Here is a scenario that is becoming increasingly common in 2026: your SaaS platform runs dozens of AI agents simultaneously, each one belonging to a different tenant, each one chaining together calls across GPT-5, Claude 4, Gemini Ultra 2, and your own fine-tuned domain model. An agent kicks off a
As agentic AI systems mature in 2026, one architectural challenge has quietly become the most dangerous blind spot in enterprise deployments: privilege escalation across nested agent hierarchies. When a parent orchestrator agent spawns a child agent, which in turn spawns its own subagents, the question of "what tools can
AI agents are no longer a novelty. In 2026, they are deeply embedded in production systems: scheduling tasks, querying databases, drafting communications, and making decisions on behalf of users across dozens of enterprise platforms. But with that power comes an uncomfortable truth that many backend teams are quietly avoiding: the
Here is a scenario that will feel familiar to any platform engineer running a multi-tenant AI product in 2026: it is 2:17 AM, your on-call alert fires, and your upstream foundation model provider (OpenAI, Anthropic, Google Gemini, or one of the newer players like xAI Grok API) is experiencing
Upgrading a foundation model in a multi-tenant AI agent platform feels a lot like performing open-heart surgery on a running aircraft. One tenant's legal document summarizer, another's customer support bot, and a third's code review agent are all sharing the same underlying model infrastructure.