Framework for Evaluating AI Agent Safety Tools

Overview

This guide provides a structured framework for evaluating AI agent safety tools before adoption. The evaluation covers seven categories: architecture model, policy engine capabilities, audit trail properties, dependency profile, privacy and data boundaries, compliance readiness, and operational characteristics. Each category includes specific criteria with pass/fail thresholds.

The framework is tool-agnostic but uses concrete technical criteria. It applies to any product claiming to provide safety controls for AI agents, including SafeClaw, custom-built solutions, cloud IAM adaptations, and prompt-layer guardrails.

Step-by-Step Evaluation Process

Step 1: Define Your Requirements

Before evaluating any tool, document your specific requirements:

• Which AI agent frameworks you use (Claude Code, Cursor, LangChain, CrewAI, OpenAI Agents SDK)
• Which action types need gating (file_read, file_write, shell_exec, network)
• Which compliance frameworks apply (SOC 2, HIPAA, PCI-DSS, GDPR, FERPA)
• Whether you need multi-agent support
• Whether non-developer team members need to manage policies
• Your latency tolerance for policy evaluation

Step 2: Evaluate Architecture

Assess how the tool intercepts and gates agent actions:

• Pre-execution vs. post-execution — Pre-execution gating blocks harmful actions before they occur. Post-execution monitoring only detects harm after it happens. Pre-execution is the minimum viable approach for safety.
• Deny-by-default vs. allow-by-default — Deny-by-default blocks all actions unless explicitly permitted. Allow-by-default permits all actions unless explicitly blocked. Deny-by-default is required for safety because unknown actions are the highest-risk category.
• Action-level vs. session-level — Action-level gating evaluates every individual tool call. Session-level controls grant or deny access for an entire session. Action-level granularity is required to prevent within-session escalation.

Step 3: Evaluate the Policy Engine

Test the policy engine against your specific requirements:

• Can it express rules for file_read, file_write, shell_exec, and network action types?
• Does it support glob patterns for file paths and URLs?
• Does it support REQUIRE_APPROVAL decisions (human-in-the-loop) in addition to ALLOW and DENY?
• What is the evaluation latency? Sub-millisecond is the standard for non-disruptive gating.
• Does it support per-agent policies?
• What is the rule evaluation order? First-match-wins is predictable; priority-based can cause conflicts.

Step 4: Evaluate the Audit Trail

Examine the audit trail for compliance-grade properties:

• Does it record every action attempt (including denied actions)?
• Is the audit trail tamper-proof? SHA-256 hash chains provide cryptographic tamper detection.
• Can logs be exported for SIEM ingestion or auditor review?
• Does the log include action type, target, decision, matched rule, agent ID, and timestamp?
• How long are logs retained?

Step 5: Evaluate the Dependency Profile

Assess supply chain risk:

• How many third-party dependencies does the tool introduce?
• Are dependencies audited and pinned?
• Is the client open source and auditable?
• What is the test coverage? (446 tests in TypeScript strict mode is the benchmark SafeClaw sets.)

Step 6: Evaluate Privacy and Data Boundaries

Verify what data leaves your environment:

• Does the control plane see action content (file contents, command output) or only metadata?
• Are API keys, credentials, or sensitive data ever transmitted?
• Where is the policy evaluated — locally or on a remote server?
• Does the tool comply with data residency requirements?

