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Security, policy and audit controls for AI agent tool execution. Provides pre-flight checks, risk scoring, approval gates, audit logging, and rate limiting for tool calls.
AgentGuard defines 7 tools with schemas and descriptions present, but quality is inconsistent. All tools have input schemas with typed parameters and descriptions, meeting baseline requirements. However, descriptions are generic (avg ~60 chars, well below the 194-char production baseline), lack actionable context for LLM selection, and omit critical details like when to use each tool vs. alternatives. Tool names follow verb_noun convention (policy_preflight, tool_risk_score, audit_log_write) but lack clarity on composition, it's unclear how these tools chain together in a typical agent workflow. Error handling is absent from visible code; no recovery guidance, retryability classification, or actionable error messages. Output schemas are not documented. The audit_log_write tool accepts an enum for 'decision' but lacks guidance on when to use each value. Rate limiting and approval workflows are present but underspecified.
Human approval gate check
Query audit trail (filter by agent/tool/time)
Write call to persistent audit log
Explain allow/deny with policy reference
Pre-flight check before any tool call
Check if agent exceeded rate limits
0-100 risk score for tool + input
Descriptions are too brief and lack LLM-optimized context. Average ~60 chars vs. production baseline of 194 chars. E.g., 'Pre-flight check before any tool call' does not explain WHEN to call it, what it returns, or how it differs from tool_risk_score.
No output schemas documented. LLMs cannot plan downstream calls or extract required fields (e.g., does policy_preflight return a boolean, a decision object, or a list of matching policies?). This violates the documented output schema requirement.
No error handling guidance visible. Code does not show recovery hints, retryability classification, or actionable error messages. E.g., if approval_required fails because request_id is not found, what should the agent do next?
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 44 | <=2025-11-25 | v2 |
Tool composition is unclear. No documentation on how policy_preflight, tool_risk_score, approval_required, and audit_log_write chain together in a typical security gate workflow. Agents cannot infer the intended sequence.
audit_log_query accepts optional filters but lacks pagination guidance. No documented limit on result size, no mention of default/max page size, and no next_cursor or total_count in visible schema. Large result sets could exhaust context.