# Agentic Analytics Now > AI agents plus unified governed semantic data equals agentic analytics. This site covers the agent stack of model, harness, and open interfaces, the open lakehouse that holds the governed data underneath, and what it takes to run the combination across an enterprise rather than one pilot team. Written by Alex Merced, Head of Developer Relations at Dremio and co-author of Apache Iceberg: The Definitive Guide. The argument in one paragraph: an AI agent can do an analyst's work, taking a question, finding the data, running the query, checking the result, and trying again. What decides whether it is right is not the model. It is whether the data underneath has one definition per metric, one authoritative copy per dataset, and one place where access is enforced. Agents multiply readers and multiply the temptation to make private copies, which is why an open lakehouse built on Apache Parquet, Apache Iceberg, Apache Polaris, Apache Arrow, and Apache Ossie is the substrate that holds up under that pressure. The site is static, has no login, and every page listed here is public. ## What agentic analytics is - [What is agentic analytics](https://agenticanalyticsnow.com/knowledge-base/what-is-agentic-analytics): An AI agent doing the work of an analyst against governed data, with the authority to run queries and the obligation to show where the answer came from. - [From dashboards to questions](https://agenticanalyticsnow.com/knowledge-base/from-dashboards-to-questions): Dashboards answer questions somebody anticipated. Agents answer the ones nobody did, which changes what the data layer has to support. - [Text to SQL and why it stalls](https://agenticanalyticsnow.com/knowledge-base/text-to-sql-and-its-limits): Generating SQL from a question was solved years ago. Generating the right SQL against a real schema was not, and the gap is definitional rather than technical. ## The agentic stack: model, harness, open interfaces - [The agentic stack](https://agenticanalyticsnow.com/knowledge-base/the-agentic-stack): Three separable layers, model, harness, and open interfaces, each with its own failure mode. Most agent problems are misattributed across them. - [The model](https://agenticanalyticsnow.com/knowledge-base/the-model): What the reasoning layer actually contributes, what it cannot know, and why treating model choice as the whole architecture leads people astray. - [The harness](https://agenticanalyticsnow.com/knowledge-base/the-harness): The program that runs the loop. It decides context, tools, retries, memory, and when to stop, which is where most agent behaviour actually comes from. - [Open interfaces](https://agenticanalyticsnow.com/knowledge-base/open-interfaces): The four contracts an analytics agent speaks through, why each should be a specification rather than a product, and what a closed one costs later. ## The open lakehouse substrate - [Why an open lakehouse](https://agenticanalyticsnow.com/knowledge-base/why-the-lakehouse): Agents multiply the number of readers and the number of questions. That pressure breaks architectures that assume one engine, one copy, and predictable queries. - [The semantic layer as an agent contract](https://agenticanalyticsnow.com/knowledge-base/semantic-layer-as-contract): The place where a business writes down what its terms mean. Without it, an agent guesses at definitions, and a fluent guess is worse than an error. - [Apache Iceberg](https://agenticanalyticsnow.com/knowledge-base/apache-iceberg): The table format that lets many readers and writers share one set of files safely, which is exactly the condition agentic analytics creates. - [Apache Polaris](https://agenticanalyticsnow.com/knowledge-base/apache-polaris): An open catalog that resolves table names, decides who may read them, and hands out credentials scoped to just those files. The enforcement point agents cannot route around. - [Apache Arrow](https://agenticanalyticsnow.com/knowledge-base/apache-arrow): A standard in-memory layout for tabular data, so results move between the engine, the tool, and the agent without being repeatedly converted. - [Apache Parquet](https://agenticanalyticsnow.com/knowledge-base/apache-parquet): The columnar file format most lakehouse data sits in. Its statistics are what let an agent's exploratory query read a fraction of the table instead of all of