Most existing tools scan before installation, then walk away. AgentWard and AgentWarden both enforce at runtime — here's how they differ.
| Capability | Cisco Scanner | Caterpillar | Snyk mcp-scan | SecureClaw | AgentWarden | AgentWard |
|---|---|---|---|---|---|---|
| Core Capabilities | ||||||
| Static scanning | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Runtime enforcement | ✗ | ✗ | Proxy† | Plugin‡ | ✓ | ✓ |
| Declarative policy (YAML) | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ |
| Skill chaining control (lateral movement) | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| Data flow classification | ✗ | ✗ | PII† | ✗ | ✗ | ✓ |
| Structured audit trail | ✗ | ✗ | ✗ | ✗ | ✓ | ✓ |
| Human-in-the-loop approval | ✗ | ✗ | ✗ | ✗ | Admin§ | ✓ |
| Enforcement Model | ||||||
| Enforced in code (not prompts) | ✗ | ✗ | Partial† | ✗ | ✓ | ✓ |
| Outside LLM context window | — | — | Partial | ✗ | ✓ | ✓ |
| Prompt-injection resistant | — | — | Partial | ✗ | ✓ | ✓ |
| Coverage | ||||||
| OpenClaw skills | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| MCP servers | ~ | ~ | ✓ | OC only | ✓ | ✓ |
| Python SDK tools | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| Compliance | ||||||
| Compliance mapping | ✗ | ✗ | ✗ | OWASP | OWASP§ | ✓ 4 frameworks |
| SOX / GDPR / PCI-DSS | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| Auto-fix compliant policy | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
| License & Source | ||||||
| Source-available (BUSL 1.1) | ✗ | ✓ | Partial | ✗ | Partial§ | ✓ BUSL 1.1 |
† Snyk: proxy mode available but limited to MCP; PII detection is advisory. ‡ SecureClaw: injects policy rules as natural language into LLM context — vulnerable to prompt injection by design. § AgentWarden: admin approval workflows (not per-call interactive); OWASP/MITRE ATLAS mapping; CLI is open source, cloud enrichment is SaaS.
These tools aren't exactly competing — they address different layers of the stack. AgentWard is the only one focused on runtime enforcement at the tool-call level.
Static analysis at install time. Excellent for discovering vulnerabilities before deployment. Nothing happens at runtime — the enforcement responsibility shifts to you.
Injects restriction rules as natural language into the LLM context. The LLM reads them and tries to comply — but these rules live inside the context window and can be bypassed by prompt injection or agent reasoning errors.
Control access at the identity and infrastructure level. Excellent for who-can-call-what at the service boundary. AgentWard complements IAM — it adds per-tool-call policy enforcement at the agent layer, not the infra layer.
Fleet-wide install-time gating and command interception via a Go binary wrapper. Strong on supply-chain scanning (SAST, CVEs, secrets, SBOM) with cloud-enriched trust scoring. Enterprise SaaS model with continuous monitoring and SIEM integration.
Code-level enforcement in a transparent proxy, enforcing at every tool call, completely outside the LLM context window. Source-available under BUSL 1.1. Differentiators: skill chaining / lateral movement detection, data flow classification (PII/PHI), multi-framework compliance (HIPAA, SOX, GDPR, PCI-DSS) with auto-fix, and per-call human-in-the-loop approval.
Being clear about scope matters. AgentWard is specifically the tool-call enforcement layer. It doesn't try to be everything.
Cisco Skill Scanner, Caterpillar, and Snyk mcp-scan all scan at install time and produce useful reports. AgentWard can ingest their scan results as input — it doesn't duplicate that work, it adds the runtime layer they don't have.
NeMo Guardrails and Guardrails AI focus on LLM input/output validation. AgentWard focuses on tool calls — what the agent actually does in the world, not what it says.
AgentWard complements your existing IAM. IAM controls infrastructure-level access. AgentWard adds agent-layer enforcement: per-skill, per-tool, per-call policy evaluation that IAM can't see.
Making the LLM itself safe is Anthropic's and OpenAI's problem. AgentWard assumes the model layer is handled and focuses exclusively on the tool/skill layer — what actions the agent is permitted to take.