What agentic analytics actually is
For years, “AI in BI” meant a chatbot that turned a question into a single SQL query. Agentic analytics is a different thing. The agent does the work: forming a plan, running many queries, checking the data, interpreting results, and surfacing what matters - the way a human analyst would, but at a breadth no human has time for.
It can be human-led (you steer the investigation in the canvas) or automated (the agent handles reporting, alerting, or low-stakes transactional decisions on its own). Either way, the analysis itself - not just the query - is what has been delegated.
What it means for the data team’s role
When agents can do the analysis, the data team’s job moves up a level: context management. The role becomes providing clean, governed data and context that lets agents be accurate, and making that context accessible across everywhere the business now works - the canvas, Slack, Claude, Cursor, the CRM, the support tool. Done well, this is “headless BI” realised: one governed context layer, many interfaces.
The data team needs to be comfortable with that context being used in different places, because the demands of the business - to use their data alongside other tools like MCP servers, sales intelligence, customer support, transcripts - mean the data team has to provide that clear context layer and let it travel.
(For more on how the analyst role is changing, see The Three Most Important Jobs for Analytics Teams in an AI-First World.)
Why Count’s architecture is the right one
Getting a chart from data on demand is not enough to make data valuable. To turn an answer into a decision you need more than that - you need to discuss the answers with your team, understand how they were built, iterate and explore as you discuss, and give the agent enough compute to be accurate and thorough. That is what Count is built around.
Auditable artifacts, not black boxes.
An agent’s answer is only trustworthy if you can see how it got there. In Count, every query the agent runs is visible, editable and laid out on the canvas - methodology you can inspect, correct and build on. That is what makes agentic output safe to act on.
A shared, infinite canvas for deep exploration.
Decisions are rarely made alone. Count gives agents and humans the same space to go as deep as the question demands, show their working, and bring in qualitative context - comments, sticky notes, data from MCP sources - so a team can reason in the round and reach a decision faster. You do not want to artificially constrain the agent’s ability to access data, because it means it might get things wrong or miss things. The canvas gives it room to go further and show how it got there.
“Count increases the speed of delivery by enabling collaboration - no one is ever working on anything for too long before they collect and iterate on feedback.”
A compute layer built for agents.
Agents are computationally hungry - they fire many queries fast. Run that directly against a warehouse and you either throttle the agent or torch your budget. Count’s compute layer distributes work across the warehouse, Count’s servers and the browser, running ~80% of queries outside the data warehouse with no per-query charge. More compute, run cheaply, means agents that find more and miss less.
“The ability to query the smallest granularity, cache it in Count's local DuckDB server, and play, aggregate and recalculate as much as you like without any further costs - this is amazing.”
Where the category goes in the next 12 months
Agentic analytics is about to get broader. Pre-AI, “analytics” mostly meant tabular data from the warehouse - metrics and charts. Over the next year it expands to mean the ability to take data of any kind and blend it together to get a business decision that is as holistic as possible and therefore more valuable. Qualitative data - not just quantitative data - pulled together in one interface for humans to verify, discuss and act on.
The most valuable analysis will not be the cleanest dashboard. It will be the most complete picture, assembled by an agent and verified by a human. That is the surface Count is building.