How Do You Give an AI Agent a Verifiable, Auditable, Enforceable Identity?

Access Is Not Identity

Most AI agents in production today authenticate using one of three approaches—all of which give access without identity:

These approaches answer the question “can this process connect?” They don't answer “who is this agent, what is it allowed to do, and can we prove it did what it claims?”

The Three Properties of Real Agent Identity

When we set out to solve this problem, we defined what “identity” actually means for an AI agent. It requires three properties—and you need all three:

Existing protocols give you fragments of these properties. OIDC provides verifiable human identity. OAuth provides some enforceability through scopes. But no existing protocol provides all three for autonomous non-human software.

1. Making Identity Verifiable: Cryptographic Keypairs

The foundation of verifiable identity is cryptography—not tokens, not passwords, not shared secrets. When an agent registers with AIM, it receives its own Ed25519 keypair:

# Agent registration generates a unique keypair
from aim_sdk import secure

agent = secure("customer-data-reader", capabilities=[
    "database:read",
    "api:customers:get"
])

# The agent now has:
# - A unique agent ID (e.g., agent_01HYX3KM7VQB9W2TJNP5GZ8R4E)
# - An Ed25519 private key (stored securely, never transmitted)
# - A public key registered with AIM (used for verification)
# - Declared capabilities (enforced at runtime)

Why Ed25519? It's fast, produces compact signatures, and is widely trusted in security-critical systems (SSH, Signal, WireGuard). But the specific algorithm matters less than the principle: each agent's identity is a cryptographic keypair, not a shared secret.

Every action the agent takes is signed with its private key. A database query, an API call, a file write—each one carries a cryptographic proof of which agent performed it. This is fundamentally different from bearer tokens, where any process holding the token is indistinguishable from the legitimate holder.

2. Making Identity Auditable: Signed Action Records

Verifiable identity enables something that bearer tokens can't: non-repudiation. When every action is cryptographically signed, you get an audit trail where:

{
  "agent_id": "agent_01HYX3KM7VQB9W2TJNP5GZ8R4E",
  "agent_name": "customer-data-reader",
  "action": "database:read",
  "resource": "customers.profiles",
  "result": "allowed",
  "timestamp": "2026-02-11T14:23:07.891Z",
  "signature": "ed25519:a7f3b2c9...",
  "trust_score": 0.96
}

// Any party can verify this record using the agent's public key
// Tampering with any field invalidates the signature

This matters for three reasons:

• Compliance: Regulations like SOC 2, HIPAA, and GDPR increasingly require demonstrable accountability for automated data access. “An API key accessed the database” isn't sufficient—auditors need to know which system, what it accessed, and whether it was authorized.
• Incident response: When something goes wrong, you need to trace the exact sequence of actions across agents. With signed audit records, you can determine root cause in minutes, not days.
• Trust: Customers and partners increasingly ask “how do you govern your AI?” A cryptographic audit trail is the strongest possible answer.

3. Making Identity Enforceable: Runtime Capability Checks

Verifiable identity tells you who the agent is. Auditable identity tells you what it did. Enforceable identity ensures it can only do what it's supposed to do.

This is where most identity solutions fall short. OAuth scopes are declared at token issuance time but aren't enforced by the protocol itself—enforcement is left to each resource server. API keys have no concept of capabilities at all. The result is a gap between what an agent should do and what it can do.

AIM closes this gap with runtime capability enforcement:

# Agent registered with specific capabilities
agent = secure("customer-data-reader", capabilities=[
    "database:read",
    "api:customers:get"
])

# This works — capability matches
result = agent.execute("database:read", target="customers.profiles")

# This is BLOCKED — agent doesn't have database:write
result = agent.execute("database:write", target="customers.profiles")
# CapabilityDenied: agent 'customer-data-reader' lacks 'database:write'

The enforcement happens at the SDK level, before the action reaches the target system. This matters because:

The Missing Piece: MCP Server Attestation

Agent identity doesn't exist in a vacuum. Agents connect to MCP (Model Context Protocol) servers for tools—database access, API calls, file operations, web searches. The identity of the agent is only meaningful if you also verify the tools it connects to.

AIM addresses this with MCP server attestation:

Beyond Binary: Continuous Trust Scoring

Traditional identity systems are binary: you're authenticated or you're not. The token is valid or expired. This makes sense for human login flows—but AI agents operate continuously, and their trustworthiness can change over time.

AIM computes a continuous trust score for each agent based on eight factors:

The trust score updates in real time. If an agent's behavior deviates from its baseline, its trust score drops. If its MCP servers drift, the score drops. If it repeatedly attempts unauthorized actions, the score drops. And as the score drops, the system can automatically restrict the agent's access—without waiting for a human to intervene.

This is the difference between identity and authentication. Authentication is a point-in-time check. Identity is continuous—it encompasses who the agent is, what it's doing, and whether it should still be trusted.

Putting It All Together

Here's what the full identity lifecycle looks like for an AIM-managed agent:

Why This Matters Now

The AI agent ecosystem is at an inflection point. Frameworks like LangChain, CrewAI, AutoGen, and LangChain4j make it trivially easy to deploy agents that interact with production systems. MCP is becoming the standard for agent-tool communication. Enterprises are moving from experimentation to production deployment.

But the identity infrastructure hasn't kept pace. Most organizations are deploying agents with the same identity primitives they use for human users or traditional services—and discovering that the gap between “access” and “identity” creates real operational, security, and compliance risks.

The question isn't whether AI agents need proper identity—it's whether you build it before or after the first incident that makes you wish you had.