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Chapter 8 : AI Agents Advanced

Models - LLMs​

Models or specifically Large Language Models (LLMs) power AI capabilities. Its recommended to use Hybrid Model Workflow meaning using Multi-LLM Workflows.

How to Choose Models​

For Building Agents​

As per the budget - high to medium,

  • Prefer premium, heavyweight reasoning models like Claude Opus
  • Alternative consider using Claude Sonnet 4.6 or similar, which are Cost-efficient, rapid production workhorse.
  • Last resort, Use models like Claude Haiku 4.5 or similar GPT-5 Mini, which are cost-efficient workhorses.
caution

Lower end models will not give good response for tasks like:

  • From table schema
    • Identify Sensitive Data Columns, create view scripts.
    • Ask AI What Insights and Trends Can Get?
    • Ask AI What AI Workflows Can Implement
    • Get Proactive Insights Workflows

For Using Agents and Chat​

Example Use Case: AI Data Insights​

For an agentic solution running real-time data insights, your models must have exceptional JSON/structured output capabilities, low latency, and a low cost-per-million-tokens to accommodate the high volume of text processed in chained workflows.

Example Work Load​

Solution based on Spring AI agentic platform is used by multiple users, where 2 to 3 users may execute simultaneous workflows containing 4 to 5 chained AI tasks.

One of the recommendation is to use OpenRouter Paid Tier. The most cost-efficient, highly reliable paid models on OpenRouter that fit this exact profile are given below.

Best for Complex Statistical Reasoning: DeepSeek R1​
  • Model: deepseek/deepseek-r1
  • It is more expensive than DeepSeek V3
Best Overall for Data Insights: DeepSeek V3​
  • Model: deepseek/deepseek-chat
Other​
  • The models which are low-cost may not be much useful.
  • Models: Google Gemini 2.5 Flash
Tasks:Data Cleaning & Structuring→ Use DeepSeek V3
Tasks:Insight Analysis & Aggregation→ Use DeepSeek R1 or V3
(Requires strong logic and strict JSON generation)(Good intelligence at low cost)

How to Use OpenRouter​

To use OpenRouter Models,

  • Set its API Key as OPENAI_API_KEY.
  • Add below properties to agent\spring-java\agentserver\src\main\resources\application.properties.
  • Use suffix :free for model free tier usage.
spring.ai.openai.base-url=https://openrouter.ai/api

# Optionally Specify model to use in the format, e.g.
#
#spring.ai.openai.chat.options.model=deepseek/deepseek-chat
#spring.ai.openai.chat.options.model=deepseek/deepseek-r1
#spring.ai.openai.chat.options.model=anthropic/claude-haiku-4.5
# 
#spring.ai.openai.chat.options.model=google/gemini-2.5-flash
#spring.ai.openai.chat.options.model=qwen/qwen3-coder:free
#spring.ai.openai.chat.options.model=google/gemma-4-31b-it
#spring.ai.openai.chat.options.model=google/gemma-4-26b-a4b-it

How to Optimize Model Usage Costs​

Token Calculation​

Total Tokens = Prompt + Reasoning + Completion

Optimize Tokens​

Data Context

  • Give optimized data for LLMs to perform Best.
  • Use Data Modeling and Pushdown features.
  • Breakdown AI Tasks to perform data preparation by low cost models.
  • And give aggregated final context to reasoning models to process final output.

In general

  • Avoid giving very large context in Prompts.
  • Limit agents to small MCP Tool groups (e.g., under 30 tools), which saves massive input token costs.
  • Use RAG with Relevance search.

Event-Driven AI Workflows​

  • Use pre-built Kafka based events Pre-configured for your Data
  • Use Configuration For Backend:
    • Kafka Events Producer, Consumer : Include ✅

Invoke Workflows​

→ From Event Trigger, invoke AI Workflow via webhook or API call.

→ From Backend Event Consumer Service, invoke AI Workflow via WebClient API call.

- See file for details: `EmKafkaConsumerService.java`

Create Events From Workflows​

→ In workflows, AI models connect to Kafka via Tools to create events.

- Use pre-built kafka event APIs and event templates pre-configured for your data.
- e.g. use tool like:
- `Tool Name: event_publish_product`

RAG and Semantic Cache​

RAG - Retrieval Augmented Generation​

RAG Provides the LLM with knowledge context from matching documents in vectorStore.

Semantic Cache​

Semantic Caching remembers previous answers to similar questions. And avoids expensive LLM calls by reusing responses for similar queries.

Agent Server Redis Configuration​

Agent Server is configured to use Redis - RAG, Semantic Cache If Backend Redis Enable Selected.

  • Dependencies and application.properties are set for using Redis, docker image is redis-stack-server.

Redis Vector Store​

  • RedisVectorStore lets developers use Redis as a high-performance vector database.

Setup and Use Redis Vector Store​

  • RedisVectorStore - To initialize, load documents into, and use it for RAG & Semantic Cache, See sample code file provided:

    • agentserver\src\main\java\com\example\agentserver\RedisVectorStore.java

  • Customize Agent Server to implement them further.

  • Customize to Implement RAG and Semantic Cache

  • Create source files from the sample code and customize existing code to use VectorStore.

  • Please refer to below Spring AI doc pages:

  • You can also initialize vectorStore by uncommenting application.property (instead of Configuration class):

    • spring.ai.vectorstore.redis.initialize-schema=true

Advanced Agentic patterns​

Try out advanced Agentic patterns after completing tutorial "Chapter 6 : AI Agents".

Getting Started Steps​

In src\main\java\com\example\emagent\app\EmAgentSpringApp.java

  • Enable Run EmAgentExtra

The additional agentic patterns are called via:

  • src\main\java\com\example\emagent\app\EmAgentsExtra.java

In EmAgentsExtra.java:

Choose and Enable run flags for below patterns as required.

    boolean runEmAgentParallel = false;
    boolean runEmAgentEvaluatorOptimizerFixed = false; 
    boolean runEmAgentEvaluatorOptimizer = false; 
    boolean runEmAgentOrchestratorWorkers = false; 
    boolean runEmAgentRouting = false;

NOTE: All Agents come with access to MCP Tools

Agentic Pattern Parallelization​

  • AI Agent EmAgentParallel
  • Agentic System Workflows with agentic patterns Parallelization

Agentic Pattern Evaluator-Optimizer Fixed​

  • AI Agent EmAgentEvaluatorOptimizerFixed
  • Agentic System Workflows with agentic patterns Evaluator-Optimizer
    • Fixed - means fixed number of evaluator loops.

Agentic Pattern Evaluator-Optimizer​

  • AI Agent EmAgentEvaluatorOptimizer
  • Agentic System Workflows with agentic patterns Evaluator-Optimizer
    • There is no limit on number of evaluator loops. It is performed till satifying criteria.

Agentic Pattern Orchestrator-Workers​

  • AI Agent EmAgentOrchestratorWorkers
  • Agentic System Workflows with agentic patterns Orchestrator-Workers

Agentic Pattern Routing​

  • AI Agent EmAgentRouting
  • Agentic System Workflows with agentic patterns Routing

Schedule AI Agent Runs​

  • Schedule AI Agent Runs using Kestra
  • See section Agents & Dashboard : Schedule Agent Runs
  • When Creating Agent Run Schedule in Kestra
    • Use java jar process execute based Flow
  • You can use compiled jar runs for below main program:
agent\spring-java\emagent\src\main\java\com\example\emagent\app\EmAgentSpringApp.java

Optionally Enable advanced agentic patterns in it via:

EmAgentsExtra.java
  • Email Agent Run reports:
    • To send an output report via email in Kestra, use the MailSend task