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symbiont

Run secure, policy-driven AI agent orchestration with typestate-enforced ORGA reasoning and Cedar authorization.

53|10|Updated Jul 10, 2025
Search Tags:#security#knowledge#webhook#policy#sandbox#symbiont
One-click install
npx skills add https://github.com/ThirdKeyAI/Symbiont --skill symbiont
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: symbiont
Source: https://github.com/ThirdKeyAI/Symbiont/tree/main
Command: npx skills add https://github.com/ThirdKeyAI/Symbiont --skill symbiont

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Symbiont provides a secure, policy-driven runtime to build autonomous AI agents with typestate-enforced ORGA reasoning, Cedar policy authorization, knowledge bridge, zero-trust security, multi-tier sandboxing, webhook verification, markdown memory, skill scanning, metrics, scheduling, and a declarative DSL

Core Features & Use Cases

  • Typestate-enforced Observe-Reason-Gate-Act ORGA loop with compile-time safety.
  • Cedar policy engine for runtime authorization and policy-as-code.
  • Knowledge bridge with vector-based memory for contextual reasoning and learning.
  • Multi-tier sandboxing (Docker, gVisor, Firecracker) for safe agent execution.
  • Webhook verification, AgentPin/SchemaPin cryptography, and Markdown memory persistence.
  • Scheduling, metrics, and agent delivery/routing capabilities across channels.

Quick Start

Define your Symbiont agent in the DSL, then parse the DSL and launch the runtime to execute the agent.

Frequently Asked Questions about symbiont

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I enforce security policies on autonomous AI agents during runtime?▼

Multi-tier sandboxing secures autonomous AI agent execution by isolating operations in Docker, gVisor, or Firecracker environments. This containment prevents unauthorized system access during the observe-reason-gate-act reasoning loop.

How do I verify webhooks and establish cryptographic identity for AI agents?▼

To define and launch a policy-driven AI agent, you define its behavior in a declarative DSL and then parse the DSL to launch the runtime. This initiates the typestate-enforced observe-reason-gate-act loop for secure execution.

Does this AI agent runtime support vector-based memory for contextual reasoning?▼

Typestate-enforced reasoning guarantees compile-time safety for AI agent orchestration by structuring the observe-reason-gate-act loop. This prevents invalid state transitions in the agent runtime before execution occurs.

What's the best way to schedule autonomous agents across diverse execution channels?▼

You should not use basic AI agent orchestration without multi-tier sandboxing when handling untrusted operations. Without Cedar policy authorization and cryptographic identity, autonomous agents risk unauthorized system access.

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