AAGT: Autonomous Agent Governance & Transport

The High-Performance Infrastructure for Resilient AI Agents.

AAGT is a next-generation application framework designed for building autonomous, durable, and secure agent systems. Built on Rust, it bridges the gap between high-speed asynchronous execution and complex stateful reasoning. By integrating Wasm-based skill sandboxing, a persistent cognitive state machine, and a unified message bus, AAGT provides the industrial-grade "Governance and Transport" layer required for production AI deployments.

Core Pillars

AAGT is built on a modular architecture designed for extreme reliability and real-time orchestration.

1. High-Performance Execution (Rust Core)

Rust's type-safety and ownership model provide the foundation for execution and safety.

• Zero-Latency: Async-first design (Tokio) with microsecond overhead.
• Parallel Tool Calling: Concurrent execution of multiple tools with configurable limits.
• Risk Management: Pluggable RiskManager with atomic quota reservation and trade validation.

2. Isolated Capability Runtimes

Extend agent capabilities without compromising system stability.

• Wasm Runtime: Sandboxed, high-performance skills written in any Wasm-compatible language (Rust, Go, etc.) with per-skill security policies.
• Self-Evolving Skills: Built-in ForgeSkill capability allowing agents to generate, compile (Wasm), and dynamically register new tools at runtime.
• Python Sidecar: gRPC-linked ipykernel for heavy data analysis, LangChain integration, and persistent Jupyter-like logic.

3. Persistent State Machine

Agents that survive restarts and handle long-running reasoning loops.

• SQLite Serialization: Full cognitive state (dialogue history, reasoning steps, tool status) persisted to disk.
• Suspend & Resume: Native support for human-in-the-loop (HITL) workflows where agents pause for approval and resume seamlessly.

4. Tiered Memory System (aagt-qmd)

Content-addressable hybrid search engine for deep historical reasoning.

• Tiered Search: Seamlessly bridges Short-Term (Hot) and Long-Term (Cold/Vector) tiers.
• Semantic Context: Automatic retrieval of relevant documents via BM25 and Vector Search re-ranking.
• Active Indexing: Real-time background indexing of files and conversation history.

5. Decentralized Swarming (aagt-swarm)

Enable cross-process and internet-scale agent collaboration.

• libp2p Transport: Secure communication via Noise/TLS with Kademlia DHT discovery.
• Gossip Orchestration: Peer-to-peer task bidding and announcement protocols.
• NAT Traversal: Automatic hole-punching for seamless connectivity across different networks.

Security & Reliability

• Policy-Driven Execution: Configurable policies (Auto, RequiresApproval, Disabled) for every tool and skill.
• Binary Sandbox: Strict syscall and capability filters for binary skills.
• Resource Monitoring: Built-in tracking of RAM, Disk, and Token usage.

Multi-Provider Support

Native support for LLMs via a unified Provider trait:

• OpenAI, Anthropic (Claude), Gemini, groq, DeepSeek, Ollama.

Quick Start (v0.3.0)

use aagt_core::prelude::*;
use aagt_providers::openai::OpenAI;

#[tokio::main]
async fn main() -> Result<()> {
    // 1. Initialize Components
    let provider = OpenAI::from_env()?;

    // 2. Build Agent with Persistence & session management
    let agent = Agent::builder(provider)
        .model("gpt-4o")
        .session_id("agent-session-001")
        .with_memory_path("data/storage.db") // Persistent SQLite + Vector tier
        .build()?;

    // 3. Start Execution
    let response = agent.prompt("Analyze the provided dataset and suggest an optimized workflow.").await?;
    println!("Agent response: {}", response);

    Ok(())
}

Architecture Detail

Support & License

• License: MIT / Apache 2.0
• If you find this collection valuable and appreciate the effort involved in obtaining and sharing these insights, please consider supporting the project.

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