Most teams should not build agent orchestration first. If your goal is governed business workflows across AI agents, APIs, robots, and human approvals, the safer move is usually to buy the orchestration layer and reserve custom engineering for the logic that actually differentiates your business. That matters because fewer than 10 percent of enterprises experimenting…
Most teams evaluating OpenAI workspace agents for enterprise use should not treat this as a binary choice. Workspace agents now support shared, long-running team workflows inside ChatGPT, and OpenAI says those agents can also run on schedules and in Slack with approvals and analytics. That makes them a serious option for workspace AI automation. But…
Testing AI agents in production is not primarily a QA staffing problem. It is an architecture problem. If an agent system was not designed to expose its decisions, constrain its action space, and produce replayable evidence, no amount of extra test cases will make it reliably production ready. You can hear that frustration clearly in…
Enterprise AI agent security is failing for a simple reason: most teams are deploying agents before they design the control plane around them. By spring 2026, 88% of organizations had already experienced AI agent security incidents, while only 14.4% of organizations said their entire agent fleet had full IT and security approval. That gap is…
IBM Think 2026 made one thing clear: watsonx Orchestrate is no longer being framed as a workflow builder first. It is now being framed as an agentic control plane, a layer for running, governing, and optimizing autonomous agents across enterprise systems. That shift matters because it moves the conversation from task automation to enterprise control,…
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