AgentWard enforces what they're allowed to touch — in code, not prompts. Scan, enforce, and audit every tool call.
Cisco, Snyk, and Caterpillar all scan before installation — then walk away. OpenClaw has 140K stars and gives agents full computer control with zero runtime governance. Prompt-based guardrails can be bypassed by prompt injection. You need enforcement in code, outside the LLM context window entirely.
Static scanners that check at install time, then stop watching. No runtime enforcement. No audit trail. No skill chaining controls. Lateral data movement between tools goes undetected. You're left hoping your prompts hold.
"Telling an agent 'don't touch the stove' is a natural-language guardrail that can be circumvented. AgentWard puts a physical lock on the stove — code-level enforcement that prompt injection can't override."
AgentWard scans every tool your agent can reach, risk-rates them, detects dangerous skill chains (lateral data movement between tools), generates a policy, and wires enforcement — all in seconds.
Run agentward init — or go step by step.
Map every tool your agent can reach. Risk ratings, data access patterns, dangerous skill chains — lateral movement paths that existing DLP and IAM don't cover.
Generate smart-default YAML policies from scan results — tailored to your use-case pattern.
Runtime proxy intercepts every tool call. Block, redact, or require human approval — in real time.
Evaluate policies against HIPAA, SOX, GDPR, PCI-DSS. Auto-generate compliant policy YAML with fixes applied.
Python 3.11+ · No API key required · Everything runs locally · Mac + Linux
5 seconds to see what your AI agent's tools can actually do.