You've just built your first AI agent pipeline. It kicks off beautifully: the agent reads a task, plans a series of steps, calls a few tools, and starts executing. But somewhere around step seven or eight, something goes wrong. The agent seems to "forget" an instruction
Agentic AI is no longer a prototype in a sandbox. As of early 2026, enterprise platform teams are running multi-agent workflows that autonomously browse the web, write and execute code, call third-party APIs, mutate database records, send emails, trigger financial transactions, and provision cloud infrastructure. According to Deloitte's
There is a quiet crisis unfolding inside enterprise AI stacks right now. Multi-tenant agentic workflows are failing in ways that traditional observability tooling was never designed to catch. By the time an alert fires, the damage is already done: a corrupted context window has been handed to your foundation model,
Here is a hard truth that most platform engineering teams are not ready to hear: your observability stack is lying to you. Not through bad data, not through misconfigured collectors, and not through careless instrumentation. It is lying to you by design, because the mental model baked into every OpenTelemetry
In 2026, multi-agent AI systems are no longer a research curiosity. They are the backbone of enterprise automation: orchestrating deployments, approving financial transfers, modifying production databases, and triggering irreversible supply chain actions. Alongside this power comes a threat that most platform security models were never designed to handle. When a