Now in Private Early Access

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

AgentGuardian intercepts every tool call before it executes, enforces your policies at runtime, and automatically recovers from failures — without waiting for the next deploy.

agentguardian · arep runtime · live stream
12:04:01 [ObservationEvent] finance-agent read_file ✓ 84ms
12:04:02 [ObservationEvent] finance-agent read_file ✓ 91ms
12:04:03 [ObservationEvent] finance-agent read_file ✓ 78ms
12:04:05 [ObservationEvent] finance-agent read_file ↻ loop?
12:04:07 [FailureEvent] detector loop_detected risk=0.91
12:04:07 [IncidentEvent] incident-mgr P1 opened blast_radius=2
12:04:08 [RecoveryEvent] recovery-eng retry action=retry_with_backoff
12:04:35 [RecoveryEvent] recovery-eng resolved ↑ back to healthy
Topology Graph Live
finance-agent
read_file
write_file
exec_shell
deploy_agent
Works with your agent framework
LangChain AutoGen CrewAI Claude / Anthropic OpenAI Agents SDK Custom MCP Servers

Finding failures after the fact
doesn't protect your users.

By the time a failure surfaces, your users have already experienced it. AI agents need a control layer that acts before damage happens — not after.

🚫

No enforcement before execution

Every tool call your agent makes goes directly to execution — unchecked. There is no layer that evaluates intent, applies policy, or requires approval before a sensitive operation runs.

⚠️

Failures reach users before you do

Agents loop indefinitely, exceed error thresholds, or act against their stated intent with no automated response. By the time a human is paged, the damage is already in production.

🔓

Governance is an afterthought

Any agent can call any tool with any input. No policy layer. No approval workflow. No versioned audit trail. In a regulated industry, this isn't a monitoring gap — it's a compliance violation.

Intercept. Enforce. Recover.
All at runtime — before users notice.

1

Intercept

Route tool calls through the AgentGuardian gateway, or add 3 lines of SDK code using the AREP runtime. Every tool invocation is captured before it executes — no changes to your tools or prompts.

@arep_agent(agent_id="my-agent")
async def my_agent():
  # your existing code unchanged
2

Enforce

Before the tool executes, AREP evaluates your policies. ALLOW, DENY, or hold for human approval. Violations are blocked and emit a failure event — before any damage happens.

exec_shell  → DENY (denylist policy)
deploy_prod → APPROVAL (approval gate)
external_api → DENY (rate limit exceeded)
read_file   → ALLOW (passes policy)
3

Recover

When a failure occurs at runtime, the recovery engine acts immediately — no deploy needed, no pager required. Severity determines the action automatically.

P0 intent_mismatch → quarantine
P1 loop_detected  → retry
P2 error_rate     → reroute
P3 any           → escalate

The control plane your AI agents
are running without right now

🛡️

Policy Gateway

Every tool call is intercepted before it executes. ALLOW, DENY, or hold for human APPROVAL. Policies are versioned, auditable, and rollback-able in one API call. The tool never sees the request on a DENY.

AREP Gateway · Pre-execution

Automated Runtime Recovery

When a failure is detected at runtime, the recovery engine acts immediately — no deploy, no human required. Severity determines the action: retry, reroute, quarantine, or escalate to Slack/PagerDuty.

AREP Runtime · No deploy needed
🚨

Automated Incident Management

Related failures are correlated into P0–P3 incidents automatically. Full lifecycle: open → acknowledged → recovering → resolved. Causal chain links every incident to the exact tool call that triggered it.

P0–P3 severity scoring
🗺️

Live Topology Graph

A live map of every agent-to-tool relationship in your system. Health state, active incidents, and blocked tool calls update the moment they happen — no refresh, no polling delay.

Live · Zero delay
🧠

Intent Mismatch Detection

Catches the gap between what an agent said it would do and what it's actually doing — before the action completes. No predefined rules required. Detects a class of AI safety violations that rule-based systems cannot express.

AI safety · No external API
🔗

Full Causal Chain Audit

Every incident traces back through the exact failure event to the originating tool call — with event IDs, timestamps, and agent identity at every link. One API call returns the complete chain.

Compliance-ready audit trail

Policy-first AI agent control

Define what every agent is allowed to do. Enforce it at runtime. Audit everything.

  • Four policy types out of the box

    Allowlist, denylist, rate limit, and approval gate — configurable per tool, per agent, per team.

  • Policy versioning and rollback

    Every policy change is versioned. Roll back to any previous version with a single API call. No downtime.

  • Dry-run evaluation

    Test any policy against real tool calls before enforcing. No surprises in production.

  • Webhook escalation

    P0 and P1 incidents push structured JSON to Slack, PagerDuty, or any webhook — instantly.

Active Governance Policies
DENY Block exec_shell globally exec_shell
APPROVAL DB writes require approval db_write
RATE LIMIT External API — 10/min external_api
ALLOWLIST Deploy: CI agents only deploy_to_prod
DENY PII database access blocked read_pii_db
Causal Chain — INC-0042
Observation finance-agent → read_file (loop #5)
Failure loop_detected · risk=0.91
Incident P1 blast_radius=2 · 3 agents affected
Recovered retry_with_backoff → resolved in 28s

Infrastructure plays win early.
AI agents need a control plane.

150,000 agents per Fortune 500 enterprise by 2028. Each one executing tool calls with no policy layer between them and production systems.

$52.6B
AI agents market by 2030, growing at 46.3% CAGR from $7.84B today (Markets & Markets, 2025)
150,000+
AI agents per Fortune 500 enterprise by 2028 — up from fewer than 15 today (Gartner, 2026)
13%
of enterprises believe they have the right AI agent governance in place today (Gartner survey, 360 IT leaders)

"Every cloud service needs an API gateway. Every AI agent deployment needs AgentGuardian — the enforcement layer that sits between your agents and the tools they can damage."

Gartner predicts 40%+ of agentic AI projects will be canceled by 2027 due to weak governance and lack of runtime risk controls. AREP is the infrastructure layer that makes AI agents safe enough to stay in production.

Gartner has already named the category: "Guardian Agents" — tools that monitor, enforce policy on, and recover other AI agents — are projected to capture 10–15% of the entire agentic AI market by 2030. AgentGuardian is the platform for that category.

📌

Sources: Market size — Markets & Markets AI Agents Report 2025. Fortune 500 agent count & governance gap — Gartner: AI Agent Sprawl, April 2026. Guardian agents category — Gartner, June 2025. Project cancellation rate — Gartner, June 2025.

Get runtime control
of your AI agents

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