Topic
AI Agents
54 posts tagged “AI Agents”.
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Budgeting for Agentic Analytics When Every Question Costs Something Different
The pilot ran for six weeks with forty users and cost less than a team lunch. Someone approved rolling it out to eight hundred people. The first full…
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The Five Layers of an Agentic Lakehouse and Where the MCP Server Sits
Someone on your team connects an AI desktop client to a query engine, asks a question about last quarter, and gets an answer in fifteen seconds. It…
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Wiring Analytical Queries to Transactional APIs in Closed-Loop Decision Agents
An agent reads a table, decides something, and calls an API that changes the world. That sentence contains a distributed systems problem that most…
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Why Agentic AI Needs a Governed Semantic Layer Behind the Model Context Protocol
An executive asks an AI assistant what revenue looked like last quarter. The assistant writes SQL against the warehouse, sums an amount column, and…
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Moving From Supply Chain Dashboards to Decision Loops With the Model Context Protocol
A supply chain control tower shows a red tile. A supplier's on-time delivery rate dropped below threshold four days ago. The tile has been red for…
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Metric Contracts as the Interface AI Agents Actually Need
Two teams present in the same meeting. Sales says pipeline conversion is 24 percent. Finance says it is 19. Both numbers came from the same…
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Defending the Lakehouse Gateway Against Prompt Injection and Data Exfiltration
A support ticket contains a customer's message. Somewhere in that message is a sentence addressed to nobody who works at your company: ignore your…
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Guardrails for Analytics Agents That Do More Than Answer Questions
An agent monitoring inventory levels notices a stockout risk, drafts a purchase order, and submits it. The logic was sound. The signal it read came…
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Building Agent Telemetry Tables in Iceberg That Survive an Audit
An agent gives a customer-facing team a revenue number. Six weeks later someone asks where it came from. The application logs rolled off after 14…
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What Agentic Analytics Actually Costs, and How to Keep It Bounded
A data platform team gets a question from finance in month four of an agent rollout. The engine bill is up 38 percent and the model provider invoice…
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When the Query Optimizer Starts Managing Its Own Materializations
A data engineer maintains 60 materialized views. Twelve of them accelerate queries nobody runs anymore, because the dashboard they served got retired…
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Why AI Agents Fail on Raw Data, and What to Give Them Instead
An analytics agent gets read access to the data lake. Someone asks it for last quarter's revenue by region. It finds a table named factorders, writes…
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The Five Layers Between Your Lakehouse and a Trustworthy Agent
An organization ships an analytics agent. It has access to the warehouse, a good model, and a well-written system prompt. Three weeks in, it has…
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Surviving Commit Conflicts When Dozens of Writers Hit the Same Iceberg Table
A compaction job runs for three hours, rewrites 4,000 files, and dies at the last step with CommitFailedException: Cannot commit changes based on…
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Wiring an AI Agent to Apache Polaris with the Model Context Protocol
An engineer opens Cursor, types "what tables do we have in the sales namespace, and which ones have a customerid column," and gets an answer in four…
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Governing What Agents Cost You
A platform team I spoke with watched their query volume rise 40 times in six weeks. No new dashboards, no new users, no new data sources. What…
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Active Analytics Loops for AI Action Agents
Most analytics agents in production today answer questions. You type a request, the agent generates SQL, runs it, and hands back a chart or a…
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The Chase-Catch Gap in Enterprise AI Agents
Building an impressive AI agent demo takes an afternoon. Wire an LLM to a few tools, give it a system prompt, point it at a sample dataset, and it…
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Data Lakehouse Open Standards for AI Agents
A single analyst running a report might touch three tables and issue a dozen queries in an afternoon. An AI agent working the same problem can issue…
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Five-Layer Agentic Lakehouse Architecture
Hand an AI agent a database connection string and broad SQL access, and you have built the fastest possible path to an inconsistent, unauditable, and…
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GSA MCP Servers for Open Data AI Agents
The U.S. government publishes hundreds of thousands of datasets through Data.gov and agency portals, and almost none of them are shaped for an AI…
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Iceberg Concurrency for AI Agent Writes
A single autonomous agent can attempt more table commits in an hour than a team of analysts produces in a week. Multiply that by a fleet of agents…
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MCP Stateless Gateways for Data Agents
A Model Context Protocol server that holds session state in memory is a server you cannot scale by adding replicas. The moment one process remembers…
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Semantic View Autopilot for AI Governance
Most data glossaries are wrong by the time you read them. A column gets renamed, a metric changes its grain, a new product line ships, and the human…
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Building Closed-Loop Decision Agents: Moving from Passive BI Dashboards to Active Goal-Directed Workflows
Dashboards are excellent at showing people what happened. They are less good at deciding what should happen next. That gap is where closed-loop…
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Designing Your Own AI Harness: A Deep Dive Into the Architecture of Agent Loops, Tools, Context, and Control