it. - [Apache Ossie](https://agenticanalyticsnow.com/knowledge-base/apache-ossie): An incubating project defining a vendor-neutral standard for semantic metadata, so a metric definition is portable rather than trapped in one tool. ## Running it in an enterprise - [Governance for agents](https://agenticanalyticsnow.com/knowledge-base/governance-for-agents): Identity, scoped credentials, and audit, arranged so that an agent cannot reach what its user cannot reach, and so you can prove it afterwards. - [One copy, many readers](https://agenticanalyticsnow.com/knowledge-base/one-copy-many-readers): The architectural property that decides whether agentic analytics scales past a pilot, and the specific way copy sprawl produces disagreeing answers. - [Evaluation and trust](https://agenticanalyticsnow.com/knowledge-base/evaluation-and-trust): How to know whether an analytics agent is right, why accuracy on a benchmark is not the question, and what to measure instead. - [Cost and latency](https://agenticanalyticsnow.com/knowledge-base/cost-and-latency): Agentic analytics changes the shape of the workload from few large queries to many small ones, which moves where the money goes and what needs optimising. - [Portability and lock-in](https://agenticanalyticsnow.com/knowledge-base/portability-and-lock-in): What it would actually cost to replace each component of an agentic analytics stack, and why the semantic layer is now the most likely trap. - [Rolling this out enterprise wide](https://agenticanalyticsnow.com/knowledge-base/enterprise-rollout): What separates an agent pilot that impresses a steering committee from a capability a whole organisation uses, and the order to do the work in. ## Site pages - [Home](https://agenticanalyticsnow.com/): the thesis, with the stack and the substrate summarised. - [The stack](https://agenticanalyticsnow.com/the-stack): the four layers drawn out, top to bottom. - [Knowledge base](https://agenticanalyticsnow.com/knowledge-base): all 20 entries in four sections. - [Writing and video](https://agenticanalyticsnow.com/writing): articles, playlists, community, and sister sites. - [Books](https://agenticanalyticsnow.com/books): 37 titles on agents, semantics, and the lakehouse, from a catalog of 65. ## Selected writing - [What Is Agentic Analytics?](https://www.dremio.com/blog/what-is-agentic-analytics): The definition, and what separates an agent answering a question from a chatbot guessing at one. - [The Semantic Layer: The Definitive Guide](https://www.dremio.com/blog/semantic-layer-the-definitive-guide): Why shared definitions became load bearing the moment machines started asking the questions. - [Apache Polaris: The Catalog Standard for Lakehouses and AI](https://www.dremio.com/blog/apache-polaris-the-catalog-standard-for-lakehouses-and-ai): One catalog that both engines and agents resolve against, with access decided in one place. - [Open Source and the Data Lakehouse](https://www.dremio.com/blog/open-source-and-the-data-lakehouse): Assessing openness layer by layer, which is the test that decides whether agents can reach your data at all. - [What Are Table Formats and Why Were They Needed?](https://www.dremio.com/blog/what-are-table-formats-and-why-were-they-needed): How files in object storage became tables that many readers can query at once without stepping on each other. - [What Apache Iceberg Native Actually Means](https://www.dremio.com/blog/what-apache-iceberg-native-actually-means): Telling real table format support from a connector that reads and little else, which matters more once agents depend on it. ## Channels - [Dremio blog](https://www.dremio.com/blog/author/alex-merced/): The working archive: agentic analytics, semantic layers, catalogs, and the lakehouse underneath all of it. - [YouTube, data and AI](https://www.youtube.com/@alexmerceddata): Walkthroughs and explainers covering both halves of this subject, from agent tooling to table format internals. - [Data Lakehouse Hub](https://datalakehousehub.com): The community hub: articles, a knowledge base, events, and a Slack where practitioners compare notes on what actually shipped. - [YouTube playlist: Lakehouse Engineering](https://www.youtube.com/playlist?list=PLsLAVBjQJO0qVfGet6FEQw-nZ6ygLtYuH): Building the substrate: table formats, catalogs, engines, and maintenance. - [YouTube playlist: Data 