Observe & Monitor Agent Actions

Use Case

Continuously observe agent behavior, decisions, tool use, resource access, policy outcomes, and enforcement across runtime workflows. Correlate activity with agent identity, owner, delegated authority, purpose, and behavioral baselines so teams can distinguish expected automation from activity requiring investigation or intervention.

Challenges

Approved, third-party, and Shadow Agents can operate across many applications, tools, and frameworks. Conventional logs often lack the context needed to understand autonomous behavior:

• Isolated events omit agent identity, mission, and ownership
• Tool calls lack delegated-user and policy context
• Behavioral drift is difficult to distinguish from normal automation
• Multi-step workflows are hard to reconstruct after incidents

Solution

PointGuard AI provides a comprehensive solution for continuous, security-aware agent observability:

1. Identify monitored agents. Use AI Discovery & Inventory and Agent Mission Control to maintain visibility into governed, shadow, and connected agents.

2. Capture runtime activity. Collect OpenTelemetry-compatible traces for prompts, responses, actions, tool calls, MCP traffic, agent messages, enforcement events, and outcomes.

3. Add decision context. Correlate activity with verified identity, delegated authority, intent, target resource, policy result, approvals, and containment.

4. Detect and investigate anomalies. Use behavioral baselines and attributable audit evidence to identify drift, misuse, policy violations, and abnormal execution paths.

Security teams gain continuous context for investigation, accountability, and timely intervention while agents operate.

Risks Addressed

Applicable framework risks and controls include:

OWASP Top 10 for LLMs

• LLM03:2026 Excessive Agency

OWASP Top 10 for Agentic Applications

• ASI02: Tool Misuse and Exploitation
• ASI03: Identity and Privilege Abuse
• ASI07: Insecure Inter-Agent Communication
• ASI08: Cascading Failures
• ASI10: Rogue Agents

NIST AI Risk Management Framework

• GOVERN 2.1: Roles, responsibilities, and communication are documented and clear
• MAP 1.1: Intended use, context, users, and lifecycle risks are documented
• MEASURE 2.7: AI system security and resilience are evaluated and documented
• MANAGE 4.1: Post-deployment monitoring and change management are implemented