As AI agents move from experimental deployments to production systems, the question of agent identity has become critical — and the answer is not straightforward. Traditional approaches assume that an AI agent can simply be treated like a service account: give it API keys or tokens and let it run. But AI agents operate with fundamentally different characteristics than traditional software services, requiring rethinking of identity and access patterns.
The core identity problem with AI agents is their autonomy. A traditional service account performs a fixed, deterministic set of actions. An AI agent, by contrast, makes decisions in real time based on context, instructions, and training. This means the range of actions it might take is not predetermined — and therefore the permissions it should hold are harder to scope. An agent instructed to “draft a contract” might need access to contract templates, relevant case law, client information, and legal precedent databases. But scoping that access precisely is challenging when the agent’s reasoning path is not fully predictable.
Non-human identity for AI agents requires several departures from traditional IAM models. First, runtime monitoring: rather than just checking permissions at access time, systems must continuously monitor what an AI agent is doing and alert when its actions deviate from expected patterns. Second, capability-based access: instead of granting broad permissions, grant specific capabilities to perform specific actions, with the AI system requesting those capabilities as needed. Third, audit trail enrichment: capture not just what the agent did but why it did it — what reasoning led to that action.
The authentication layer is also more complex. Humans can use multi-factor authentication — something they know, something they have, something they are. AI agents have none of these. Authentication must rely on cryptographic proofs of identity, attestation of the agent’s configuration and model, and continuous validation of the agent’s integrity. If an AI agent is compromised — for example, if its model is subtly altered by an attacker — traditional authentication mechanisms would not detect it.
For organisations deploying AI agents in production, the identity challenge is pressing. Current identity infrastructure was not designed for autonomous systems. The agents that organisations are deploying today are likely using overly broad permissions, operating with minimal runtime visibility, and lacking the audit trails necessary for forensic investigation if something goes wrong. Organisations need identity solutions specifically designed for AI agents, not general-purpose IAM platforms retrofitted to support them.