101](https://www.youtube.com/playlist?list=PLsLAVBjQJO0p_4Nqz99tIjeoDYE97L0xY): Foundations, for anyone joining this from the AI side rather than the data side. ## Sister sites in this network - [AgenticLakehouse.com](https://agenticlakehouse.com): Agents operating on lakehouse data, covered at greater length. - [OpenLakehouse.AlexMerced.com](https://openlakehouse.alexmerced.com): The lakehouse substrate on its own terms, one entry per layer and project. - [SemanticLakehouse.com](https://semanticlakehouse.com): The semantic layer, which is the contract this whole argument rests on. - [OpenAgenticPlatform.com](https://openagenticplatform.com): The wider agent platform: protocols, harnesses, identity, and governance. - [AlexMercedAI.com](https://www.alexmercedai.com): The AI and agent projects, including the open specs behind them. ## Community - [Data Lakehouse Hub Slack](https://join.slack.com/t/thedatalakehousehub/shared_invite/zt-274yc8sza-mI2zhCW8LGkOh1uxuf8T5Q) - [Agentic Lakehouse events](https://luma.com/agenticlakehouse) - [Data Lakehouse Hub events](https://luma.com/DataLakehouseHub) - [r/datalakehouseandai](https://www.reddit.com/r/datalakehouseandai/) - [The podcast on Spotify](https://open.spotify.com/show/2PRDrWVpgDvKxN6n1oUsJF) ## Books on these subjects - [AI and Agents for Normal People](https://books.alexmerced.com/books/ai-and-agents-for-normal-people/): A practical guide to using AI tools for everyday tasks, covering Gemini, NotebookLM, ChatGPT, Claude, and local agent workflows without technical jargon. - [AI Application Architecture: Patterns for Building Intelligent Systems](https://books.alexmerced.com/books/ai-application-architecture-patterns-for-building-intelligent-systems/): Covers architectural patterns for building intelligent applications, from inference pipelines to multi-agent orchestration. - [Apache Iceberg for Agentic AI: Connecting Structured Enterprise](https://books.alexmerced.com/books/apache-iceberg-for-agentic-ai-connecting-structured-enterprise/): Explores how to connect structured enterprise data in Apache Iceberg lakehouses to agentic AI systems. - [Being Productive with Open Models](https://books.alexmerced.com/books/being-productive-with-open-models/): A practical guide to using open models with Opencode, PI, and Hermes for AI-assisted development. - [Building Knowledge Systems for AI: Graphs, RAG, Memory, and Context](https://books.alexmerced.com/books/building-knowledge-systems-for-ai-graphs-rag-memory-and-context/): A deep dive into building robust knowledge systems for AI using knowledge graphs, RAG, memory architectures, and context management. - [Constructing Context and Semantics for AI Agents](https://books.alexmerced.com/books/constructing-context-and-semantics-for-ai-agents/): A practical guide to embeddings, knowledge graphs, memory systems, RAG, evaluation, and production agent architectures. - [Enabling Agentic Analytics with Apache Iceberg and Dremio](https://books.alexmerced.com/books/enabling-agentic-analytics-with-apache-iceberg-and-dremio/): A practical guide to building AI-ready lakehouse systems using Apache Iceberg and Dremio for agentic analytics workflows. - [Evaluating AI Systems: Testing LLMs, RAG, and Agents](https://books.alexmerced.com/books/evaluating-ai-systems-testing-llms-rag-and-agents/): A guide to properly testing and evaluating AI systems, including benchmarking strategies for LLMs, RAG pipelines, and agents. - [Governing AI Systems](https://books.alexmerced.com/books/governing-ai-systems/): A guide to AI governance frameworks, covering risk management, compliance, ethics, and organizational oversight of AI systems. - [Hands-On Agentic Engineering: A Practical Guide to Building Multi-Agent Systems](https://books.alexmerced.com/books/hands-on-agentic-engineering-a-practical-guide-to-building-multi-agent-systems/): A practical guide to building multi-agent systems with Apache Iceberg. - [The 2026 Guide to Lakehouses, Apache Iceberg and Agentic AI](https://books.alexmerced.com/books/the-2026-guide-to-lakehouses-apache-iceberg-and-agentic-ai/): A comprehensive guide covering the convergence of lakehouse architecture, Apache Iceberg, and the rise of agentic AI systems. - [The Agentic Enterprise: Deploying AI Agents Across the Modern Organization](https://books.alexmerced.com/books/the-agentic-enterprise-deploying-ai-agents-across-the-modern-organization/): A