Agentic AI for HPE Storage: Autonomous Ops & Compliance in Rust with Rig

[anandtk]

Introduction:

Agentic AI systems, which autonomously plan, reason, and act, significantly benefit from solid software engineering and design patterns to remain maintainable, robust, and controllable. Rust, with its emphasis on correctness, ownership, and async concurrency, provides a strong foundation for building such intelligent agents. When paired with an LLM client abstraction like Rig (latest framework version 0.16.0, announced July 30, 2025, on the Reddit r/rust forum), which encapsulates prompt shaping, agent instantiation, and model calls, users can build modular, composable, and safe agent pipelines capable of efficiently interacting with and automating tasks on critical enterprise infrastructure, including HPE Storage systems.

Core architectural patterns for agentic AI, mapped into idiomatic Rust and exemplified with a rig-style LLM client. It includes:

•   Agent abstraction and orchestration
•   Planning / execution separation
•   Chain of responsibility & middleware
•      Retry, circuit breaker, and fallback
•   Memory & context management
•   Tool integration
•   Observability & telemetry
•   Safety, sandboxing, and guardrails

Use Case: 

Autonomous Storage Operations & Compliance Agent for HPE Storage

Context:

HPE Storage platforms like Alletra, Primera, Nimble, 3PAR, and HPE GreenLake for Block/Object Storage do generate rich telemetry via HPE InfoSight, REST APIs, and CLI tools.
They also expose programmatic control for:

• Volume creation & deletion

• Replication setup/failover

• Data placement & tiering

• Snapshot/backup policy management

• Encryption & compliance settings

However, reacting to alerts, performing remediation, and ensuring ongoing compliance is still human-intensive.

Goal

Implement an autonomous agent that:

1. Monitors HPE Storage telemetry in real-time.

2. Plans optimized corrective or preventive actions.

3. Executes actions using HPE Storage APIs.

4. Verifies success and compliance.

5. Generates synthetic test scenarios to stress-test detection logic.

Agent Roles (Agentic AI Pattern)

Observation Collector Agent

• Pulls metrics from HPE InfoSight API or local array REST endpoints.

• Examples:

  • Volume latency (read_latency, write_latency)

  • Disk health (wear_level, reallocated_sectors)

  • Replication lag

  • Encryption state (at_rest_encryption_enabled)

  • Policy violations (snapshot age beyond retention)

Planner Agent (GPT via Rig in Rust)

• Takes a summarized telemetry snapshot and compliance policies as context.

• Produces a structured plan:

[
{
"action": "replicate_volume",
"target": "volume-123",
"reason": "Primary node showing high error rate"
},
{
"action": "enable_encryption",
"target": "volume-456",
"reason": "Policy requires encryption-at-rest"
}
]

Executor Agent

• Uses Rust Tool Traits to map each action to an HPE Storage API call.

• Examples:

  • POST /api/volumes/{id}/replicate

  • PATCH /api/volumes/{id} to enable encryption

  • POST /api/data_movement/rebalance

Verifier Agent

• Re-checks metrics and configuration post-action:

  • Confirm replication complete & latency reduced.

  • Confirm encryption flag set.

  • Confirm no policy violations remain.

Synthetic Generator Agent

• Uses an NLI-like augmentation prompt to create synthetic storage failure patterns:

  “Given a normal latency graph, generate a plausible scenario of a sudden I/O storm followed by a node failover.

”Contrastive cases: healthy vs unhealthy node telemetry.

• Used for offline testing and AI model retraining.

Architecture

Rust + Rig Implementation Algorithm:

use rig::providers::openai::Client;
use serde::{Serialize, Deserialize};
use std::env;

#[derive(Debug, Serialize, Deserialize)]
pub enum StorageAction {
ReplicateVolume { volume_id: String },
EnableEncryption { volume_id: String },
Rebalance { pool_id: String },
}

#[tokio::main]
async fn main() -> anyhow::Result<()> {
let api_key = env::var("OPENAI_API_KEY")?;
let rig_client = Client::new(&api_key);

// Planner Agent
let planner = rig_client.agent("gpt-4")
.preamble("
You are an HPE Storage Planner Agent.
Given telemetry, produce JSON actions to fix anomalies and ensure compliance.
")
.build();

// Example telemetry input
let telemetry = r#"
Volume 123: latency high, replication lag
Volume 456: encryption disabled
"#;

let plan = planner.prompt(telemetry).await?;
println!("Planner Output: {}", plan);

// Executor - Here you'd map StorageAction -> HPE API calls
// Example: call HPE Alletra API to enable encryption or replicate volume

Ok(())
}

Benefits for HPE Storage:

• Proactive Ops: Uses HPE InfoSight telemetry to anticipate issues.

• Self-Healing: Reduces downtime and manual interventions.

• Compliance at Scale: Continuous automated enforcement.

• Safe Execution: Verifier ensures changes succeeded.

• Test Before Production: Synthetic scenarios stress-test planning logic.

Summary

The article implements an Autonomous Operations & Compliance Agent for HPE Storage systems (e.g., HPE Alletra, Primera, Nimble, 3PAR) using Agentic AI principles, powered by Rust for performance and safety, and Rig(version 0.16.0) for structured LLM (GPT) orchestration.

The system is composed of specialized AI-driven agents:

• Observation Collector – Gathers real-time telemetry from HPE InfoSight and storage APIs.

• Planner Agent (GPT via Rig) – Analyzes telemetry, detects anomalies, and produces actionable remediation plans.

• Executor Agent – Safely executes API calls to provision, replicate, rebalance, or enforce compliance policies.

• Verifier Agent – Confirms successful action completion and validates compliance post-change.

• Synthetic Generator Agent – Creates realistic test scenarios to stress-test planning and verification logic before deployment.

Key Benefits for HPE Storage Environments:

• Proactive Issue Resolution – Detect and fix performance or health issues before they impact workloads.

• Continuous Compliance – Enforce encryption, retention, and replication policies automatically.

• Self-Healing Infrastructure – Minimize manual interventions and human error.

• Safe AI-Driven Automation – Verified changes prevent disruption.

• Tested & Reliable – Synthetic workload scenarios ensure robustness.

[pearlwilson]

Well-crafted article into applying Agentic AI with Rust and Rig for autonomous, compliant HPE Storage operations. The modular design and real-world use cases make it both practical and forward-looking. 

[yashwanth_k]

Wow this is a slick setup for HPE Storage! Using Rust and Rig to build those autonomous agents is super smart and love how it handles everything from telemetry to compliance without needing constant human babysitting. That synthetic testing part is a nice touch. Great stuff Anand!