Agent identity is the practice of giving an AI agent its own authenticated principal — separate from the human who delegated the work — so every action it takes can be attributed, scoped, logged and revoked independently.
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
Last updated:
The prevailing anti-pattern is a static API key on a shared service account. It defeats least privilege, because the key must cover the broadest thing the agent might ever do; it defeats attribution, because the log shows a service account rather than a person and a purpose; and it defeats revocation, because rotating the key breaks every agent using it.
The worse variant is an agent inheriting a human's session or credential, which produces a log that positively misattributes machine actions to a person.
The alternative is delegation: the agent authenticates as itself, requests authority for a specific task, receives a short-lived scoped grant, and every action records the agent, the delegating human, the scope and the timestamp.
Agent identity is what makes autonomous agents auditable, and audit regimes across regulated sectors assume a named actor behind every access. It is also the control that bounds the damage from prompt injection, since a narrowly scoped agent can do little even when compromised.
Not a shared service account and not a borrowed human session. The agent authenticates as itself so its actions are separable from everyone else's in the record.
Authority is granted for a specific job and expires, turning a permanent skeleton key into something closer to a hotel card that stops working at checkout.
Each action logs the agent, the human who delegated it, the granted scope and the timestamp. Delegation cannot be reconstructed afterwards, so it must be captured at write time.
One agent's authority can be withdrawn without breaking every other agent, which is what makes prompt revocation operationally realistic rather than an outage.
Scoped-per-task grants mean an agent holds only the access the current job requires, instead of the union of everything it has ever needed.
A compromised or injection-manipulated agent can only reach what its current narrow grant permits, which converts a potential breach into a contained incident.
The log cannot show which matter or which lawyer triggered the access, so a potential conflict crossing an ethical wall is unreconstructable.
Every action ties to the agent, the delegating attorney, the matter and the scope, and the authority expires when the task ends.
The deployment fails the individual-attribution standard auditors apply, independent of whether anything improper occurred.
Every agent is its own principal with scoped, short-lived credentials, and every action records the agent, the delegating human, the granted scope and the timestamp. Because ibl.ai is the agentic AI platform where you own all the code and the data, that audit trail is written to storage inside your perimeter in a schema you control and can export in full — an asset you hold rather than a reporting view exposed by a subscription. Agent identity integrates with your existing directory, so delegation is recorded against real people. It is model-agnostic, carries no per-seat pricing, and you can deploy anywhere. 1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
Learn about ibl.aiibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.