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Harness Engineering for Data Agents on Databricks: Building Reliable Agentic Data Platforms with MCP, Databricks, Data Contracts, Governance, and Multi-Agent Workflows (AI Agents & MCP Series) (en Inglés)
Calado, Leandro (Autor)
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Independently published
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Harness Engineering for Data Agents on Databricks: Building Reliable Agentic Data Platforms with MCP, Databricks, Data Contracts, Governance, and Multi-Agent Workflows (AI Agents & MCP Series) (en Inglés) - Calado, Leandro
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Reseña del libro "Harness Engineering for Data Agents on Databricks: Building Reliable Agentic Data Platforms with MCP, Databricks, Data Contracts, Governance, and Multi-Agent Workflows (AI Agents & MCP Series) (en Inglés)"
Move Beyond Prompt Engineering. Welcome to Harness Engineering. An AI model can explain a failed pipeline, draft SQL, and suggest a data contract[cite: 1]. However, none of those acts establishes that it should be allowed to change production[cite: 1]. As the enterprise AI landscape shifts heavily toward AgentOps and secure multi-agent workflows, the biggest challenge is no longer making AI smarter—it is building the strict environment that controls it. Harness Engineering for Data Agents on Databricks is the definitive guide to designing the runtime and control environment in which model reasoning can safely become action[cite: 1]. Instructions shape behavior, but controls constrain behavior[cite: 1]. This book teaches you how to separate the model's probabilistic reasoning from the deterministic execution plane of your data platform[cite: 1]. Through the lens of a real-world payment company case study[cite: 1], author Leandro Calado[cite: 1] provides a deep dive into building reliable agentic data platforms[cite: 1]. You will learn how to build a harness that owns tool discovery, identity, context selection, stopping conditions, and budgets[cite: 1]. Inside this book, you will master: The Minimum Agency Principle: Learn to use the minimum amount of agency necessary to solve the problem[cite: 1]. Multi-Agent Orchestration Patterns: Coordinate specialized roles like the Requirements Agent, Catalog Agent, and SQL Agent through typed, immutable handoffs[cite: 1]. Building an MCP Gateway: Use the Model Context Protocol to expose versioned tools, authenticate callers, authorize each call, and enforce time and cost limits[cite: 1]. Databricks Implementation: Leverage Unity Catalog as the governance and platform boundary[cite: 1]. Use Lakeflow Jobs for workflow automation and Delta Lake for transactional safety and atomic commits[cite: 1]. The Production Autonomy Budget: Calculate operational blast radius and risk to dynamically route agent decisions to EXECUTE, PREPARE_FOR_APPROVAL, RECOMMEND_ONLY, or DENY[cite: 1]. Data Contracts & Verification: Treat data contracts as versioned, machine-enforceable agreements[cite: 1]. Implement independent quality gates that evaluate program, data, and business correctness before promotion[cite: 1]. Harness Engineering for Data Agents on Databricks shows you how to stop treating unconstrained conversation as operational authority[cite: 1]. If you are ready to build the autonomous data platform of the future without sacrificing governance and security[cite: 1], this book provides the architecture, code, and operational rigor to get you there.
