Boomi unveils Agent Control Plane to rein in enterprise AI agents

What changed: Boomi introduced an Agent Control Plane, a vendor- and model-neutral control layer that sits between AI agents and core systems like Salesforce, SAP, Oracle, and Workday, giving teams centralized visibility into every agent and tool interaction. It enforces identity and rate limits, caps token and compute usage, and routes high‑risk transactional actions for human approval, and is paired with Boomi Connect, which exposes over 1,000 systems as governed tools that agents built on Claude, ChatGPT, and Gemini can safely call.

Why it matters: Enterprises running multiple agents across clouds now have a single place to see what agents are doing, control spend, and prevent risky writes into critical systems. That makes it much easier to move from experimental agents to production workflows without losing compliance or cost control.

Try/watch: Map your current and planned agents to a control‑plane pattern: define which systems they can touch, set hard budgets per agent, and require human approval for irreversible operations like payments or record deletions.

Voicing AI launches Knowledge Mesh to fix brittle agentic AI projects

What changed: Voicing AI announced general availability of Knowledge Mesh, an enterprise context layer built specifically for AI agents and already in production in financial services and telecom deployments. The launch is framed against a Gartner forecast that more than 40% of agentic AI projects will be cancelled by the end of 2027 due to escalating costs, unclear business value, and inadequate risk controls, with AI context platforms projected as a $28 billion market in 2026 growing to about $78 billion by 2030.

Why it matters: Many agents fail not because of model quality but because they lack reliable, governed access to company knowledge. A dedicated context layer gives teams a way to standardize how data is prepared, filtered, and delivered to agents, improving quality and reducing the risk of agents acting on stale or sensitive information.

Try/watch: Audit where your agents are currently pulling context from, then prioritize one or two high‑value domains (support knowledge base, policy library, product catalog) to route through a governed knowledge layer before scaling agent usage.

GPT‑6 Astra goes GA in Microsoft Foundry with agent‑grade governance

What changed: OpenAI's GPT‑6 Astra is now generally available in Microsoft Foundry, positioned as a model designed for agentic, cross‑application work including “computer‑use” style capabilities. Foundry deployments integrate with Microsoft Entra ID, role‑based access control, private networking, and monitoring, so Astra‑based agents can be secured using the same identity and network controls used for other workloads. Separate coverage highlights Astra’s 1 million‑token context window, strong scores on advanced benchmarks like ARC‑AGI‑3 and ExploitBench, and capabilities spanning form filling, CRM updates, calendar organization, software testing, and complex terminal operations.

Why it matters: Teams can now deploy highly capable computer‑use agents with built‑in hooks for identity, network isolation, and observability rather than bolting on governance later. That lowers the barrier for using agents in sensitive workflows like finance, operations, and security while preserving auditability.

Try/watch: Treat Astra agents as a new class of production workload: integrate them with your existing IAM, require RBAC for tool access, and log all agent actions in your observability stack before granting access to live production systems.

Dubai Chambers trains 14,000+ companies on agentic AI adoption

What changed: Dubai Chambers announced a training programme for more than 14,000 member companies focused on adopting agentic AI across operations. The initiative, open to Business Groups and Business Councils under the Dubai Chamber of Commerce, covers using agentic AI systems that can carry out multi‑step tasks and make decisions with limited human oversight, going beyond the generative AI tools most firms have used so far.

Why it matters: This signals a shift from "playground" generative AI to structured agentic deployments across a large regional business base. Founders and operators in or working with the region can expect faster normalization of automated workflows and growing expectations that vendors provide agent‑ready tools.

Try/watch: If you operate in the Gulf or serve customers there, align your product roadmap with agentic use cases (multi‑step task execution, decision support) and be ready to offer training and governance guidance alongside your tools.

Tech Mahindra opens AWS centre to industrialize agentic AI operations

What changed: Tech Mahindra launched an AWS Agentic Process Transformation Centre of Excellence to help enterprises move from agentic AI pilots into day‑to‑day operations. The centre combines Tech Mahindra’s business process services with AWS tools and cloud infrastructure, focusing on telecom, healthcare, banking and financial services, retail, and manufacturing, and has already delivered Collections Guru, an arrears‑management product that uses agentic AI to autonomously adjust collection strategies for UK financial services provider Target Group.

Why it matters: This is a concrete example of large‑scale, sector‑specific agent deployment rather than generic demos. Service providers building on cloud platforms can use similar patterns—vertical centres of excellence plus an initial flagship agent product—to accelerate adoption while proving ROI.

Try/watch: Identify one repeatable process in a target industry (collections, claims handling, onboarding) and design an agent that can own the end‑to‑end workflow with clear guardrails, then use that as the anchor for a broader agentic services offering.