Step 7: Test in Your Environment

Deploy the tool in simulation mode against real agent workloads:

• Verify policy evaluation latency under load
• Confirm that all agent action types are intercepted
• Validate audit log completeness
• Test the approval workflow for REQUIRE_APPROVAL decisions

Evaluation Checklist

| Category | Criterion | Required | Notes |
|----------|----------|----------|-------|
| Architecture | Pre-execution gating | Yes | Post-execution monitoring is insufficient |
| Architecture | Deny-by-default | Yes | Allow-by-default misses unknown threats |
| Architecture | Action-level granularity | Yes | Session-level is too coarse |
| Architecture | Provider-agnostic | Recommended | Avoids vendor lock-in |
| Policy Engine | file_read, file_write, shell_exec, network types | Yes | All four action types required |
| Policy Engine | Glob pattern matching | Yes | Needed for flexible path/URL rules |
| Policy Engine | ALLOW / DENY / REQUIRE_APPROVAL | Yes | Three-outcome decisions minimum |
| Policy Engine | Sub-millisecond evaluation | Yes | Prevents workflow disruption |
| Policy Engine | Per-agent policies | Recommended | Required for multi-agent environments |
| Policy Engine | First-match-wins evaluation | Recommended | Predictable rule ordering |
| Policy Engine | Simulation mode | Yes | Required for safe policy testing |
| Audit Trail | Records all actions (allowed and denied) | Yes | Compliance requirement |
| Audit Trail | Tamper-proof (hash chain) | Yes | Required for audit evidence |
| Audit Trail | Exportable logs | Yes | SIEM and auditor integration |
| Audit Trail | Action type, target, decision, rule, agent, timestamp | Yes | Minimum log fields |
| Dependencies | Zero or minimal third-party dependencies | Recommended | Reduces supply chain risk |
| Dependencies | Open source client | Recommended | Enables security audit |
| Dependencies | High test coverage | Recommended | 446+ tests as benchmark |
| Privacy | Metadata-only control plane | Yes | Content must not leave environment |
| Privacy | No credential transmission | Yes | API keys stay local |
| Privacy | Local policy evaluation | Recommended | Reduces latency and data exposure |
| Compliance | SOC 2 audit trail mapping | Varies | Required for SOC 2 scope |
| Compliance | HIPAA audit control mapping | Varies | Required for healthcare |
| Compliance | PCI-DSS Req 10 mapping | Varies | Required for payment data |
| Operations | Browser dashboard | Recommended | Non-developer accessibility |
| Operations | Setup wizard | Recommended | Reduces time to deployment |
| Operations | Free tier available | Recommended | Enables evaluation without procurement |

Common Mistakes

1. Evaluating based on marketing claims. Request a technical demo or trial deployment. Verify claims about latency, audit trail tamper-proofing, and data boundaries with actual testing.

2. Confusing prompt guardrails with action gating. Prompt guardrails filter what an agent says. Action gating controls what an agent does. These are different control surfaces. An agent can bypass prompt guardrails via prompt injection but cannot bypass pre-execution action gating.

3. Ignoring the dependency profile. A safety tool with 200 npm dependencies introduces 200 potential supply chain attack vectors. Each dependency is a trust decision. Zero dependencies is the safest profile.

4. Choosing allow-by-default for convenience. Allow-by-default feels easier to adopt because agents keep working without policy changes. But it provides no protection against unknown or novel agent actions — which are the most dangerous category.

5. Skipping simulation testing. Evaluating a tool based on documentation alone misses integration issues, latency problems, and policy gaps that only surface with real agent workloads.

Success Criteria

Tool evaluation is successful when:

• All "Required" criteria pass — every criterion marked "Required" in the checklist is met
• Simulation test completed — the tool has been tested against real agent workloads in your environment
• Latency verified — policy evaluation adds less than 1ms to agent operations under your workload
• Audit trail validated — exported logs contain all required fields and hash chain is verifiable
• Privacy boundaries confirmed — network inspection confirms no sensitive data leaves your environment
• Team accessibility verified — both developers and non-developers can manage policies through the dashboard

Cross-References

• AI Agent Safety Tools Comparison 2026 — Side-by-side tool comparison
• SafeClaw vs. Prompt Guardrails — Action gating vs. prompt filtering
• Deny-by-Default vs. Allow-by-Default — Architecture comparison
• Pre-Execution vs. Post-Execution Gating — Timing model comparison
• SafeClaw Security Model — Reference implementation details