How Agents Schema brings trusted business context to AI
Explore the architecture, open standards, and semantic layer that give AI agents access to accurate, reusable business context.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Collaborative BI That Drives Action: From Shared Insights to Shared Accountability
Here’s a scenario, and not an uncommon one either. A dashboard flags a margin drop on Tuesday morning. Someone from the Sales team adds a comment. Finance adds another. A colleague from Operations agrees the number looks wrong. By Friday, the issue is still open, and no one owns the fix. That is the gap in many business intelligence collaboration setups. The data was shared. The discussion happened. The decision never moved.
The Hallucination Tax You Can't Afford as a CFO
Most finance leaders have now tried an AI assistant on real data. Most of them had a similar experience. Here is what is actually happening, and what trustworthy finance AI looks like when it works.
Reliable Pipelines, Predictable Bills: Why Settle for One Without the Other
Somewhere along the way, data teams accepted a trade-off: pipelines that just work, or bills that you can actually forecast, pick one. So you live with the silent failures, the schema changes that break dashboards overnight, and the month-end invoice that never quite matches the data you moved. Not because it's acceptable, but because it's familiar. In this session, we're challenging that trade-off head-on. We'll break down where pipelines fail quietly and where costs inflate invisibly and show you, live, what it looks like when your pipeline gives you full visibility into every sync, every record, and every dollar.
How Centerprise AI Makes AI Agents Scalable for Production | Astera
Scaling AI agents to production comes down to the data foundation behind them. Astera's Centerprise AI gives agents secure, governed access to enterprise data, and its business context without exposing it to the LLM. Watch to see how it works.
How to Track AI Search Visibility With GA4 and Search Console
A manual walkthrough, and the Claude skill that does it for you.
New: Trusted data for the people and the AI making decisions on it
Ask three people in your company to pull the number of active customers this month, and you’ll probably get three different answers, even though each person labeled the metric the same way. One counts everyone who logged in, another counts only paying users, and a third filters down to a single plan tier. Nobody is wrong here. They’re all working from real data; they just never agreed on a single definition. Do that enough times, and the data itself becomes the thing everyone argues about.
Spotter Enhancements
Spotter just got smarter and more in your control. You can now customize your agent's name, persona, output formatting, and guardrails to dictate exactly how it should (and shouldn't) handle data. Set it once, in plain language, and every user across the organization gets a configured, governed Spotter. Other new features include: Ad-hoc file analysis: Upload any flat file directly into Spotter and start asking questions instantly, solo or blended with your governed data.
Agent development and AgentOps with BigQuery, ADK, and MCP
Join this session to learn about Agent Development Kit (ADK) and Model Context Protocol (MCP) integration methods that standardize how agents connect to your data while removing the need to build custom database connectors from scratch. Discover how to build agents with the ADK that accesses BigQuery for analysis, Google Maps for geospatial insights, and AlloyDB for transactions – all in a single workflow. Learn how to implement agent operations (AgentOps) for deep observability into both agent performance and cost with a single line of code.
Demo: Real-Time Context Engine for Fleet Management
Use Real-Time Context Engine and Claude, or any MCP-compatible client, to explore operational data using natural language in real time. That includes everything from simple lookups to multi-step investigative questions like: Confluent’s Real-Time Context Engine gives AI agents live access to operational context as events happen across the business. Instead of relying on stale snapshots, agents can query and reason over continuously updated tables in real time.