guide to deploying AI agents across enterprise functions, covering strategy, governance, and organizational adoption. - [The AI Lakehouse: Architecting Data Platforms for AI](https://books.alexmerced.com/books/the-ai-lakehouse-architecting-data-platforms-for-ai/): Explores how to architect data lakehouse platforms specifically designed to serve AI and machine learning workloads. - [The Book on Agentic Analytics](https://books.alexmerced.com/books/the-book-on-agentic-analytics/): Explores the foundations of agentic data architecture, where AI agents autonomously discover, query, and analyze data. - [The Book on Data, Data Engineering, Data Analytics, and Agentic AI - Volume 1](https://books.alexmerced.com/books/the-book-on-data-data-engineering-data-analytics-and-agentic-ai-volume-1/): A complete guide to modern data systems, data engineering, analytics, and AI. - [The Book on Data, Data Engineering, Data Analytics, and Agentic AI - Volume 2](https://books.alexmerced.com/books/the-book-on-data-data-engineering-data-analytics-and-agentic-ai-volume-2/): A complete guide to SQL analytics, BI, and agentic AI for modern data platforms. - [The No Lock-in Apache Iceberg Lakehouse with Agentic Analytics](https://books.alexmerced.com/books/the-no-lock-in-apache-iceberg-lakehouse-with-agentic-analytics/): A practical guide to building a portable Apache Iceberg lakehouse with open catalogs, agentic analytics, and AI-ready data platforms while avoiding vendor lock-in. - [Using AI Agents for Data Engineering and Data Analysis](https://books.alexmerced.com/books/using-ai-agents-for-data-engineering-and-data-analysis/): A practical guide to leveraging AI agents for automating data engineering pipelines and performing intelligent data analysis. - [AI-Ready Data](https://books.alexmerced.com/books/ai-ready-data/): Designing data platforms optimized for LLMs, AI agents, and retrieval-augmented generation (RAG) workflows. - [Dremio and the Semantic Layer](https://books.alexmerced.com/books/dremio-and-the-semantic-layer/): The journey to providing meaning and context for AI with Dremio's semantic layer. - [Shipping AI: From Prototype to Production Systems](https://books.alexmerced.com/books/shipping-ai-from-prototype-to-production-systems/): Covers the full lifecycle of taking AI systems from prototype to production, including deployment, scaling, and monitoring. - [The 2026 Guide to AI-Assisted Development](https://books.alexmerced.com/books/the-2026-guide-to-ai-assisted-development/): Covers prompt engineering, AI coding assistants, and agent-driven development workflows for modern software engineers. - [The AI Engineering Handbook](https://books.alexmerced.com/books/the-ai-engineering-handbook/): A full-stack reference for building AI systems, covering LLMs, embeddings, agents, RAG, and production deployment patterns. - [The Economics of AI: Cost, Latency, and Infrastructure Tradeoffs](https://books.alexmerced.com/books/the-economics-of-ai-cost-latency-and-infrastructure-tradeoffs/): Analyzes the cost structures of AI systems and how to optimize spending across compute, latency, and infrastructure. - [The Economics of Labor in the AI Era](https://books.alexmerced.com/books/the-economics-of-labor-in-the-ai-era/): Examines how the AI era is disrupting the labor market and explores paths for adaptation and the future of work. - [Apache Iceberg: The Definitive Guide](https://books.alexmerced.com/books/apache-iceberg-the-definitive-guide/) (O'Reilly Media): A comprehensive guide to Apache Iceberg and modern lakehouse architecture, covering table format internals, schema evolution, partitioning, and integration with major query engines. - [Apache Polaris: The Definitive Guide](https://books.alexmerced.com/books/apache-polaris-the-definitive-guide/) (O'Reilly Media): A guide to open catalogs for Apache Iceberg lakehouses, covering Polaris catalog architecture, configuration, and governance. - [Architecting an Apache Iceberg Lakehouse](https://books.alexmerced.com/books/architecting-an-apache-iceberg-lakehouse/) (Manning Publications): Architecture patterns and best practices for building production-grade, Iceberg-based data platforms from the ground up. - [The Book on Apache Polaris](https://books.alexmerced.com/books/the-book-on-apache-polaris/): Building the open data lakehouse with the industry standard