PRIVATE EARLY ACCESS

Govern what your AI agents do.
Recover when they don't.

AgentGuardian checks every tool call against your policies before it executes and recovers failing agents at runtime. Your agent code stays unchanged.

arep · live event stream
12:04:01 observation finance-agent read_file ✓ 84ms
12:04:02 observation finance-agent read_file ✓ 91ms
12:04:05 observation finance-agent read_file ↻ repeat ×5
12:04:07 failure    detector loop_detected risk=0.91
12:04:07 incident   P1 opened · blast_radius=2
12:04:08 recovery   engine retry_with_backoff
12:04:35 recovery   engine resolved · agent healthy
Topology finance-agent read_file exec_shell deploy_agent
Framework agnostic: @arep_agent MCP gateway REST API If your agent calls tools, AREP can govern it.
The problem

Finding failures after the fact doesn't protect anyone.

Agents loop, time out, and drift from their stated intent in production. Most teams learn about it from a pager, hours later, with no way to stop the next call. Observability tells you what went wrong. It does nothing to prevent it.

How it works

Intercept. Enforce. Recover.

A reliability layer between your agents and their tools, acting before users notice anything.

01

Intercept

Wrap your agent with one decorator or route tool calls through the gateway. AREP captures every invocation before it executes.

@arep_agent(agent_id="my-agent")
async def my_agent():
  # your existing code, unchanged
02

Enforce

Policies run before the tool does. Each call is allowed, denied, or held for human sign-off. A denied call never reaches the tool.

exec_shell   → DENY blocked by policy
deploy_prod  → APPROVAL awaiting sign-off
read_file    → ALLOW
03

Recover

When a failure is detected, the recovery engine acts on the incident immediately. How serious it is picks the action, while the agent is still running.

12:04:07 P1 opened failure detected
12:04:08 recovering automatic action
12:04:35 resolved agent healthy
Platform

The control plane your agents run without today

Enforce before execution

Every tool call is checked against your policies before it runs. Anything denied never reaches the tool.

Recover without a deploy

Failing agents are retried, rerouted, or isolated automatically, matched to how serious the incident is.

Incidents, not alert noise

Related failures roll up into one prioritized incident that tells the full story of what caused it.

Your fleet, live

Every agent and tool on one live map. Health changes and blocked calls appear the moment they happen.

Catch agents going off-script

Actions that drift from what the agent said it would do get flagged before they complete.

Audit-ready by default

A tamper-evident record of every decision, verifiable on demand when compliance asks.

Enterprise governance

Guardrails ready on day one

Start with vetted policy packs, then extend them with rules of your own. Every decision is recorded for audit.

  • Prebaked, enterprise-vetted guardrails

    Curated policy packs cover the riskiest tool surfaces, so agents are governed from the first call.

  • Bring your own policies

    Express your organization's rules and roll them out per agent, per tool, or across the whole fleet.

  • Rehearse before you enforce

    Run any policy in dry-run against live traffic and see what it would block before it blocks anything.

  • Humans stay in the loop

    Sensitive actions wait for sign-off, and critical incidents reach your on-call in the channels you already use.

policy packs
Production guardrails shell & system access ENFORCING
Data access controls databases & PII ENFORCING
Deployment approvals production releases ENFORCING
Payments team · custom team-defined rules DRY-RUN
Human approval gates sensitive actions REVIEW
For investors

Gartner already named the category: Guardian Agents.

Software that supervises other AI agents is projected to take 10 to 15 percent of the agentic AI market by 2030. AgentGuardian is built for that category: the enforcement layer between agents and the systems they can damage.

$52.6B
Projected AI agents market by 2030, up from $7.84B today.
150,000
AI agents Gartner expects per Fortune 500 enterprise by 2028.
40%+
Agentic AI projects Gartner predicts will be canceled by 2027, mostly over weak governance.

Sources: Markets & Markets, 2025 · Gartner on agent sprawl, 2026 · Gartner on guardian agents, 2025 · Gartner on project cancellations, 2025

Early access

Get runtime control of your AI agents

We're onboarding teams that run AI agents in production and need a policy layer they can show to compliance.

We reply personally within 48 hours.

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