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HarnessHub
AI Agent Governance Platform
An enterprise lifecycle platform for AI agents that unifies models, agents, MCP, Skills, and memory assets, covering discovery, admission, runtime monitoring, and risk protection.
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100K+ IPs · 100 asset types covered
0 types
Jailbreaks, prompt injection, tool abuse, data leaks, agent overreach, and more
<0 ms
From threat detection to blocking, with no business impact
Introduction
See every AI asset.
Control every agent action.
Agents can invoke tools, access data, and execute code. As platforms and tools proliferate, enterprises need to uncover shadow AI, clarify ownership and permissions, and control admission, launch, and runtime risks.
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Gate admission and protect every action at runtime
HarnessHub governs models, agents, MCP servers, Skills, and memory assets. Static checks and adversarial evaluation control admission, while call tracing, decision reconstruction, and policy orchestration detect and contain runtime risks.
Asset overview
Found0
Governed0
MCP0
Memory0
Agent0
Skills0
Pre-launch evaluation
All domains
Search apps / owners
Search apps / ownersDomainPassed / total apps
R&D0 / 0Marketing0 / 0Support0 / 0Finance0 / 0People & admin0 / 0Total appsR&DMarketingSupportFinancePeople & admin
Unified policy delivery
DeliveringSensitive-data egress approvalv2.4
- Scope
- Admitted apps with external send
- Targets
- 0 apps
R&D docs assistant
Delivered
Campaign model
Pending
Support agent
Failed
Risk event: Tried to skip approval and call an external send tool
Suggested extra policyPending · not delivered
Identity claim vs. authorization checkAn in-chat admin claim cannot replace system authorization. Scope: admitted apps with external send capability.
Govern the complete enterprise AI agent lifecycle.
Gate admission, protect runtime activity, and build a governance closed loop.
Asset governance
Discover shadow AI and build one asset inventory
Discover unregistered agents on the internal network and catalog models, agents, MCP tools, Skills, and memory assets with clear type, location, ownership, and status—providing a complete asset list for downstream admission reviews.
- Probe single IPs, subnets, and CIDR ranges with three-stage agent identification—no endpoint install
- Record IP, port, type, fingerprint, and discovery time; support patrols, trends, and Excel export
- Register multi-vendor models, manage versions, and enforce memory access controls
Admission control
Review tools and skills, tighten access permissions
Control external agent entry points, MCP tools, and Skills through managed proxies, static risk checks, and credential authorization. Failed tools and skill packs cannot enter the pool; only authorized MCP tools may be exposed externally.
- Register A2A agent endpoints and auth; manage Agent Cards and proxy entry points
- Assess MCP transport, boundary, execution, and supply-chain risk; report and narrow permissions
- Detect Skills injection, data exfiltration, and destructive actions; reuse conclusions for unchanged packs
Compliance evaluation
Run dynamic adversarial tests before model and agent launch
Set pre-launch evaluation gates for models and agents. Combine standard benchmarks with scenario red teaming, configurable adversarial depth, and progressive attack paths. Failed evaluations block launch, retain pending status, and surface remediation guidance.
- Cover prompt injection, content safety, data leakage, instruction following, and tool-call safety
- Map to GB/T 45654-2025, NIST AI RMF, EU AI Act, OWASP LLM Top 10, and more
- Automate adversarial runs with scores, results, and remediation suggestions
Runtime protection
Orchestrate policies and contain runtime risk in real time
During model and agent runtime, HarnessHub orchestrates policies on top of Jundun text-risk detection—binding protection targets, choosing enforcement actions, and applying them on the traffic path as concrete call and content handling.
- Differentiate protection by model, agent, or credential
- Block high-risk calls with alerts and mask sensitive data in real time
- Rewrite intent and use safe automated answers for risky content
Behavior observation
Reconstruct calls and decisions for audit and continuous operations
Continuously record models, token usage, latency, and status into dashboards and trace logs—helping teams understand consumption, find anomalies, trace agent behavior, and inform retesting and governance improvements.
- Track token usage by app, model, agent, and team with budgets, alerts, and overspend blocks
- Trace tool calls and decision nodes; link traces, agents, and key IDs to replay execution
- OpenTelemetry-ready integration with APM, Langfuse, and internal systems for external agent calls
Connect asset governance, policy enforcement, and risk auditing
Make enterprise AI manageable, controllable, and traceable
Enterprise AI asset overview
When AI assets are scattered, poorly owned, or shadow AI is hard to find, internal scans and scheduled inspections surface unregistered agents. Inventory models, agents, MCP, Skills, and memory, then track changes and export lists to support onboarding review and governance.
Unified enterprise AI protection policies
Orchestrate and deliver consistent policies across departments, models, and agents. Bind controls by model, agent, or key, then block, rewrite intent, answer safely, or mask sensitive data.
Enterprise AI risk overview and traceable auditing
When enterprise AI risk signals are scattered and incidents are hard to locate or trace, dashboards and logs summarize runtime status, risks, and enforcement. Link traces, agents, and key IDs, follow tool calls and decision nodes, and reconstruct each incident for investigation, accountability, retesting, and governance.
Internal network discovery
R&D
Operations
Data team
10.20.8.0/24
OpenClaw Unregistered
OpenClaw
Owner pendingFound 15:0010.20.8.23 : 18789
Asset changes
Scheduled scan
Unified asset ledger
128 assetsAssetOwnerStatus
OpenClawOperationsUnregistered
Qwen2.5R&DRegistered
Database MCPData teamRegistered
Report SkillOperationsRegistered
Project MemoryR&DRegistered
Showing 5 of 128 assetsA governance loop
that keeps AI continuously trustworthy
Plan AI
A risk governance loop across the full AI lifecycle—linking compliance registration, asset discovery, evaluation, control, and audit end to end to help enterprises operationalize AI planning systematically and avoid fragmented responses.
Discover AI
Make enterprise AI assets visible and controllable. Automatically inventory models, agents, skills, MCPs, and more to surface risk exposure and inform governance decisions.
Evaluate AI
Deep pre-launch risk evaluation to keep hazards out of production. Systematically assess agents, skills, and supply chains for hallucination, overreach, and privacy risks.
Protect AI
Security across agent runtime and multimodal content. Real-time checks and intervention on inputs and outputs, plus AIGC authenticity, tamper detection, provenance, and copyright risk review—balancing speed and governance.
Audit AI
Adaptive compliance for regulatory needs. Full-chain behavioral logging, iterative policy tuning, and support for registration requirements—reducing ongoing compliance cost.
Make every agent call controllable and traceable
Whether you need an enterprise AI inventory, unified protection policies, or risk auditing, HarnessHub connects admission, evaluation, runtime protection, and evidence.
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