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Exabeam adds security telemetry for enterprise AI agents

What changed: Exabeam expanded its Behaviour Intelligence platform with new tools to secure AI agents and autonomous workflows, doubling its AI- and agent-related behavioural detections to 90 and adding support for Anthropic Claude alongside other major AI platforms. The update extends coverage across Agent Behaviour Analytics, Outcomes Navigator, Nova, Threat Centre, Attack Surface Insights, search, and data collection workflows, and introduces Observra, an open source library for agent telemetry and observability aligned with the OWASP Top 10 for Agentic AI.

Why it matters: As agents start to act on behalf of employees inside core systems, traditional user-based monitoring misses many risky automated behaviours. Dedicated detections for human–agent interactions and autonomous agent activity give security teams a way to spot unusual tool calls, cross-system access, and credential use before they turn into incidents.

Try/watch: Inventory every AI agent interacting with production data and map them to Exabeam-style agent behaviour analytics or equivalent, then define clear playbooks for when Observra-like telemetry shows anomalous autonomous actions.

Ory connects coding agents to enterprise identity with Agent DX

What changed: Ory launched Agent DX, a product that plugs its identity stack into AI coding agents such as Claude Code, OpenAI Codex, and Gemini CLI through free plugins. Agent DX lets developers build, test, and manage authentication and authorisation workflows from within AI-assisted development environments, complementing Ory’s existing Agent Security offering that focuses on securing agents in production.

Why it matters: Many teams experiment with coding agents inside local development tools and only bolt on access control later, creating inconsistent identity logic across services. Agent DX lets developers bake enterprise-grade auth into agent-generated code from day one, reducing the risk of shadow APIs, hard-coded secrets, and mis-scoped permissions.

Try/watch: Enable Agent DX or similar plugins in your IDE, mandate that any agent-generated service uses the same central identity provider, and review how much auth-related boilerplate your developers can safely offload to agents.

Pentagon pilots AI agents to speed software approvals

What changed: The Pentagon is piloting AI agents to automate parts of its Authority to Operate (ATO) process, aiming to compress compliance timelines that can currently stretch to two years. The department’s Chief Digital and AI Officer highlighted how generative and agentic AI could handle documentation and other compliance tasks, and announced the Agent Network, a program pairing combatant commands with commercial AI and defense tech firms to deploy agentic AI into operations.

Why it matters: If AI agents can reliably generate and update compliance paperwork, software teams can ship secure capabilities faster instead of waiting years for approvals. The Agent Network also signals growing demand for operational agentic AI that can fuse intelligence sources and deliver decision-ready information to commanders.

Try/watch: Track how the ATO pilots define guardrails for compliance agents, and adapt those patterns—templated controls, supervised document generation, and audit trails—for internal governance workflows in your own organisation.

Berkeley RDI pushes toward self-organising agent development

What changed: Berkeley RDI’s Agentic AI Weekly highlights new research arguing for an AI-centric approach to agent development, where a base scaffold is provided and the agent learns how to organise topology, tools, and memory from experience and feedback. The newsletter introduces OpenSage, an Agent Development Kit that supports self-generating agent topology and dynamic tool synthesis, letting agents create and register their own tools and run them asynchronously in sandboxed environments.

Why it matters: Most current agent systems still depend on human experts to hand-design agent graphs, tool sets, and memory layouts, which does not scale across diverse tasks. Toolkits like OpenSage point to a future where agents autonomously configure sub-agents, tools, and skills, lowering the engineering overhead to deploy complex multi-agent workflows.

Try/watch: Experiment with ADKs that support AI-driven topology and tool creation, and evaluate where self-organising agents can replace brittle, manually wired task graphs in your product or operations stack.

Forbes warns agentic AI can strain infrastructure before it pays off

What changed: A Forbes analysis argues that many firms still treat agentic AI as upgraded chatbots, but at scale these agents expose weaknesses in cost control, governance, data architecture, and operational efficiency. The piece emphasises that proactive agents continuously monitor conditions, make decisions, call tools and APIs, and trigger thousands of small, context-rich interactions, requiring a platform-first approach: build the control plane and strengthen data and infrastructure layers before scaling agents across the enterprise.

Why it matters: Moving from demo agents to production workloads without robust platforms can overwhelm existing infrastructure and budgets, even if individual agents appear inexpensive. Founders and operators who invest early in shared agent platforms and governance avoid fragmented deployments that are hard to secure, scale, and measure.

Try/watch: Before greenlighting broad agent rollouts, define an internal "agent platform" with central routing, observability, cost controls, and data safeguards, and pilot agents only on top of that foundation rather than inside isolated teams.