Traditional identity models like SAML and OAuth are insufficient for agents. Agent access must be hyper-ephemeral and contextual, granted dynamically based on a specific task. Instead of static roles, agents need temporary permissions to access specific resources only for the duration of an approved task.
Current agent frameworks create massive security risks because they can't differentiate between a user and the agent acting on their behalf. This results in agents receiving broad, uncontrolled access to production credentials, creating a far more dangerous version of the 'secret sprawl' problem that plagued early cloud adoption.
The LLM itself only creates the opportunity for agentic behavior. The actual business value is unlocked when an agent is given runtime access to high-value data and tools, allowing it to perform actions and complete tasks. Without this runtime context, agents are merely sophisticated Q&A bots querying old data.
It's a mistake to think of an agent as 'User V2.' Most enterprise and consumer agents (like ChatGPT) are inherently multi-tenant services used by many different people. This architecture introduces all the complexities of SaaS multi-tenancy, compounded by the new challenge of managing agent actions across compute boundaries.
The evolution of AI assistants is a continuum, much like autonomous driving levels. The critical shift from a 'co-pilot' to a true 'agent' occurs when the human can walk away and trust the system to perform multi-step tasks without direct supervision. The agent transitions from a helpful suggester to an autonomous actor.
Unlike previous tech waves, agent adoption is a board-level imperative driven by clear operational efficiency gains. This top-down pressure forces security teams to become enablers rather than blockers, accelerating enterprise adoption beyond the consumer market, where the value proposition is less direct.
A real-world example shows an agent correctly denying a request for a specific company's data but leaking other firms' data on a generic prompt. This highlights that agent security isn't about blocking bad prompts, but about solving the deep, contextual authorization problem of who is using what agent to access what tool.