Tag: AI Agents
All the articles with the tag "AI Agents".
- 21 MIN READ•Aug 4, 2026
Budgeting for Agentic Analytics When Every Question Costs Something Different
Budgeting for agentic analytics when every question costs something different: token economics, query economics, instrumentation, and the cost controls that actually return.
AI AgentsTCOCost Management - 21 MIN READ•Aug 4, 2026
The Five Layers of an Agentic Lakehouse and Where the MCP Server Sits
The five layers of an agentic lakehouse and where the MCP server sits: storage, catalog, semantic layer, MCP gateway, and agent surface, plus identity, session isolation, and budgets.
AI AgentsMCPAgentic Lakehouse - 21 MIN READ•Aug 4, 2026
Wiring Analytical Queries to Transactional APIs in Closed-Loop Decision Agents
Wiring analytical queries to transactional APIs in closed-loop decision agents: conditional writes, sagas with compensations, decision records, and blast radius controls.
AI AgentsDecision LoopsSaga Pattern - 21 MIN READ•Aug 4, 2026
Why Agentic AI Needs a Governed Semantic Layer Behind the Model Context Protocol
Why agentic AI needs a governed semantic layer behind the Model Context Protocol: metric consistency, access control, Apache Ossie for portable definitions, and Apache Polaris for enforcement.
AI AgentsMCPSemantic Layer - 21 MIN READ•Aug 4, 2026
Moving From Supply Chain Dashboards to Decision Loops With the Model Context Protocol
Moving from supply chain dashboards to decision loops with MCP: sense, decide, act, and verify, with typed action tools, idempotency keys, and graduated human approval.
AI AgentsMCPSupply Chain - 21 MIN READ•Aug 4, 2026
Metric Contracts as the Interface AI Agents Actually Need
Metric contracts as the interface AI agents need: calculation, inclusion rules, grain, temporal semantics, ownership, semantic versioning, and testing metrics in CI.
AI AgentsMetric ContractsSemantic Layer - 21 MIN READ•Aug 4, 2026
Defending the Lakehouse Gateway Against Prompt Injection and Data Exfiltration
Defending the lakehouse gateway against prompt injection and data exfiltration: per-user identity, no-SQL tool surfaces, volume bounds, and detection in query behavior.
AI AgentsPrompt InjectionSecurity - 31 MIN READ•Jul 28, 2026
Guardrails for Analytics Agents That Do More Than Answer Questions
The risk isn't agents going rogue, it's agents acting correctly on bad input at machine speed. Here's how to classify actions by consequence, gate capability, and design approval steps people actually use.
AI AgentsGuardrailsData Governance - 31 MIN READ•Jul 28, 2026
Building Agent Telemetry Tables in Iceberg That Survive an Audit
A practical guide to building agent decision traces in Apache Iceberg that support audit reconstruction, governance review, and cost attribution across sessions.
Apache IcebergAI AgentsData Governance - 31 MIN READ•Jul 28, 2026
What Agentic Analytics Actually Costs, and How to Keep It Bounded
Agent analytics generates two cost streams that scale on different variables. Here's the arithmetic, the levers that actually move the number, and how to build attribution before you need it.
AI AgentsAnalyticsCost Optimization - 31 MIN READ•Jul 28, 2026
When the Query Optimizer Starts Managing Its Own Materializations
Autonomous materialized view management replaces quarterly review meetings with workload-driven scoring, and it's essential when AI agents generate unpredictable query patterns.
Apache IcebergQuery OptimizationAI Agents - 31 MIN READ•Jul 28, 2026
Why AI Agents Fail on Raw Data, and What to Give Them Instead
Agents fail on raw lake data because business rules live in people's heads. Data products with semantic contracts fix this at the source.
AI AgentsApache IcebergSemantic Layer