fix(deps): update dependency com.alibaba.cloud.ai:spring-ai-alibaba-bom to v1.1.2.2 - autoclosed

[renovate]

This PR contains the following updates:

Package	Change	Age	Confidence
com.alibaba.cloud.ai:spring-ai-alibaba-bom	1.1.2.1 → 1.1.2.2

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Release Notes

alibaba/spring-ai-alibaba (com.alibaba.cloud.ai:spring-ai-alibaba-bom)

v1.1.2.2

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AgentScope Integration

AgentScope Java is an agent-oriented programming framework for building LLM-powered applications.

• AgentScope – AgentScopeAgent wraps AgentScope ReActAgent as a BaseAgent for use in graph workflows.

# Add the following dependency to ochestrate AgentScope using SAA Graph
<dependency>
  <groupId>com.alibaba.cloud.ai</groupId>
  <artifactId>spring-ai-alibaba-starter-agentscope</artifactId>
  <version>1.1.2.2</version>
</dependency>

Multiagent Patterns

• Subagent – Main orchestrator delegates tasks to specialized sub-agents (codebase explorer, web researcher, etc.) via Task/TaskOutput tools; supports both Markdown and API-defined sub-agents.
• Supervisor – Central supervisor agent wraps calendar and email agents as tools (AgentTool), invokes them on demand, and synthesizes results.
• Skills – Single agent uses read_skill to load skill content on demand; system prompt shows only skill descriptions for progressive disclosure and smaller context.
• Routing
  • Routing (simple) – LlmRoutingAgent classifies the user query, invokes specialist agents (GitHub, Notion, Slack) in parallel, then synthesizes a single answer.
  • Routing (graph) – LlmRoutingAgent as a StateGraph node with preprocess/postprocess and an internal merge node for routing and result synthesis.
• Handoffs
  • Handoffs (single-agent) – One ReactAgent advances steps via state (e.g. current_step); a ModelInterceptor injects step-specific system prompt and tools per turn.
  • Handoffs (multi-agent) – Sales and support agents as graph nodes; handoff tools update active_agent and conditional edges route between agents.
• Workflow – Custom workflow examples: RAG (rewrite → retrieve → prepare → agent) and SQL agent (list_tables → get_schema → run_query) as graph-based flows.

Multimodal & Voice Agent

• Voice Agent – Sandwich architecture (STT → ReactAgent → TTS): WebSocket-based real-time voice with DashScope ASR and CosyVoice TTS, plus text input; agent uses sandwich-order tools and streams events (stt_chunk, agent_chunk, tts_chunk).
• Multimodal – Vision (image in/out) and TTS: DashScope vision models for image understanding and ReactAgent with media input; image generation via tools (Wanx), TTS via DashScopeAudioSpeechModel; ToolMultimodalResult for structured tool responses (url/base64).

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Configuration

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Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
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0.0% Coverage on New Code
0.0% Duplication on New Code

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 58.32%. Comparing base (7bb7496) to head (5f77a37).
⚠️ Report is 12 commits behind head on master.

Additional details and impacted files

@@            Coverage Diff            @@
##             master    #5839   +/-   ##
=========================================
  Coverage     58.32%   58.32%           
  Complexity     1144     1144           
=========================================
  Files           270      270           
  Lines          5358     5358           
  Branches        339      339           
=========================================
  Hits           3125     3125           
  Misses         2057     2057           
  Partials        176      176           

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