{"id":111947,"date":"2026-02-02T09:34:52","date_gmt":"2026-02-02T09:34:52","guid":{"rendered":"https:\/\/www.artificialintelligence-news.com\/?p=111947"},"modified":"2026-02-02T09:34:59","modified_gmt":"2026-02-02T09:34:59","slug":"thoughtspot-on-the-new-fleet-of-agents-delivering-modern-analytics","status":"publish","type":"post","link":"https:\/\/www.artificialintelligence-news.com\/news\/thoughtspot-on-the-new-fleet-of-agents-delivering-modern-analytics\/","title":{"rendered":"ThoughtSpot: On the new fleet of agents delivering modern analytics"},"content":{"rendered":"\n

If you are a data and analytics leader, then you know agentic AI is fuelling unprecedented speed of change right now. Knowing you need to do something and knowing what to do, however, are two different things. The good news is providers like ThoughtSpot are able to assist, with the company in its own words determined to \u2018reimagin[e] analytics and BI from the ground up\u2019<\/a>.<\/p>\n\n\n\n

\u201cCertainly, agentic systems really are shifting us into very new territory,\u201d explains Jane Smith, field chief data and AI officer at ThoughtSpot. \u201cThey\u2019re shifting us away from passive reporting to much more active decision making.<\/p>\n\n\n\n

\u201cTraditional BI waits for you to find an insight,\u201d adds Jane. \u201cAgentic systems are proactively monitoring data from multiple sources 24\/7; they\u2019re diagnosing why changes happened; they\u2019re triggering the next action automatically.<\/p>\n\n\n\n

\u201cWe\u2019re getting much more action-oriented.\u201d<\/p>\n\n\n\n

Alongside moving from passive to active, there are two other ways in which Jane sees this change taking place in BI. There is a shift towards the \u2018true democratisation of data\u2019 on one hand, but on the other is the \u2018resurgence of focus\u2019 on the semantic layer. \u201cYou cannot have an agent taking action in the way I just described when it doesn\u2019t strictly understand business context,\u201d says Jane. \u201cA strong semantic layer is really the only way to make sense\u2026 of the chaos of AI.\u201d<\/p>\n\n\n\n

ThoughtSpot has a fleet of agents to take action and move the needle for customers. In December, the company launched four new BI agents<\/a>, with the idea that they work as a team to deliver modern analytics.<\/p>\n\n\n\n

Spotter 3, the latest iteration of an agent first debuted towards the end of 2024<\/a>, is the star. It is conversant with applications like Slack and Salesforce, and can not only answer questions, but assess the quality of its answer and keep trying until it gets the right result.<\/p>\n\n\n\n

\u201cIt leverages the [Model Context] protocol, so you can ask your questions to your organisation\u2019s structured data \u2013 everything in your rows, your columns, your tables \u2013 but also incorporate your unstructured data,\u201d says Jane. \u201cSo, you can get really context-rich answers to questions, all through our agent, or if you wish, through your own LLM.\u201d<\/p>\n\n\n\n

With this power, however, comes responsibility. As ThoughtSpot\u2019s recent eBook exploring data and AI trends for 2026 notes<\/a>, the C-suite needs to work out how to design systems so every decision \u2013 be it human or AI \u2013 can be explained, improved, and trusted.<\/p>\n\n\n\n

ThoughtSpot calls this emerging architecture \u2018decision intelligence\u2019 (DI). \u201cWhat we\u2019ll see a lot of, I think, will be decision supply chains,\u201d explains Jane. \u201cInstead of a one-off insight, I think what we\u2019re going to see is decisions\u2026 flow through repeatable stages, data analysis, simulation, action, feedback, and these are all interactions between humans and machines that will be logged in what we can think of as a decision system of record.\u201d<\/p>\n\n\n\n

What would this look like in practice? Jane offers an example from a clinical trial in the pharma industry. \u201cThe system would log and version, really, every step of how a patient is chosen for a clinical trial; how data from a health record is used to identify a candidate; how that decision was simulated against the trial protocol; how the matching occurred; how potentially a doctor ultimately recommended this patient for the trial,\u201d she says.<\/p>\n\n\n\n

\u201cThese are processes that can be audited, they can be improved for the following trial. But the very meticulous logging of every element of the flow of this decision into what we think of as a supply chain is a way that I would visualise that.\u201d<\/p>\n\n\n\n

ThoughtSpot is participating at the AI & Big Data Expo Global,<\/a> in London, on February 4-5. You can watch the full interview with Jane Smith below:<\/em><\/p>\n\n\n\n

Photo by Steve Johnson<\/a> on Unsplash<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

If you are a data and analytics leader, then you know agentic AI is fuelling unprecedented speed of change right now. Knowing you need to do something and knowing what to do, however, are two different things. 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