Agent Native vs A.I.G (AI Infra Guard): Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Agent Native and A.I.G (AI Infra Guard) — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
A
Agent Native
Builder.io
Open-source framework for building agents that act inside real apps, with shared actions, SQL-backed state, tools, and observability.
Key features
- Shared Actions: Define work once and invoke it from UI, agent, API, MCP, A2A, and CLI.
- Agent Runtime: Bundles chat, tools, skills, memory, jobs, observability, and handoffs together.
- Backend Agnostic: Plugs into any Drizzle-supported SQL database and Nitro-compatible host.
- SQL-Backed State: Persists agent state in your own database for reliability and inspection.
- Open-Source Templates: Cloneable, fully owned SaaS app templates you can customize end to end.
- Observability: Built-in tracing and monitoring for agent behavior in production apps.
Best for
- Agentic SaaS: Build production apps where agents act inside the product, not beside it.
- Action Reuse: Expose one action set across UI, API, MCP, and CLI consistently.
- Custom Stack: Ship agents on your own database, host, and model choices.
- Template Bootstrapping: Start from a complete open-source SaaS template and own the code.
- Observable Agents: Add memory, jobs, and observability to long-running agent workflows.
A.I.G (AI Infra Guard)
Tencent Zhuque Lab
Tencent's open-source AI red teaming platform for scanning agents, agent skills, MCP servers and AI infrastructure, plus LLM jailbreak evaluation.
Key features
- Agent Skills Scan: Audits agent skill packages against a nine-category risk taxonomy aligned with the public SkillTrustBench T01-T09 classification, including detection of .pyc bytecode bypasses and charset smuggling.
- MCP Server Scan: Inspects MCP servers for threats such as tool poisoning, credential exfiltration and command injection, with tool whitelisting to prevent remote code execution during dynamic scanning.
- AI Infrastructure Vulnerability Scanning: Checks deployed AI components against a library that has grown to roughly 130 components and over 2,000 CVE rules, covering frameworks such as llama.cpp.
- Jailbreak Evaluation: Runs single-turn jailbreak operators plus multi-turn attack techniques including Many-Shot, PAIR, GOAT and ActorAttack to measure a model's resistance.
- Agent Scan with OWASP Coverage: Assesses running agents using OWASP-derived skills and web exfiltration detection, with a dedicated agent red team skill for comprehensive assessment.
- Standalone Scanner CLIs: skill-scan, mcp-scan and agent-scan each install as an independent command-line tool so scans can be wired directly into enterprise CI/CD pipelines.
- Docker Deployment with Web UI: Deploy the full platform with Docker on 4GB+ RAM and reach the web interface at localhost:8088, or use a one-click install script or a source build.
- AI Security Skill Market: A catalog of official security scanning skills, with the frontend fully open-sourced and integration available from OpenClaw chat via the aig-scanner skill.
Best for
- Pre-Deployment Agent Audit: Scan an internally built agent and its skill bundle for prompt injection, exfiltration and privilege risks before it is released to staff.
- MCP Supply Chain Review: Vet third-party MCP servers for tool poisoning and credential exfiltration before connecting them to production assistants.
- CI/CD Security Gate: Run skill-scan as a standalone CLI on every pull request so risky agent skills fail the build rather than shipping.
- Model Safety Benchmarking: Measure how a deployed LLM holds up against single and multi-turn jailbreak techniques before and after guardrail changes.
- AI Infrastructure Patch Triage: Inventory AI serving components and match them against the CVE rule library to prioritise which hosts need patching.
- Security Research and Reporting: Use the open scan engines and SkillTrustBench alignment as a reproducible basis for internal or published AI security research.