# Azure AI Agents Persistent Java

> Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use when this capability is needed.

- Skill: `tomevault-io/azure-ai-agents-persistent-java` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/azure-ai-agents-persistent-java`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/azure-ai-agents-persistent-java/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/azure-ai-agents-persistent-java

---


# Azure AI Agents Persistent SDK for Java

Low-level SDK for creating and managing persistent AI agents with threads, messages, runs, and tools.

## Installation

```xml
<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-agents-persistent</artifactId>
    <version>1.0.0-beta.1</version>
</dependency>
```

## Environment Variables

```bash
PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
MODEL_DEPLOYMENT_NAME=gpt-4o-mini
```

## Authentication

```java
import com.azure.ai.agents.persistent.PersistentAgentsClient;
import com.azure.ai.agents.persistent.PersistentAgentsClientBuilder;
import com.azure.identity.DefaultAzureCredentialBuilder;

String endpoint = System.getenv("PROJECT_ENDPOINT");
PersistentAgentsClient client = new PersistentAgentsClientBuilder()
    .endpoint(endpoint)
    .credential(new DefaultAzureCredentialBuilder().build())
    .buildClient();
```

## Key Concepts

The Azure AI Agents Persistent SDK provides a low-level API for managing persistent agents that can be reused across sessions.

### Client Hierarchy

| Client | Purpose |
|--------|---------|
| `PersistentAgentsClient` | Sync client for agent operations |
| `PersistentAgentsAsyncClient` | Async client for agent operations |

## Core Workflow

### 1. Create Agent

```java
// Create agent with tools
PersistentAgent agent = client.createAgent(
    modelDeploymentName,
    "Math Tutor",
    "You are a personal math tutor."
);
```

### 2. Create Thread

```java
PersistentAgentThread thread = client.createThread();
```

### 3. Add Message

```java
client.createMessage(
    thread.getId(),
    MessageRole.USER,
    "I need help with equations."
);
```

### 4. Run Agent

```java
ThreadRun run = client.createRun(thread.getId(), agent.getId());

// Poll for completion
while (run.getStatus() == RunStatus.QUEUED || run.getStatus() == RunStatus.IN_PROGRESS) {
    Thread.sleep(500);
    run = client.getRun(thread.getId(), run.getId());
}
```

### 5. Get Response

```java
PagedIterable<PersistentThreadMessage> messages = client.listMessages(thread.getId());
for (PersistentThreadMessage message : messages) {
    System.out.println(message.getRole() + ": " + message.getContent());
}
```

### 6. Cleanup

```java
client.deleteThread(thread.getId());
client.deleteAgent(agent.getId());
```

## Best Practices

1. **Use DefaultAzureCredential** for production authentication
2. **Poll with appropriate delays** — 500ms recommended between status checks
3. **Clean up resources** — Delete threads and agents when done
4. **Handle all run statuses** — Check for RequiresAction, Failed, Cancelled
5. **Use async client** for better throughput in high-concurrency scenarios

## Error Handling

```java
import com.azure.core.exception.HttpResponseException;

try {
    PersistentAgent agent = client.createAgent(modelName, name, instructions);
} catch (HttpResponseException e) {
    System.err.println("Error: " + e.getResponse().getStatusCode() + " - " + e.getMessage());
}
```

## Reference Links

| Resource | URL |
|----------|-----|
| Maven Package | https://central.sonatype.com/artifact/com.azure/azure-ai-agents-persistent |
| GitHub Source | https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-agents-persistent |

## When to Use
This skill is applicable to execute the workflow or actions described in the overview.

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

```bash
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Azure Ai Agents Persistent Java"
```

### Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags azure-ai-agents-persistent-java ai-agents
```

### Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>
```

### Control Tower Integration

Register agents and tasks with the Control Tower (`execution/control_tower.py`) for centralized orchestration across machines and LLM providers.

### Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.

<!-- AGI-INTEGRATION-END -->

---
> Converted and distributed by [TomeVault](https://tomevault.io/claim/techwavedev) — claim your Tome and manage your conversions.
<!-- tomevault:4.0:skill_md:2026-04-13 -->