiceNow environments.

Why it matters: Buyers with heavy ServiceNow footprints can now evaluate multi-agent automation without stitching together custom glue code between platforms. This lowers integration risk for enterprises that want AI agents to trigger actions in multiple systems while keeping ServiceNow as a primary system of record.

Try/watch: If you already run ServiceNow, ask Cognizant for concrete reference architectures showing how agents coordinate multi-step processes (for example, incident triage plus approvals) and what guardrails exist around data access.

WitnessAI debuts "Agentic Control" to lock down AI agents and tools

What changed: WitnessAI introduced Agentic Control, an extension to its platform that governs how AI agents, tools, and model context protocol (MCP) servers can be accessed in production environments. The product is described as agentic security, adding policy-based controls for which agents can call which tools and data sources.

Why it matters: As multi-agent systems move from experiments to real workflows, security and compliance teams need a way to manage permissions at the agent and tool level, not just at the application boundary. Centralizing these controls helps reduce the risk of over-privileged agents and accidental data exposure when agents chain tools together.

Try/watch: Map your current AI agents and tools, then pilot an access-policy layer like this on a narrow workflow (for example, report generation) to see where implicit permissions need to be tightened.

RUCKUS rolls out "Agentic Operations" for autonomous network management

What changed: RUCKUS Networks introduced Agentic Operations, described as a new class of AI-powered agents inside the RUCKUS One cloud platform. These agents are designed to continuously monitor networks and automate tasks such as optimization and troubleshooting across wired and wireless infrastructure.

Why it matters: Network teams are under pressure to manage more devices with fewer engineers, and agent-based operations promise faster incident response and more consistent configuration changes. For managed service providers and large campuses, this could shift day-to-day work from manual tuning to supervising autonomous runbooks.

Try/watch: Start with non-critical segments—like guest Wi‑Fi or lab networks—to evaluate how these agents handle noisy environments, and track false positives before enabling automation on production sites.

HPE extends agentic AI options across GreenLake and Private Cloud AI

What changed: At its Discover 2026 event, HPE detailed updates that extend agentic AI capabilities across GreenLake and Morpheus-managed environments. HPE Private Cloud AI is also adding new ProLiant DL394 Gen12 systems with NVIDIA Vera CPUs, targeting high-performance data processing workloads for agentic AI applications.

Why it matters: Infrastructure buyers can now treat agentic workloads—such as fleets of task-specific AI agents—as first-class citizens in their hybrid cloud plans rather than side experiments. Standardizing on certified hardware and management software reduces integration work when scaling pilots into production.

Try/watch: When refreshing on-prem hardware, ask vendors to show tested reference stacks for running many concurrent AI agents, including cost per agent, latency, and observability tooling across clusters.