Iceberg REST catalog. - [The Lakehouse Built for Everyone](https://books.alexmerced.com/books/the-lakehouse-built-for-everyone/): From laptop prototypes to enterprise-scale agentic AI with Apache Iceberg and Dremio. - [The Open Source Lakehouse: Architecting the Decoupled Analytical Foundation](https://books.alexmerced.com/books/the-open-source-lakehouse-architecting-the-decoupled-analytical-foundation/): Covers building a fully decoupled lakehouse using open source technologies, emphasizing modularity and vendor independence. - [The Book on Using Apache Iceberg with Python](https://books.alexmerced.com/books/the-book-on-using-apache-iceberg-with-python/): A hands-on guide to working with Apache Iceberg using Python tools like PyIceberg, PySpark, and other Python-based engines. - [Being Productive with Claude](https://books.alexmerced.com/books/being-productive-with-claude/): A practical guide to Claude.ai, Claude Code, and Claude Dispatch for AI-powered development. - [Being Productive with Google AI](https://books.alexmerced.com/books/being-productive-with-google-ai/): A practical guide to using Google's AI ecosystem including Gemini, NotebookLM, Veo, Gemini Spark, and Jules. - [Being Productive with Grok and Cursor](https://books.alexmerced.com/books/being-productive-with-grok-and-cursor/): A practical guide to the xAI and Cursor coding ecosystem covering Grok Chat, Grok Build, DeepSearch, Cursor Composer, Agent Mode, MCP servers, and local model runtimes for agentic development workflows. - [Being Productive with Microsoft Copilot](https://books.alexmerced.com/books/being-productive-with-microsoft-copilot/): A practical guide to the Microsoft Copilot ecosystem covering Copilot Chat, M365 Copilot, GitHub Copilot Agent Mode, Copilot Studio, Copilot Cowork, and Copilot Plus PC features. - [Being Productive with OpenAI Codex](https://books.alexmerced.com/books/being-productive-with-openai-codex/): A practical guide to building documents, images, video, websites, and apps with an AI agent using OpenAI Codex. Full catalog: https://books.alexmerced.com/ ## Newsletters Two free newsletters go out each week on Substack: https://amdatalakehouse.substack.com - AI newsletter, every Thursday: Model releases, agent tooling, protocols, and AI infrastructure from the past week. - Apache lakehouse newsletter, every Friday: What moved on the Apache Iceberg, Polaris, Arrow, and Parquet dev lists. ## The rest of the network ### Lakehouse and data - [OpenDataLakehouse.com](https://opendatalakehouse.com) - [SemanticLakehouse.com](https://semanticlakehouse.com) - [OpenLakehouse.AlexMerced.com](https://openlakehouse.alexmerced.com) - [IcebergLakehouse.com](https://iceberglakehouse.com) - [AgenticLakehouse.com](https://agenticlakehouse.com) - [DataLakehouseHub.com](https://datalakehousehub.com) - [DataLakehouse.help](https://datalakehouse.help) - [DataEngnr.com](https://dataengnr.com) - [DataAIWiki.com](https://dataaiwiki.com) - [WeekOfData.com](https://weekofdata.com) ### AI and agents - [AlexMercedAI.com](https://www.alexmercedai.com) - [OpenAgenticPlatform.com](https://openagenticplatform.com) - [AgenticAnalyticsNow.com](https://agenticanalyticsnow.com) ### Identity and work - [AlexMerced.com](https://alexmerced.com) - [WhoIsAlexMerced.com](https://whoisalexmerced.com) - [AlexMercedCoder.dev](https://alexmercedcoder.dev) - [AlexMercedData.com](https://alexmerceddata.com) - [AlexMercedMedia.com](https://alexmercedmedia.com) - [Books by Alex Merced](https://books.alexmerced.com) - [Resources](https://resources.alexmerced.com) ### Writing - [AlexMerced.blog](https://alexmerced.blog) - [GrokOverflow.com](https://grokoverflow.com) - [IngestThis.com](https://ingestthis.com) - [Coding tutorials](https://tuts.alexmercedcoder.dev) ## Notes for agents - This site exposes read-only WebMCP tools in the browser: get_agentic_analytics_thesis, list_knowledge_base, search_knowledge_base, get_knowledge_base_entry, list_agentic_analytics_writing, and list_agentic_analytics_books. - Structured data is published as JSON-LD on every page, including WebSite, Person, TechArticle, BreadcrumbList, CollectionPage, ItemList, and Book nodes. - Apache Iceberg, Apache Polaris, Apache Parquet, Apache Arrow, and Apache Ossie are trademarks of the Apache Software Foundation. This site is independent and is not affiliated with or endorsed by the ASF.