Agnost AI vs A.I.G (AI Infra Guard): Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Agnost AI and A.I.G (AI Infra Guard) — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Agnost AI
Agnost Tech Inc
Product analytics for conversational agents that surfaces silent failures, user frustration and policy violations across every conversation.
Key features
- Silent Failure Detection: Reads each trace next to the conversation to catch cases where the run reported success but the user got nothing useful, including broken promises and confidently wrong answers.
- Automatic Conversation Clustering: Turns thousands of chats into ranked recurring problems, ordered by user impact and ready to investigate rather than left as raw logs.
- Frustration and Churn Signals: Pinpoints where users rage-prompt, get stuck or abandon the conversation, so churn drivers are visible before the user leaves.
- Policy and Quality Violation Alerts: Flags hallucinations and quality, policy and compliance breaches with the exact conversation and trace behind each one.
- Evidence-Backed Fix Recommendations: Hands over the highest-impact fixes with supporting evidence, a recommended change and the evals needed to ship it safely.
- Two-Step Skill Install: Connects to an existing agent by installing an agent skill and running one prompt, with no rebuild of the agent and no separate implementation project.
- Feature Request Mining: Surfaces what users repeatedly ask for across conversations, turning support volume into a prioritised roadmap signal.
- Live Demo Without Signup: Ships a public interactive demo where you can click any insight and inspect the underlying conversations before creating an account.
Best for
- Diagnosing Agent Churn: Finding the recurring conversation pattern that makes users abandon a support agent, with the specific chats as evidence.
- Auditing Production Agents for Compliance: Reviewing conversations for policy violations and unsupported claims across real traffic rather than a hand-picked sample.
- Prioritising Agent Improvements: Deciding which prompt or flow to fix next based on how many users hit each failure cluster instead of on anecdote.
- Catching Regressions After a Prompt Change: Watching whether a newly shipped change increases silent failures or user frustration in live conversations.
- Building Evals from Real Failures: Turning observed production failures into regression evals so the same bug does not ship twice.
- Mining Conversations for Roadmap Input: Extracting repeated feature requests from support and sales chats to feed product planning.
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.