Introducing AI Agent Identity Management

The AI Agent Access Challenge

Over the past year, we've watched AI agents evolve from experimental tools to production infrastructure. They're deploying code, managing databases, orchestrating cloud resources, and making critical operational decisions. But as we deployed these agents at scale, we discovered a fundamental problem: traditional identity and access management wasn't designed for autonomous systems.

Human access patterns are predictable. Users log in during business hours, access a handful of systems, and operate within well-understood behavioral boundaries. AI agents are different. They operate 24/7, can execute thousands of operations per hour, and their behavior patterns evolve as models improve.

Treating AI agents like humans with long-lived credentials creates unacceptable risks. Treating them like service accounts removes the granular control needed for safe operation. We needed a new approach.

Certificate-Based AI Agent Identity

TigerAccess now issues specialized short-lived certificates for AI agents with embedded safety controls. These certificates contain not just identity information, but operational constraints that the infrastructure enforces at every access point.

Action Budgets: Preventing Runaway Agents

One of our most powerful safety controls is the action budget system. Every AI agent certificate includes a maximum number of operations it can perform per session. This prevents runaway loops, limits blast radius during incidents, and provides a hard safety boundary.

# Request certificate with 100-operation budget
tac agent login \
  --agent-id=deployment-agent \
  --action-budget=100 \
  --ttl=1h

# Certificate includes embedded constraints
Certificate Extensions:
  Agent ID: deployment-agent
  Action Budget: 100 operations
  Rate Limit: 10 req/sec
  Allowed Resources:
    - k8s/production-cluster
    - git/infrastructure-repo
  Valid Until: 2024-12-10T15:30:00Z

As the agent operates, TigerAccess tracks operations against this budget. When 80% is consumed, we emit warnings. At 100%, the certificate is revoked and the agent must re-authenticate with fresh authorization.

Circuit Breakers and Anomaly Detection

Beyond static limits, we've implemented dynamic circuit breakers that monitor agent behavior in real-time. Our system establishes baseline behavioral patterns for each agent and automatically intervenes when anomalies occur.

For example, if a deployment agent that typically accesses 5-10 Kubernetes pods suddenly attempts to access 500, the circuit breaker trips. The certificate is temporarily suspended, security teams are alerted, and the agent must pass additional checks before resuming operation.

MCP Protocol Integration

We've built first-class support for the Model Context Protocol (MCP), allowing AI agents to securely access infrastructure through standardized interfaces. MCP servers run within TigerAccess proxy infrastructure, inheriting all safety controls.

# AI agent accessing Kubernetes via MCP
import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    tools=[{
        "type": "mcp_server",
        "uri": "tigeraccess://k8s-prod",
        "credentials": "auto"  # Uses TigerAccess certificate
    }],
    messages=[{
        "role": "user",
        "content": "Scale the web-app deployment to 10 replicas"
    }]
)

# TigerAccess enforces:
# - Agent has k8s/production-cluster in allowed resources
# - Action budget not exceeded
# - Rate limits respected
# - Full audit trail logged

Comprehensive Audit Trail

Every AI agent action flows through TigerAccess audit logging. Unlike traditional logs that capture "what happened," our audit events capture the complete decision context: the agent's identity, the resource accessed, the action performed, the authorization decision, and critically—the reason provided by the agent.

This "reason" field is mandatory for all AI agent operations. It creates a chain of reasoning that security teams can review during incidents and compliance audits can verify.

Getting Started

AI agent identity is available in TigerAccess Enterprise starting today. Here's how to create your first AI agent:

# Create AI agent identity
tacctl agents add \
  --name=deployment-agent \
  --type=ai \
  --default-ttl=1h \
  --action-budget=200 \
  --rate-limit=10/sec

# Grant access to specific resources
tacctl agents grant deployment-agent \
  --resources=k8s/prod-cluster,git/infra-repo \
  --roles=deployer

# Agent authenticates and receives certificate
tac agent login --agent-id=deployment-agent

# Use certificate for infrastructure access
export TIGERACCESS_CERT=~/.tigeraccess/agent-cert.pem
kubectl get pods  # Proxied through TigerAccess

Looking Forward

This is just the beginning of our AI agent platform. Over the next quarter, we're shipping:

• Agent SDKs for Python, TypeScript, and Go with built-in safety controls
• Behavioral fingerprinting for more sophisticated anomaly detection
• Agent-to-agent authorization for complex multi-agent workflows
• Compliance reporting specifically designed for AI agent operations

As AI agents become more capable and autonomous, the security and compliance requirements will only increase. We're committed to building the infrastructure that makes AI agent deployment safe, auditable, and compliant.