Traditional Controls — IAM, DLP, AppSec
Governs identity, access, applications, and data.
These tools were not designed to make contextual policy decisions about autonomous agent actions at runtime.
Runtime policy enforcement
The Runtime
Policy Layer
for AI Agents
Define once, and enforce security, compliance, and token policies in real time across every tool, data source, and system your agents use.
- Security & Compliance
- Token Control
- Your Custom Rules
Your policy remains portable across clouds, models, and frameworks.
Detailed agent insights
Understand Each Agent in Context
Get a complete view of every agent’s purpose, identity, permissions, and activity. See how it’s configured, what it can access, and how it behaves — so you can enforce the right policies with confidence.
- Mission and purpose
- Identity and application context
- Trigger source and actor type
- Tags, activity, and operating state
Why existing tools fall short
Action Control — Skyrelis
Governs what an agent is allowed to do.
Based on who it is acting for, which tool it is using, what data is involved, and which policy applies.
At runtime. Before the action executes.
How It Works
Every Agent Action.
Evaluated in Real Time.
Skyrelis acts as a transparent proxy between your agents and all downstream resources. No SDK, no agent code changes — just a routing configuration that puts Skyrelis inline on day one.
Skyrelis Runtime Proxy — Inline Policy Enforcement
Inline by Design
Every tool call routes through Skyrelis before it reaches any target. Allow, block, redact, or escalate — in milliseconds. No latency impact on the happy path.
Hyper-Specific Policy
Not "is this prompt risky?" — but "should this agent take this action, under this customer's policy, right now?" Define rules in plain English. They live in Skyrelis, not your codebase.
Zero Code Changes
No SDK. No agent rewrites. Hot-swap LLMs or guardrails anytime. Works with LangChain, CrewAI, AutoGen, and any custom agent build — framework agnostic by design.
Architecture
Built for Cloud-Based
AI Agents
Cloud-based AI agents do more than exchange prompts. They access data, call tools, invoke APIs, and take actions across enterprise systems. Skyrelis sits directly in the agent's decision path — controls managed outside the application, deployable in hours.
Skyrelis — Inline Decision Layer
Inline enforcement on the agent decision path
Controls prompts, tool calls, APIs, data access, and outputs
Centralized policy managed outside application code
Framework and cloud agnostic
Deploy new policies in hours, not weeks
Built for production cloud agents at scale
Prompt-Only & SDK-Only Approaches
Prompt protection without action-level governance
Security logic distributed across applications
Developers own security implementation
Policy updates require engineering effort
Inconsistent enforcement across agent fleet
Slower rollout of enterprise security policies
Policy Engine
Plain English Policy.
Enforced at Machine Speed.
Define what every agent is and isn't allowed to do. Policies live in Skyrelis — decoupled from your application code. Change them without a redeployment.
Hyper-Specific Scope
Policy can be scoped per agent identity, per customer, per tool, per action type, per data sensitivity label, or any combination. One policy rule doesn't have to cover everything.
Hot Deploy — No Redeployment
Publish a policy change and it's live within seconds. Security team updates a rule — agent behavior changes instantly, across every environment, without touching the application.
Modular & Composable
Start with a base policy template and override specific rules per agent or customer. Inheritance means you don't repeat yourself across a fleet of similar agents.
Plain English Rules
Define policies in natural language. No custom DSL, no regex, no developer required. "Block this agent from writing to S3 buckets in the us-east-1 region" is a valid policy.
Behavioral Analysis
Know When Your Agent
Is Acting Strangely.
Skyrelis builds a behavioral baseline for each agent and uses ML-driven risk scoring to surface anomalies — before they become incidents. Not just "what happened," but "does this pattern look right?"
Baseline & Drift Detection
Skyrelis learns what normal looks like for each agent — tool usage patterns, call frequency, data volumes. Deviations from baseline trigger risk scoring updates automatically.
ML-Driven Risk Scoring
Each agent and tool combination carries a risk score built from dozens of signals: data source trust, action type, historical patterns, customer context, and policy exposure.
Tool & Identity Inventory
Full catalog of every tool an agent has accessed — with risk scoring per tool. Identify agents with tools they shouldn't have, or tools that haven't been used but are still exposed.
Tiered Enforcement
Low-risk actions proceed automatically. Medium-risk triggers enhanced logging. High-risk requires human approval or is blocked outright. Tunable per agent and customer context.
Integrations
Works With What
You're Already Using.
Skyrelis is framework agnostic and cloud agnostic. Point your agents at Skyrelis instead of their tools directly — no code changes, no wrappers, no SDK.
🦜
LangChain
Agent Framework
Proxy
🤝
CrewAI
Agent Framework
Proxy
⚙️
AutoGen
Agent Framework
Proxy
🔧
Custom Agents
Any HTTP-based agent
Proxy
🧠
OpenAI / GPT-4o
LLM Provider
Native
🤖
Anthropic Claude
LLM Provider
Native
☁
AWS / Azure / GCP
Cloud Provider
Native
📊
Salesforce, GitHub, S3
SaaS & Data Tools
Proxy
"
As we bring our CRM agent to more enterprise customers, each customer has different requirements for what information can be shown, redacted, or blocked, and what actions the agent can take. Skyrelis helps us support those differences without custom building or configuration sprawl for every customer, so we can ship features faster and expand our use cases while keeping our engineering team focused on improving the core product.
Daniel Castro
CEO, Persona X
Product Demos
Short, Focused Walkthroughs.
Pick One and Watch.
See the Skyrelis runtime policy layer in action — no slides, no sales deck. Unlock and watch any demo below.
Product Demo
▶
Agent Visibility & Tool Access
See how Skyrelis discovers every AI agent, profiles tool usage, and surfaces risk in real time — from first connection to full inventory.
- Automatic agent discovery on connection
- Tool catalog with ML-driven risk scores
- Real-time activity map per agent
- Behavioral baseline and drift alerts
Product Demo
▶
Customer-Specific Guardrails
Walk through how Skyrelis layers company baselines and customer-specific guardrail profiles at runtime — with no product forks and no code changes per deal.
- Base policy template + customer overrides
- Per-customer approval thresholds
- Hot policy deploy — live in seconds
- Audit trail scoped per customer
Want a Tailored Walkthrough?
We'll run Skyrelis on your agent or a live workflow we provide — policy enforced in production before the 30 minutes are up.
Start Governing
Your Agent Fleet
Policy running in production in 30 minutes. Or start with our free tools to see where your gaps are today.
Agent Security Scorecard
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Agentic Security Playbook
10 chapters on governing AI agents in production. Used by 200+ security teams.
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