Writing on agentic analytics

• Is read-only MCP access to your database safe?Jul 7, 2026Read-only database access sounds safe for an AI agent, but a real SQL-injection flaw in a popular MCP server bypassed it. Why it is not enough, and what is.
• What is agentic analytics?Jul 6, 2026Agentic analytics is AI agents that answer data questions in plain English by writing their own queries. How it works, why it is hard, what makes one reliable.
• Text-to-SQL vs the semantic layerJul 5, 2026Text-to-SQL writes queries on the fly, a semantic layer serves predefined metrics. How they differ, what the benchmarks show, and when to use each in an agent.
• How to evaluate an analytics agentJul 4, 2026How to evaluate an analytics agent before you trust it. Build a test set, measure execution accuracy, use an LLM judge, and run evals as regression tests.
• Why text-to-SQL fails on real dataJul 3, 2026Text-to-SQL works in demos but fails silently on real warehouses. Why it happens, from ambiguous metrics to wrong joins and missing context, and what fixes it.
• Open-source analytics agents, explainedJul 2, 2026An open-source analytics agent is one you can read, self-host, and control. Why that matters for data teams, the tradeoffs against closed SaaS, and what to look for.
• How to build an analytics agentJul 1, 2026A practical guide to building an analytics agent. Connect your warehouse, engineer the context, give it a correction loop, evaluate accuracy, and deploy.
• Agentic analytics use casesJun 30, 2026Where agentic analytics actually helps, from revenue and finance questions to product analytics, ops, marketing, and giving execs self-serve answers without SQL.
• What is an AI data analyst?Jun 29, 2026An AI data analyst answers data questions in plain English by writing its own queries. What it can do, where it falls short, and how it compares to a human.
• Conversational analytics, explainedJun 28, 2026Conversational analytics lets anyone query data by asking in plain English. How it works, how it differs from dashboards, and what makes it reliable.
• Is it safe to give an AI agent access to your data?Jun 27, 2026The real risks of giving an analytics agent your data, from exposure to wrong answers, and how to run one safely with governance and self-hosting.
• How to run analytics from Slack or TeamsJun 26, 2026Put an analytics agent in Slack or Teams so anyone can ask data questions where they work. How it works, why chat fits, and what to watch for.
• Why MCP alone is not enough for AI analyticsJun 25, 2026MCP connects an AI agent to your data, but it does not make the answers correct. Why analytics agents need a semantic layer on top of MCP, and what Gartner says.
• How to test a text-to-SQL agent in productionJun 24, 2026A hands-on guide to testing a text-to-SQL agent. Build a question set with known answers, measure execution accuracy, and catch silent drift in production.
• How to self-host a semantic layer for AI agentsJun 23, 2026Why self-host a semantic layer for AI agents, the dbt Cloud constraint, the open-source options like MetricFlow and Cube, and how it fits an analytics agent.
• LangChain vs Wren AI vs nao: which open-source tool for agentic analytics?May 22, 2026Three open-source projects for agentic analytics tested on the same BigQuery question. Setup, accuracy, context approach, and which one to pick.
• How to Make the Semantic Layer Work for Analytics AgentsApr 7, 2026How to go from a semantic layer answering no questions to 82% reliability across a wide business range, in 4 steps with real benchmark data.
• How to Do Data Modeling for AI Agents: 8 Practical RulesApr 1, 2026A practical guide to data modeling for AI agents, with clear rules to optimize for precision, reliability, and better chat-with-data performance.
• How to Build a Context Stack for Agentic AnalyticsMar 20, 2026A practical 7-step guide for data teams to build a context stack that improves analytics agent reliability, speed, and cost control.
• How to do context engineering for analytics agentsMar 19, 2026A summary of three context engineering studies on analytics agents. What actually moves reliability, whether a semantic layer is worth it, and how to set up a testing and monitoring framework.
• What's the Best Analytics Agent for Your Data Team?Mar 16, 2026A practical guide to choose the best analytics agent option for your data team, comparing buy, setup, build, and open-source approaches.
• How to Set Up an AI Analytics Slack Bot with an Open Source FrameworkMar 12, 2026A practical step-by-step guide to set up an AI analytics Slack bot with an open source framework so your team can chat with data directly in Slack.
• How to Set Up an AI Analytics Teams Bot with an Open Source FrameworkMar 12, 2026A step-by-step guide to set up an AI analytics Microsoft Teams bot with an open source framework so your team can chat with data directly in Teams.
• 4 Steps to Improve Your Analytics Agent Reliability from 45% to 86%Mar 11, 2026A practical case study on how context engineering, dbt documentation, and data-model fixes improved an analytics agent from 45% to 86% reliability.
• How to Build Your In-House Analytics Agent Fully with Open SourceMar 5, 2026A practical 7-step framework to build your in-house analytics agent fully with open source tooling, from context engineering to evaluation and rollout.
• 5 Steps to Deploy an Analytics Agent on dbt MCP to Your Whole CompanyMar 3, 2026A practical 5-step setup guide to deploy an analytics agent on dbt MCP with nao, from choosing the right MCP to rolling out chat with data across your company.
• How to Build an AI-First Data TeamMar 2, 2026A practical 5-step guide to turning your team into an AI-first data team with context engineering, open source analytics, and chat with data workflows.
• How Data Teams Build Skills for Agentic Analytics: 7 Practical StepsFeb 27, 2026A practical 7-step guide for data teams to design, organize, and test skills that improve analytics agent reliability and chat-with-data outcomes.
• How to Evaluate an Analytics Agent: A Practical Guide with nao testFeb 26, 2026A step-by-step guide to evaluating your analytics agent's reliability using nao's built-in unit test framework, from writing your first test to running the visual dashboard.
• What Is the Impact of a Semantic Layer on Analytics Agent Performance?Feb 24, 2026We tested MetricFlow semantic layers against a plain rules.md file to measure the real impact on analytics agent reliability, cost, and speed. Here is what the data says.
• What Context Has the Most Impact on Analytics Agent Performance?Feb 21, 2026We tested schema, data sampling, profiling, dbt repos, and rules.md to find which context pieces actually improve analytics agent reliability. Here is what the data says.
• 5 Best Open Source Analytics Agents (2026 Comparison)Feb 18, 2026Compared 5 open source analytics agents on SQL accuracy, context depth, and production readiness. Find the right stack for your data team.
• Why data teams need an open framework for context engineeringFeb 14, 2026Analytics agents won't scale without a proper context stack. Here is why context engineering needs its own open framework, and what that framework looks like.
• 5 Reasons Natural Language Analytics is Replacing Traditional BI DashboardsFeb 5, 2026Why natural language interfaces are displacing traditional BI dashboards, and how to prepare your data warehouse for this shift.
• How to Build Production-Ready AI Agents for Data Analytics: The Complete GuideFeb 4, 2026A complete guide to building reliable AI agents for data analytics, from context engineering to production deployment.
• 20 Best AI Analytics Agents Compared: 2026 BenchmarkFeb 3, 2026A full benchmark of 20 AI analytics agents on accuracy, cost, context depth, and data team UX. Warehouse-native tools, AI-native BI, and open-source agents compared.
• Basics of Git data analysts should learnOct 9, 2025Learn Git essentials for data professionals, including practical workflows and how AI can help automate version control for SQL, Python, and data projects.
• How to choose the right data stackOct 5, 2025A comprehensive guide to choosing the right data stack for your company's stage and data maturity.
• How to Set Up dbt Core (2026): Your First Model with AISep 1, 2025Learn to set up dbt Core in 2026. Install the CLI, write staging and fact models on BigQuery, add tests, and use AI to build 10x faster.