The most underappreciated finding in applied AI this year fits in one statistic: a major framework team took the same model, changed nothing about…
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Deterministic Data Engineering With AI Harnesses: Using Claude Code, Codex, Antigravity, and OpenCode for Data Work You Can Actually Trust
There is an apparent contradiction at the heart of using AI agents for data work, and resolving it properly is worth an entire article, because the…
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Enforcing Fine-Grained Security at Machine Speed: Dynamic Access Control for High-Frequency AI Agents
AI agents change the security model for analytics. A human user may run a handful of queries, pause, interpret the answer, and ask a follow-up. An…
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The Metric Contract Mandate: Standardizing Semantic Layers Before AI Agent Access
AI agents are very good at moving quickly. That is the opportunity and the risk. If an agent can inspect metadata, generate queries, compare results,…
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Personal Context vs. Shared Context: A Deep Dive Into How Humans and Organizations Should Feed Their AI Agents
The most important discovery of the agent era fits in one sentence: most AI failures are context failures, not model failures. When your assistant…
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Autonomous Materialization for Agentic Analytics
Autonomous materialization is useful when it is tied to workload evidence, governance checks, and lifecycle management. For platform teams tuning…
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Composable Semantic Layers for Analytical Agents
AI agents need more than metric names. They need composable business logic that survives multi-step analysis. For analytics engineers and AI platform…
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The Context Layer for AI Agents
A semantic layer is necessary, but agents also need lineage, quality, freshness, compliance, and ownership context. For data governance and AI…
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Fine-Grained Security for AI Agents
Machine-speed analytics requires machine-enforced policy, identity, masking, filtering, and audit controls. For security architects and platform…
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The Complete Guide to Agentic Coding Tools in 2026
Agentic coding tools have matured into four distinct categories that serve different developer workflows: CLI agents for terminal-first users,…
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Agentic Lakehouse Concurrency and Isolation
Agentic writes need isolation contracts, not just write permissions. That is the useful lens for agentic lakehouse concurrency in June 2026. The…
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Anatomy of an Agentic Lakehouse
An Agentic Lakehouse is storage, catalog governance, semantic context, and agents working as one operating model. That is the useful lens for agentic…
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Real-Time Agentic Analytics with ClickHouse
Real-time agents need analytical systems that can answer while an event still matters. That is the useful lens for real-time agentic analytics in…
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Goal-Directed Analytics Agents on Apache Iceberg
The next step after text-to-SQL is a governed action loop with checks before every external effect. That is the useful lens for goal-directed…
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Microsoft Fabric Build 2026 Agentic Analytics Stack
Microsoft's Fabric direction shows that agentic analytics is becoming a platform architecture, not a chat feature. That is the useful lens for…
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Securing Agent Identities in the Lakehouse
Every lakehouse agent needs its own identity, scope, and audit trail. That is the useful lens for agent identities lakehouse in June 2026. The market…
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Data Platform Native AI Agent Tooling in 2026
Every data platform vendor now offers some form of AI agent tooling. The approaches vary widely, from full agent authoring frameworks to MCP server…
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Use Hermes Agent for Free With DeepSeek V4 and Slack
Most AI agent frameworks lock you into a paid model. Claude Code needs an Anthropic subscription. Codex needs an OpenAI plan. Cursor costs $20 a…
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A Journey from AI to LLMs and MCP - 10 - Sampling and Prompts in MCP – Making Agent Workflows Smarter and Safer
Sampling and Prompts in MCP – Making Agent Workflows Smarter and Safer
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A Journey from AI to LLMs and MCP - 9 - Tools in MCP – Giving LLMs the Power to Act
Tools in MCP – Giving LLMs the Power to Act
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A Journey from AI to LLMs and MCP - 8 - Resources in MCP – Serving Relevant Data Securely to LLMs
Resources in MCP – Serving Relevant Data Securely to LLMs
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A Journey from AI to LLMs and MCP - 7 - Under the Hood – The Architecture of MCP and Its Core Components
Under the Hood – The Architecture of MCP and Its Core Components
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Journey from AI to LLMs and MCP - 6 - Enter the Model Context Protocol (MCP) – The Interoperability Layer for AI Agents
Enter the Model Context Protocol (MCP) – The Interoperability Layer for AI Agents
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A Journey from AI to LLMs and MCP - 5 - AI Agent Frameworks – Benefits and Limitations
AI Agent Frameworks – Benefits and Limitations
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A Journey from AI to LLMs and MCP - 4 - What Are AI Agents – And Why They're the Future of LLM Applications
What Are AI Agents – And Why They're the Future of LLM Applications
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A Journey from AI to LLMs and MCP - 3 - Boosting LLM Performance – Fine-Tuning, Prompt Engineering, and RAG
Boosting LLM Performance – Fine-Tuning, Prompt Engineering, and RAG
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A Journey from AI to LLMs and MCP - 2 - How LLMs Work – Embeddings, Vectors, and Context Windows
How LLMs Work – Embeddings, Vectors, and Context Windows
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A Journey from AI to LLMs and MCP - 1 - What Is AI and How It Evolved Into LLMs
What Is AI and How It Evolved Into LLMs
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Building a Basic MCP Server with Python
The Basics of Building a Basic MCP Server