Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Triggers: "PersistentAgentsClient", "persistent agents java", "agent threads java", "agent runs java", "streaming agents java".
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安装前可以查看交给 AI 代理的指令内容。
Low-level SDK for creating and managing persistent AI agents with threads, messages, runs, and tools.
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-agents-persistent</artifactId>
<version>1.0.0-beta.1</version>
</dependency>
PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project> # Required for project configuration
MODEL_DEPLOYMENT_NAME=gpt-4o-mini # Required for agent model selection
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
import com.azure.ai.agents.persistent.PersistentAgentsClient;
import com.azure.ai.agents.persistent.PersistentAgentsClientBuilder;
import com.azure.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;
String endpoint = System.getenv("PROJECT_ENDPOINT");
TokenCredential credential = new DefaultAzureCredentialBuilder()
.requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
.build();
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();
PersistentAgentsClient client = new PersistentAgentsClientBuilder()
.endpoint(endpoint)
.credential(credential)
.buildClient();
The Azure AI Agents Persistent SDK provides a low-level API for managing persistent agents that can be reused across sessions.
| Client | Purpose |
|---|---|
PersistentAgentsClient | Sync client for agent operations |
PersistentAgentsAsyncClient | Async client for agent operations |
// Create agent with tools
PersistentAgent agent = client.createAgent(
modelDeploymentName,
"Math Tutor",
"You are a personal math tutor."
);
PersistentAgentThread thread = client.createThread();
client.createMessage(
thread.getId(),
MessageRole.USER,
"I need help with equations."
);
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());
}
PagedIterable<PersistentThreadMessage> messages = client.listMessages(thread.getId());
for (PersistentThreadMessage message : messages) {
System.out.println(message.getRole() + ": " + message.getContent());
}
client.deleteThread(thread.getId());
client.deleteAgent(agent.getId());
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());
}
| 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 |
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Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
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Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
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Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence. WHEN: 'Insufficient nvidia.com/gpu', GPU pod Pending, model-load OOM, DCGM/VRAM, KAITO Workspace not ready, or GPU autoscaling. DO NOT USE FOR: setup (airunway-aks-setup), non-GPU incidents (aks-troubleshooting), standalone VM quota (azure-quotas), or generic cost (cost-analysis or cost-optimization from the optional azure-cost plugin).
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Lookup documented AKS fixes only when the prompt includes an exact catalog signature and all of its qualifiers: VMCannotFitEphemeralOSDisk; NodePoolMcVersionIncompatible; 'NodeImageVersion is not accepted'; AKS SkuNotAvailable with size, location, and zone; ZonalAllocationFailed with insufficient zone capacity; OverconstrainedAllocationRequest with listed constraints; nested AKS vmssCSE/CSE VMExtensionError_OutboundConnFail, VMExtensionError_K8SAPIServerConnFail, or VMExtensionError_K8SAPIServerDNSLookupFail; or AllocationFailed with the full cataloged internal-error or insufficient-regional-capacity message. Never use for quota errors, code-only or bare wrappers, generic symptoms, incomplete signatures, or failures outside AKS; use aks-troubleshooting or azure-diagnostics.
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Collects bounded packet captures from AKS nodes and Azure network configuration for wire-level evidence. WHEN: "capture packets on an AKS node", "take a pcap", "run tcpdump on AKS", "prove where packets drop". Use for explicit packet-capture intent after read-only diagnostics, not general AKS connectivity or ingress troubleshooting.
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Debug live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation. WHEN: pod crashes or Pending, CrashLoopBackOff, OOMKilled, ImagePullBackOff, node NotReady, DNS or ingress failure, connectivity timeout, network policy, SNAT exhaustion, node-pool scaling blocked by QuotaExceeded or InsufficientVCPUQuota, upgrade stuck, spot or zone disruption, a bare VMExtensionProvisioningError or AllocationFailed wrapper, an uncataloged capacity symptom, or 'investigate my AKS cluster'. DO NOT USE FOR: packet capture (use aks-network-capture); GPU or model-serving issues (use aks-gpu-inference); cluster creation or provisioning (use azure-kubernetes); cost (use cost-analysis from the optional azure-cost plugin); pod rightsizing (use azure-kubernetes); a fully qualified documented AKS signature with every required nested qualifier (use aks-known-issues); standalone failures on non-AKS Azure resources (use azure-diagnostics). Unqualified errors and open-ended incidents stay here only for AKS.
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