Systems | Development | Analytics | API | Testing

Cheap Tokens, Exploding Bills: The Hidden Cost of AI Agents

80% cheaper AI model. Nearly 2X the data warehouse bill. Switching to a cheaper AI model can cut your token costs by 80%, but it might accidentally double your public cloud data warehouse bill. In this video, we break down the "Agentic Cost Shift"—how autonomous AI agents generate hidden, high-compute SQL queries that drive up data lakehouse costs—and how to fix it using Cloudera. See where the real cost of AI is shifting.

Modernizing at the speed of AI: How state and local governments can keep quality in check

For the first time ever, artificial intelligence has topped NASCIO’s annual ranking of state CIO priorities, ending cybersecurity’s 12-year reign at number one. It’s easy to see why: public agencies – like their private sector counterparts – see real potential in AI to modernize operations, accelerate service delivery, and help lean teams do more with fewer resources.

Migrating Your BlazeMeter Tests With an AI Agent

Our BlazeMeter to OctoPerf Maven plugin is archived. What replaces it is not another tool to install: OctoPerf 17 ships a BlazeMeter migration playbook for AI agents. Real prompts, real output, run against a real BlazeMeter account. The plugin worked: one mvn command copied your workspaces, projects and JMeter scripts into OctoPerf. But a Maven command cannot ask you a question, so it never said which tests were worth moving, never brought your load profiles, and never replayed what it imported.

Insurance Underwriting Automation: Architecture, AI Models, and ROI (2026)

Insurance underwriting automation integrates data ingestion, validation, risk scoring, decisioning, and policy workflows. It connects core insurance platforms with rules engines, AI models, and external data sources. Insurance underwriting automation in 2026 looks markedly different from earlier pilots.

Thought Tank: Marketing in the Age of Agents

Join us for a live broadcast of The Thought Tank: Marketing in the Age of Agents. Our host and CMO Micheline Nijmeh sits down with Katie Marcham, SVP Marketing EMEA at ThoughtSpot, to pull back the curtain on what it actually looks like to run a modern marketing organization on live data in one of the most complex, relationship-driven markets in the world. They'll cover the transformation Katie's led over the past year: leaner teams, smarter tools, and a tighter partnership with EMEA sales, all grounded in what the data is showing in real time.

Agentic AI in Banking: How Autonomous AI Is Changing Financial Services

Quick answer: Agentic AI in banking refers to AI systems that don’t just generate text or answer questions – they take a goal, break it into steps, use tools and data sources, make decisions, and complete multi-step workflows (like investigating a fraud case or processing a KYC file) with minimal human intervention, looping in a person only for genuine judgment calls. Walk into any banking technology conference in 2026, and you’ll notice the conversation has quietly shifted.

How to Answer Any Performance Question with an AI Analyst

Ask one question with a time range, a metric, a comparison, and a goal. The AI analyst does the gathering. You keep the judgment. To answer any performance question in minutes, ask Databox’s AI Analyst, Genie, one well-built question that includes: a time range, a metric, a comparison, and a goal. Genie queries the data sources you’ve connected, runs the calculation, and returns the answer with a recommendation attached.

[Finance Demo] - AgentSpot Use Case - Collections Forecast

Every month, finance teams rebuild their collections forecast by hand, copying and pasting from disconnected files and hoping nothing breaks. In this video, we use AgentSpot to build a Collections Forecast Agent that connects to accounting files, NetSuite, and live bookings data to automate the full monthly rebuild, reconcile actuals against forecast, and output a traceable Excel workbook your whole team can work from.