Data Minimization and Least-Privilege Access Data minimization and least privilege are the two principles that keep AI systems from turning into accidental surveillance machines. Minimize what you collect and store. Restrict what the system can access and do. These controls protect users, reduce compliance burden, and shrink the blast radius of mistakes. The Principles in […]

Deterministic Modes for Critical Workflows Deterministic modes are essential when AI outputs must be reproducible: audits, compliance, financial workflows, and any system where inconsistent results cause operational damage. Determinism is not only temperature. It is the whole pipeline: prompt assembly, tool calls, retrieval, and validation. Sources of Non-Determinism | Source | Example | Mitigation | […]

Prompt Injection Hardening for Tool Calls Prompt injection is a system vulnerability, not a “prompting mistake.” Once an agent can call tools, untrusted text can attempt to override instructions, exfiltrate secrets, or trigger unsafe side effects. Hardening requires clear trust boundaries, strict tool schemas, and policy enforcement at execution time. Threat Model | Injection Vector […]

Source Trust Scoring and Safe Browsing Patterns Source trust scoring is how you keep browsing and retrieval grounded. Not all sources are equal, and not all pages are safe to treat as evidence. A trust system combines provenance, reputation, freshness, and internal policies to decide what can be cited and what must be treated cautiously. […]

Testing Agents with Simulated Environments Simulated environments are the fastest way to test agents safely. They let you run thousands of scenarios, inject failures, and measure behavior without touching production systems. The key is fidelity: the simulator must reproduce the constraints that matter, including permissions, timeouts, and tool schemas. What Simulators Are For Regression testing: […]