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Your AI Agents Are Running
Unsupervised. We Fix That.

When your AI agents fail, go rogue, or break policy — Agentic Leash tells you why, enforces your rules in real-time, and makes them self-correcting. Any agent. Any framework. Microsecond overhead.

Early access members get an extended free trial. No credit card. No BS.

agent.py

from agentic_leash import AgenticLeash

leash = AgenticLeash(api_key="sk-...")

@leash.protect()
def execute_agent_action(tool, params):
    # Your agent logic — now secured.
    return tool.run(params)

# That's it. Two lines. Your agent is leashed.

THE PROBLEM

Everyone's shipping agents.
Nobody's watching them.

You spent weeks building your AI agent. It works beautifully in dev. Then you deploy it to production and pray. Because right now, there's nothing between your agent and disaster. No policy enforcement. No runtime safety. No audit trail. Just vibes and hope.

The Agent That Dropped Production

Your agent decided DELETE FROM users was a reasonable query. You found out from a customer ticket.

The Agent That Leaked Everything

A prompt injection slipped through. Your agent dumped PII into a customer-facing chat. Compliance wants a word.

The Agent That Burned $40K Overnight

No rate limits. No spending policies. No alerts. Just a very expensive lesson in unsupervised autonomy.

The Agent That Hallucinated a Refund Policy

Your agent confidently promised customers a policy that doesn't exist. Now legal is involved.

These aren't hypotheticals. They're Tuesday.

THE SOLUTION

Deterministic policy enforcement for AI agents.
Not another prompt wrapper. Actual infrastructure.

Agentic Leash sits between your agents and their tools. Every action is validated against your policies in microseconds — before it executes. Not after. Not sometimes. Every. Single. Time.

Enforce

Define business policies as code. CEL-powered rules evaluate in microseconds. Block destructive actions before they execute.

Observe

Every agent action logged. Every decision auditable. Real-time visibility into what your agents are actually doing in production.

Improve

Structured denial feedback teaches agents to self-correct. Your agents get smarter with every blocked action. Not just guardrails — a feedback loop.

POLICY ENGINE

Your Rules. Enforced in Microseconds.
Not by another LLM. By math.

Write business policies in plain YAML. Under the hood, they compile to CEL (Common Expression Language) — the same tech Google uses for IAM. Deterministic. Type-safe. Evaluates in microseconds, not seconds.

⚡ < 1ms evaluation📐 Deterministic🔧 YAML config

policies/production.yaml

# policies/production.yaml
version: "1.0"
policies:
  - name: "Block Destructive DB Operations"
    severity: critical
    rules:
      - id: no_prod_deletes
        cel_expression: |
          !(action.params.query.contains("DROP"))
        decision: DENY

AI Agent makes call

Agentic Leash evaluates policy

✓ ALLOWED

Execute safely

✗ BLOCKED

Agent self-heals

response.json

{
  "allowed": false,
  "risk_level": "critical",
  "matched_rules": ["no_prod_deletes"],
  "suggestion": "Use archive instead of delete"
}

RUNTIME ENFORCEMENT

Intercept Before Impact.
Not a Logger. A Bodyguard.

Every tool call, every API request, every database query your agent makes passes through the enforcement layer before execution. If it violates policy, the action is blocked and the agent receives structured feedback explaining exactly why — and what to do instead.

🛡️ Fail-open by design — production uptime is sacred

INTEGRATION

Two Lines of Code. That's the Integration.

Python. Node.js. REST API for everything else. Works with LangChain, CrewAI, OpenAI SDK, Claude, AutoGen — whatever you're building with.

app.py

from agentic_leash import AgenticLeash

leash = AgenticLeash(api_key="sk-...")

@leash.protect()
def agent_execute(tool_name, params):
    return tool.run(tool_name, params)

app.ts

import { AgenticLeash } from '@agentic-leash/sdk'

const leash = new AgenticLeash({ apiKey: 'sk-...' })

const safeExecute = leash.protect(myAgentFunction)

Works with: LangChain · CrewAI · OpenAI · Claude · AutoGen · Any HTTP agent

VISIBILITY

“What Did the Agent Do?” Finally Has an Answer.

Every action. Every decision. Every policy match. Logged automatically. Append-only. Immutable. When compliance asks what happened, you open the dashboard.

Full traceability — timestamp, agent ID, session
Policy decisions with matched rules
Immutable audit trail
Real-time event dashboard

audit.json

{
  "timestamp": "2026-03-08T14:23:45Z",
  "agent_id": "agent_prod_01",
  "action": "execute_query",
  "decision": {
    "allowed": false,
    "risk_level": "critical",
    "matched_rules": ["no_prod_deletes"]
  },
  "latency_ms": 12
}

WHY NOW

2026 Is the Year Agents Hit Production.
Who's Watching Them?

Every company is building AI agents. The models are good enough now. Agents are shipping. But here's what nobody's talking about:

→Who enforces business policies on autonomous agents?

→Who catches the hallucination before it becomes a legal liability?

→Who provides the audit trail when compliance comes knocking?

→Who patches the failure loop so it doesn't happen again?

Agentic Leash is the infrastructure layer that should already exist. We're not adding more AI to watch your AI. We're adding deterministic, microsecond policy enforcement with a full audit trail and self-healing feedback loops.

Monitor·Enforce·Improve·Prove it works

We're Building This Right Now.
Get In Before Everyone Else Does.

Agentic Leash is in active development. We're opening access to a small group of early design partners who want to shape the future of agent security infrastructure.

No credit card required

Extended free trial for waitlist members

Your feedback shapes the product

First access when we launch

We're onboarding design partners in batches. Early waitlist = early access.

Your AI Agents Are Running Unsupervised. We Fix That.

Everyone's shipping agents.Nobody's watching them.