Tag:AI Agents
Articles tagged "AI Agents", page 3.
- 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.
AI AgentsMCPAgentic Lakehouse - 21 MIN READ•Aug 4, 2026
Wiring Analytical Queries to Transactional APIs in Closed-Loop Decision Agents
How decision agents can act on analytical reads through transactional APIs safely: conditional writes, sagas with compensations, decision records, blast limits.
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.
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.
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.
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.
AI AgentsPrompt InjectionSecurity - 31 MIN READ•Jul 28, 2026
Guardrails for Analytics Agents That Do More Than Answer Questions
How to put guardrails on analytics agents that take actions: classify by blast radius, gate capabilities, build approval steps, and stop runaway loops.
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.
Apache IcebergAI AgentsData Governance - 31 MIN READ•Jul 28, 2026
What Agentic Analytics Actually Costs, and How to Keep It Bounded
Why agentic analytics produces two bills, engine compute and model tokens, how to attribute and forecast each one, and which levers actually lower total cost.
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.
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 - 31 MIN READ•Jul 28, 2026
The Five Layers Between Your Lakehouse and a Trustworthy Agent
Agent reliability is a property of the stack the model sits on. Five layers with distinct owners and failure modes turn the agent is unreliable.
AI AgentsApache IcebergData Architecture