Microsoft reveals an open trust stack for AI agents (ASSERT + Agent Control Specification)

What changed: Microsoft used its Microsoft Build keynote to publish an open, end-to-end trust stack for AI agents and announced two open-source projects — ASSERT (Adaptive Spec-driven Scoring for Evaluation and Regression Testing) and the Agent Control Specification — to standardize safety evaluation and where controls are applied in an agent’s loop.

Why it matters: Founders and engineering leads can now adopt community-backed evaluation tooling and a common control interface for agent behavior instead of inventing ad-hoc safety checks, which speeds safe pilot launches and auditability.

Try/watch: Add ASSERT to your agent testing pipeline (or run a small pilot) to compare how your current checks map to the spec-driven tests; watch the project repos for examples and CI integration patterns.

Cisco launches Cloud Control and an "AgenticOps" platform for IT operations

What changed: Cisco unveiled Cloud Control and an AgenticOps operating model at Cisco Live — a unified platform that puts human operators and autonomous agents into a single operational view, with built-in telemetry, purpose-built models, and natural-language agent builders for networking and security workflows.

Why it matters: Operators and platform teams can consider a consolidated pilot (network, security, observability) that runs agents and people against the same data context, which reduces silos and the integration work normally needed to make multiple automation tools play nicely.

Try/watch: Run a constrained pilot that uses Cloud Control’s structured agent workflows to automate a repetitive incident path (detect → isolate → remediate → validate) and measure error rates and recovery time; monitor how models are grounded to Cisco’s operational data.

Netskope launches AI Command Center plus AgentSkope for autonomous risk triage

What changed: Netskope announced the Netskope One AI Command Center to discover AI assets across cloud, endpoints, and servers, correlate AI risk to identities and data, and ship an AgentSkope AI Risk AISecOps agent that autonomously triages and drives response.

Why it matters: Security and risk teams get a practical route to inventory and control deployed agents (including local models and browser extensions) and to automate triage without immediately expanding headcount — useful if you’re deploying agentic automation while needing to close visibility gaps.

Try/watch: Run the Command Center’s discovery on a test scope (SaaS + a sample of endpoints) to map where agents touch sensitive data, then tune playbooks for AgentSkope so human review gates remain in place for high-risk actions. Watch for eBPF-based server discovery implications on privacy and false positives.

Noma ships Agent Access Control for enterprise agent governance

What changed: Noma announced Agent Access Control, a product that auto-invents an inventory of agents and Model Context Protocol (MCP) servers, defines per-agent access boundaries, and enforces runtime policies with continuous verification.

Why it matters: For security architects and compliance teams, this gives a direct way to manage which agents can access which data and to detect when runtime inputs try to coerce an agent beyond its grant — a practical layer for least-privilege governance of large agent fleets.

Try/watch: Start with automated discovery to build an agent registry, then author least-privilege access templates for high-sensitivity data; monitor enforcement logs for policy drift and inputs that repeatedly trigger runtime violations.