召喚·Shōkan · Summoning AI Agents

AI Agents Act Fast.
Your Policies Must Too.

Shokan is a real-time intent orchestration control plane that evaluates every AI agent action against your policies — before execution. Deterministic. Least-privilege. Auditable.

Enterprise IAM·Claude Code · Codex · Cursor·Zero Trust Access·Audit & Containment
shokan debug --trace agent-run-4f2a
 
✗ BLOCKED support-bot → databricks.query
  (schema:sales.pipeline)
 
  Why: intent=customer_support
       sales.pipeline outside approved scope
 
  Fix: databricks.query on schema:sales.tickets
       Scope: this session · revoked on completion
 
shokan apply --fix
✓ Applied. Agent unblocked.
The Problem

AI Agents Can Be Authenticated.
Their Access Cannot Be Safely Scoped.

Traditional IAM verifies WHO the agent is — but not WHY it is acting. It cannot evaluate the intent of an action or the contextual risk of execution.

Today's Reality:Enterprises re-use human IAM processes for agents — manually provisioning broad permissions that accumulate over time, or delegating agent identity with excessive rights. Both strategies are manual, fragile, and create critical security debt at scale.
The Solution

A Control Plane for Enterprise Agent Access

Shokan translates agent intent into enforceable, fine-grained access policies — at runtime, before any action executes, across every tool and system your agents touch.

1

Debug

Run the debugger against any failed agent call. Start from existing traces and logs — no new infrastructure required.

2

Fix

Get a precise grant recommendation scoped to the exact task, session, and agent. Apply it in one command.

3

Expand

From debugging into monitoring, policy enforcement, and full governance — when your team is ready.

Execution Velocity

Agents act faster than any human can review.

Agents execute at machine speed. Post-action monitoring is too late, and manual approvals quickly become rubber stamps.

🕸️

Workflow Complexity

Multi-step agents across diverse tools are impossible to predict — or audit.

Multi-step reasoning over diverse tools, data, and sub-agents makes agent actions difficult to predict, track, or control.

🔑

Over-Permissioned Agents

Your agents inherit everything you can do — even when they only need a fraction of it.

Human permissions reflect the union of all tasks and roles. Agents executing a single task inherit the entire footprint — including overscoped credentials with no way to distinguish agent identity from human identity.

🆔

Agent Identity

When something goes wrong, you need to know which agent did it — not just that 'an agent' did.

Issues verified machine identities to every agent and sub-agent, establishing a cryptographically attested trust chain from user delegation to execution context.

🔍

Discovery & Mapping

You can't secure what you can't see — map every resource your agents can reach before they reach it.

Automatically identifies agent workflows, tools, credentials, and all accessible resources — creating a comprehensive operational map.

📡

MCP Observability

Your agent made dozens of MCP calls in production — do you know what any of them were?

Captures full telemetry across every Model Context Protocol call — which tools agents invoke, with what arguments, and against which resources.

🛡️

Pre-Execution Evaluation

By the time you see the error, the damage is done — stop unsafe actions before they execute.

Evaluates every tool action against established policies before execution. Sensitive or destructive operations trigger step-up verification — no agent bypasses elevated-risk action gates without explicit clearance.

📋

Charter Enforcement

What your agent is allowed to do should be enforced at runtime, not documented in a README.

Binds each agent to a machine-readable charter defining its purpose, resource scope, and risk envelope — then enforces that charter at every access decision, before any tool call executes.

🌐

Transport-Agnostic Coverage

You secured your REST API but forgot your agent also uses a CLI — enforcement gaps are how things go wrong.

Enforces policy uniformly across REST APIs, CLIs, MCP servers, and direct SDK calls — no enforcement gaps regardless of how agents communicate.

🔒

Audit & Containment

When something goes wrong, 'the agent did it' isn't a postmortem — you need the full trace.

Generates immutable audit evidence for all agent actions and provides instant kill-switch capabilities for immediate threat containment.

🔌

No-Code Adoption

You shouldn't need to become a security engineer just to ship safe agents.

Integrate via a standard SDK hook or sidecar — no custom policy authoring, no YAML schemas, and no security engineering prerequisites to get started.

🔐

Least-Privilege Orchestration

A long-lived API key sitting in your agent's memory is a breach waiting to happen.

Issues per-tool-call credentials scoped to task, agent, user, and environment — held in memory only, never written to disk, fully reaped after each session.

🔗

Delegation Chaining

Sub-agents inheriting your orchestrator's full permissions is how blast radius explodes.

Sub-agents cannot escalate beyond the privilege scope granted by their parent. Every delegation is bounded, auditable, and enforced at the control plane.

⚙️

Continuous Adjustment

Permissions set at deploy time drift — your agent's access should adapt as your posture changes.

Leverages real-time signals from CAEP/RISC/SSF and other intelligence sources to continuously adapt and fine-tune agent permissions.

🦾

Framework Compatibility

Already running OpenClaw or NemoClaw? Shokan adds the governance layer they don't provide.

Wraps OpenClaw and NemoClaw deployments with charter enforcement, scoped credentials, and immutable audit — giving teams a governed path to adopt these frameworks without inheriting ambient OS-level or network exposure.

Early Access

Ready to Summon Control Over Your AI Agents?

Join enterprise security and platform leaders who are shaping the future of agentic AI governance.

Talk to Founders

15 minutes. No pitch deck. Just a real conversation.