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  • Courses
    • Agent Harness with Spring AI
      • Start here
        • Topics
        • Front matter
        • Glossary
      • Part 1: Foundations
        • 1. Why a harness?
        • 2. Project setup
        • 3. The model call
        • 4. Tools
      • Part 2: The Loop
        • 5. The agent loop by hand
        • 6. Termination
        • 7. Guardrails
        • 8. Graphs
        • 9. Human-in-the-loop
      • Part 3: Memory and Retrieval
        • 10. Short-term memory
        • 11. Embeddings and hybrid storage
        • 12. Hybrid retrieval
        • 13. Retrieval quality
      • Part 4: Skills and Agents
        • 14. Skills as files
        • 15. Skill selection
        • 16. Sub-agents
        • 17. Agent selection
        • 18. Context isolation
      • Part 5: Observability, Evaluation, and Cost
        • 19. Step logging
        • 20. A small eval set
        • 21. Failure drills
        • 22. Cost optimization
      • Part 6: Durable Execution with Temporal
        • Part 6 overview
        • 23. Run state and messages
        • 24. Temporal basics
      • Part 7: Capstone
        • 25. Assemble the watcher
      • About this course
        • Progress and changelog
    • Apache Spark
      • Course Introduction
      • Python Vs. Scala
      • Introduction
      • About Databricks
      • Spark In The Data Platforms
      • Running Spark
      • Demo: Running Spark on Linux Ubuntu
      • Demo: Running Spark on MacOS
      • Demo: Running Spark on Windows
      • Demo Running Spark On Databricks
      • From Map Reduce To Spark
      • Spark Characteristics
      • Spark Applications
      • Spark Driver
      • Spark Session
      • Spark Cluster Manager
      • Spark Execution Mode
      • Spark Executors
      • Spark Data Partitioning
      • Spark Operations
      • Transformations Narrow Vs Wide
      • Demo: Immutability In Spark
      • Demo: RDD Text Manipulation
      • Demo: GroupByKey Vs. ReduceByKey
      • Demo: Joining RDDs
      • Demo: RDD Operations Part 1
      • Demo: Repartition Vs. Coalesce
    • Data Modeling
      • Introduction to Data Modeling
      • Fact Tables
      • Schema Types
      • Surrogate vs Natural Keys
      • Partitioning vs Bucketing
      • Kimball vs Inmon
      • Dimension Types
        • Conformed Dimension
        • Degenerated Dimension
        • Junk Dimension
        • Role Playing
        • Outrigger Dimension
        • Snowflake Dimension
        • Slowly Changing Dimension
        • Fast Changing Dimension
        • Shrunken Changing Dimension
        • Multi Valued Dimension
        • Swappable Dimension
        • Heterogeneous Dimension
    • Containerization
      • Short-lived containers
      • Long-lived containers
      • More than 1 container app
    • Big Data Engineering in Depth
      • Course Introduction
        • Introduction
        • Max Benefit
        • Assignments, Labs, & Textbooks
        • Course Content Overview
      • Data Warehouse
        • Introduction To Data Management
        • Data Abstraction
        • Data Warehouse Components
    • Hadoop and Distributed Systems
      • Introduction To Distributed Systems
      • Hadoop
      • HDFS
      • YARN
      • Map Reduce
      • Hive
  • Blog
  • Still need help?
  • Join Discord ↗
  • Agent Harness with Spring AI
    • Start here
      • Front matter
      • Glossary
    • Part 1: Foundations
      • 1. Why a harness?
      • 2. Project setup
      • 3. The model call
      • 4. Tools
    • Part 2: The Loop
      • 5. The agent loop by hand
      • 6. Termination
      • 7. Guardrails
      • 8. Graphs
      • 9. Human-in-the-loop
    • Part 3: Memory and Retrieval
      • 10. Short-term memory
      • 11. Embeddings and hybrid storage
      • 12. Hybrid retrieval
      • 13. Retrieval quality
    • Part 4: Skills and Agents
      • 14. Skills as files
      • 15. Skill selection
      • 16. Sub-agents
      • 17. Agent selection
      • 18. Context isolation
    • Part 5: Observability, Evaluation, and Cost
      • 19. Step logging
      • 20. A small eval set
      • 21. Failure drills
      • 22. Cost optimization
    • Part 6: Durable Execution with Temporal
      • Part 6 overview
      • 23. Run state and messages
      • 24. Temporal basics
    • Part 7: Capstone
      • 25. Assemble the watcher
    • About this course
      • Progress and changelog
  • Apache Spark
    • Course Introduction
    • Python Vs. Scala
    • Introduction
    • About Databricks
    • Spark In The Data Platforms
    • Running Spark
    • Demo: Running Spark on Linux Ubuntu
    • Demo: Running Spark on MacOS
    • Demo: Running Spark on Windows
    • Demo Running Spark On Databricks
    • From Map Reduce To Spark
    • Spark Characteristics
    • Spark Applications
    • Spark Driver
    • Spark Session
    • Spark Cluster Manager
    • Spark Execution Mode
    • Spark Executors
    • Spark Data Partitioning
    • Spark Operations
    • Transformations Narrow Vs Wide
    • Demo: Immutability In Spark
    • Demo: RDD Text Manipulation
    • Demo: GroupByKey Vs. ReduceByKey
    • Demo: Joining RDDs
    • Demo: RDD Operations Part 1
    • Demo: Repartition Vs. Coalesce
  • Data Modeling
    • Introduction to Data Modeling
    • Fact Tables
    • Schema Types
    • Surrogate vs Natural Keys
    • Partitioning vs Bucketing
    • Kimball vs Inmon
    • Dimension Types
      • Conformed Dimension
      • Degenerated Dimension
      • Junk Dimension
      • Role Playing
      • Outrigger Dimension
      • Snowflake Dimension
      • Slowly Changing Dimension
      • Fast Changing Dimension
      • Shrunken Changing Dimension
      • Multi Valued Dimension
      • Swappable Dimension
      • Heterogeneous Dimension
  • Containerization
    • Short-lived containers
    • Long-lived containers
    • More than 1 container app
  • Big Data Engineering in Depth
    • Course Introduction
      • Introduction
      • Max Benefit
      • Assignments, Labs, & Textbooks
      • Course Content Overview
    • Data Warehouse
      • Introduction To Data Management
      • Data Abstraction
        • Introduction
        • Physical Layer
        • Logical Layer
        • View Layer
        • Data Solution Thinking
      • Data Warehouse Components
        • Motivation
        • DWH VS Transactional DB
        • Business Types
        • Use cases
        • Multi-temperature Storage
        • Characteristics & Components
        • Source System Integration Process
        • Source System Extraction
        • Staging Layer
        • Data Modeling
        • Introduction to Data Modeling
        • ETL
        • Fact Tables
        • Schema Types
        • Dimension Types
  • Hadoop and Distributed Systems
    • Introduction To Distributed Systems
    • Hadoop
    • HDFS
    • YARN
    • Map Reduce
    • Hive
  • Still need help?
  • Join Discord ↗

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Courses
Agent Harness with Spring AI
Start here

Start here

Front matter, versions, local services, and the glossary.

Topics

  1. Front matter
  2. Glossary
docs
Last updated on Oct 10, 2026

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