Safety & Control
Guardrails, sandboxing, permission systems, and failure modes.
Agent Guardrails
Agent guardrails are programmable safety layers that intercept agent inputs, outputs, and actions to detect and block harmful, unsafe, or policy-violating behavior through multi-layer defense including input guards, output guards, and action guards.
Agent Sandboxing
Agent sandboxing constrains the execution environment of AI agents using container isolation, network restrictions, and filesystem limits to ensure that even if an agent behaves unexpectedly, the damage it can cause is bounded.
Alignment for Agents
Alignment for agents ensures that AI agents faithfully pursue their intended goals and follow their instructions without gaming specifications, finding loopholes, or optimizing for metrics at the expense of the actual objective, while balancing safety constraints with practical helpfulness.
Authorization and Permissions
Authorization and permissions control what resources and actions an AI agent can access, applying the principle of least privilege through scope-based permissions, credential management, and dynamic access control to minimize the damage from agent errors or compromise.
Human-in-the-Loop
Human-in-the-loop patterns require agent actions to be approved by a human before execution, creating safety checkpoints for destructive, costly, or irreversible operations while balancing safety with usability.
Monitoring and Observability
Monitoring and observability provide real-time visibility into agent behavior through tracing, metrics, anomaly detection, and dashboards, enabling operators to detect problems, understand failures, and maintain production agent reliability.
Prompt Injection Defense
Prompt injection defense protects AI agents from adversarial inputs that attempt to override system instructions, using multi-layer defenses including input sanitization, instruction hierarchy, output monitoring, and architectural isolation to prevent both direct and indirect injection attacks.
Resource Limits
Resource limits prevent runaway agent execution by enforcing token budgets, time limits, cost caps, and iteration maximums, acting as circuit breakers that ensure agents fail safely rather than consuming unbounded resources.
Rollback and Undo
Rollback and undo mechanisms enable the reversal of agent actions through version control, database transactions, compensating actions, and checkpoint strategies, ensuring that agent mistakes are recoverable rather than permanent.
Trust Boundaries
Trust boundaries define different trust levels for different data sources entering an agent system – from high-trust system instructions to low-trust retrieved documents – and use these levels to govern how the agent processes, weights, and acts on information from each source.