Govern every AI Agent your enterprise depends on.
Building with design partners across banking, healthcare, and public-sector teams in APAC.
Old software waits. AI agents act.
Agents call tools, read customer data, and trigger payments on their own — but the controls your enterprise relies on were built for software that waits. So when an agent heads for production, three questions block approval.
The CISO’s question — who owns the agent, why its access keeps growing, and which shadow agents nobody registered.
The compliance question — the proof a regulator wants, scattered across a dozen tools and inboxes.
The CFO’s question — no way to tie spend to a specific agent, team, or business outcome.
The thing standing between a promising pilot and a production system is not smarter AI. It is governance.
Two products. One agent governance mission.
AgentGuardian Open Source.
Open-source red teaming for AI agents.
For developers, security engineers, and AI builders who want to test agents locally or in CI/CD.
- Prompt injection testing
- Tool abuse testing
- RAG poisoning testing
- Memory attack testing
- AIVSS scoring
- Local reports
- SARIF / JSON / HTML exports
- CI/CD gate
$ pip install agent-guardian
$ agent-guardian scan ./my_agent.py
$ agent-guardian serveAgentGuardian Enterprise.
Enterprise AI agent governance platform.
For security, risk, compliance, and AI platform teams governing agents across the organization.
- Agent discovery
- Agent registry & shadow-agent inventory
- Lifecycle approvals · Agent Contracts
- Scheduled & continuous scans
- Runtime policy enforcement
- Continuous monitoring
- Drift detection & re-approval
- Per-agent cost tracking (early access)
- Signed evidence packs
- SSO · RBAC · audit logs
- Customer-resident deployment
What AgentGuardian Enterprise does.
Govern
Make every agent accountable before it runs — an owner, a purpose, a risk tier, an approval, and a review-or-retire date. No more anonymous agents.
Secure
Run continuous adversarial red teaming against prompt injection, tool abuse, RAG poisoning, memory attacks, supply-chain risk, and agent-to-agent compromise — and block risky releases before they ship.
Measure
Tie every token and every dollar to a specific agent, team, and budget — per-agent cost metering, budget alerts, and departmental showback. Early access.
Enforce
Turn governance into real control — least-privilege access validated against each contract, unsafe actions denied by your cloud's own controls, and per-agent and fleet-wide kill switches that cut access in seconds. Nothing sits in the request path.
Prove
Generate signed evidence packs with attack traces, AIVSS scores, findings, remediation guidance, and governance mapping.
How AgentGuardian works.
Discover and register
Find every agent in your estate — including the ones nobody registered — and give each an owner, a purpose, and a risk tier.
Approve with a contract
Risk-tiered approval produces a signed Agent Contract that spells out exactly what the agent is allowed to do.
Attack before attackers do
Run adversarial probes against prompt, tools, RAG, memory, and multi-agent behavior — AIVSS-scored, with unsafe releases blocked.
Enforce in your cloud
Least-privilege access validated against the contract, deny rules in your cloud's own controls, and a kill switch that revokes in seconds.
Prove it, and price it
Signed evidence packs for audit and governance review — and every token and dollar tied to a specific agent and budget.
Runs inside your cloud account.
The data plane installs into your own AWS account. We operate the control plane — orchestration and policy, never your data.
Your AWS account.
The data plane. Everything sensitive is generated, processed, and stored inside your walls.
- Prompts & tool calls
- Agent telemetry & logs
- Evidence packs
- Signing keys · your KMS
Our control plane.
Scan orchestration, policy authoring, and dashboards — metadata only, never payloads.
- Scan orchestration
- Policy authoring
- Posture dashboards
- No prompts · no keys
Your prompts, telemetry, and keys never leave your walls — the design choice that lets a regulated bank say yes.
AWS today — discovery for Google Vertex AI and Azure OpenAI is next on the roadmap.
Audit-ready evidence from real adversarial tests.
AgentGuardian does not rely only on questionnaires or posture inference. Every assessment can produce a signed evidence pack with attack traces, scores, findings, and framework mapping.
- Agents in scope
- Attack transcript
- AIVSS score
- Findings by severity
- Policy decisions
- Remediation guidance
- Framework mapping
- Verification manifest
EP-2026-Q1-0007
These are not optional and the clocks are running — MAS’s guidelines carry a 12-month transition, APRA CPS 230 is already in force, and the EU AI Act’s high-risk obligations bind in 2026.
SAFR-native runtime governance for every AI agent action.
On 3 July 2026 the Monetary Authority of Singapore published Safeguards for Agentic Finance at Runtime (SAFR) — the industry framework MAS expects every FSI enterprise in Singapore to implement for governing AI agents at runtime. AgentGuardian was built to SAFR's exact pattern before SAFR existed: policy-bound execution, real-time validation, tamper-evident audit, and interoperability.
MAS's own guidance calls out gaps SAFR does not close — prompt injection, memory poisoning, tool supply-chain trust, multi-agent collusion. AgentGuardian carries those risks as first-class controls in the same runtime plane, so a single deployment satisfies the SAFR baseline and the broader agent security perimeter.
Built for the teams accountable for AI agents.
Find and test the real AI agent attack surface.
Continuous adversarial red teaming against prompt injection, tool abuse, RAG poisoning, memory attacks, and agent-to-agent compromise — across every agent in your estate.
Turn AI governance into signed, reviewable evidence.
Every assessment produces a signed evidence pack with attack traces, AIVSS scores, findings, remediation guidance, and framework mapping for audit and regulator review.
Give developers open-source red teaming, keep production governed.
Developers run AgentGuardian Open Source locally and in CI/CD. The same engine powers AgentGuardian Enterprise — so what developers see in build is what the security team sees in production.
Prove which agents earn their keep.
Per-agent cost metering ties every token and every dollar to a specific agent, team, and budget — with budget alerts and departmental showback for finance review. Early access.
Frequently asked questions.
Is AgentGuardian Open Source the full platform?
Is AgentGuardian a guardrail?
Does our data leave our environment?
Start with red teaming.
Scale to enterprise governance.
Run AgentGuardian Open Source locally in minutes, or book a demo to see the enterprise governance platform.