Enterprises get new tools to put AI agents under strict identity and governance control

What changed: Okta has made Agent SSO for AI agents generally available, allowing agents that use the Cross App Access protocol to be registered and managed as identities in Okta’s Universal Directory. Google Cloud released an AI infrastructure report identifying security, governance, and operations as the main barriers to scaling inference workloads, with particular concern about AI agents that can read email, query databases, and trigger APIs. Airbyte expanded its Agentic Data Platform with semantic search and fine-grained entity policies in its Context Store so organizations can control which agents and users see specific workspace data.

Why it matters: These launches give enterprises a concrete way to bring AI agents under the same identity, access, and governance model they already use for staff, reducing the risk of "shadow agents" operating without clear owners or permissions. Founders and IT leaders can now design agent-based workflows from day one with auditable identities, scoped data access, and central policy control rather than bolted-on safeguards.

Try/watch: Inventory every AI agent in use, decide which should be treated as first-class identities in your IAM system, and run a small pilot with tools like Agent SSO or governed context stores before broad rollout.

Vertical platforms turn CX and corporate investigations into end-to-end agentic workflows

What changed: Crescendo launched its Customer Experience Platform as an AI-native stack that collapses previously separate categories—contact center, ticketing, workforce management, quality, voice-of-customer, and knowledge—into one system where specialized AI agents run the entire CX operation end to end and continuously self-improve. Handshakes introduced the Handshakes Agent, an AI-powered investigative tool built on more than a decade of regulatory intelligence to automate due diligence, risk screening, vendor onboarding, and conflicts-of-interest checks.

Why it matters: These launches show how vertical platforms are using agents not just to assist staff but to take ownership of whole workflows, from customer conversations to compliance reviews, which can dramatically compress cycle times. Operators now have the option to replace fragmented CX and risk systems with agent-driven hubs, but doing so requires new thinking about exception handling, escalation, and quality assurance.

Try/watch: Start with a limited pilot—a single customer queue or one segment of vendor onboarding—and compare resolution times, customer or stakeholder satisfaction, and error rates before committing to a full agentic platform migration.

Local and sector-specific deployments point to an "always-on agents" hardware era

What changed: Lifenet Insurance in Japan announced it has completed full deployment of its in-house AI agents across all corporate departments, including claims assessment, customer support, product development, and administrative functions. The same roundup highlighted Apple’s new Mac mini models with M6 and M5 Pro chips, which boost AI processing performance up to four times and are optimized for 24/7 "always-on" local AI agent workloads at the desk. An X post reported that Perplexity is launching Portable Computer, a fully local version of its agentic platform built with NVIDIA that can run models, tools, files, and multi-step workflows on-device, defaulting to local execution with zero marginal token cost and requiring user permission before sending steps to frontier cloud models, initially on DGX Spark and RTX GPUs with at least 24GB of VRAM.

Why it matters: This combination of sector-wide deployment and hardware tuned for local agents indicates a shift toward always-on, near-the-user agents that can operate with tighter data locality and lower variable costs than cloud-only setups. Organizations with sensitive data now have practical options to keep key agent work on trusted devices while still escalating complex tasks to the cloud when needed.

Try/watch: Work with IT to define which workflows must stay local, provision capable hardware where necessary, and pilot local-first agents in those areas while monitoring performance, cost, and security outcomes against cloud baselines.