Data Agents you build in plain language, alongside custom agentic solutions Jedify AI Studio builds for you – all in one Agents Hub.
Every business runs into the same pattern. Monday morning, Finance needs the weekly performance update. Marketing wants to know why conversion moved. A sales leader needs a pipeline breakdown before a meeting. Some of these repeat every week, others start the moment a metric moves – but either way, they need judgment a static dashboard can’t provide, so someone does the analysis again, by hand.
Traditional BI made data visible, but dashboards only show what someone decided to build in advance. AI chat interfaces made it easier to ask questions, but asking a chatbot the same question every Monday isn’t automation.
There’s a deeper problem: most AI agents don’t actually understand what your data means in the first place. Ask one a real business question and it may start guessing: what counts as an active customer, which definition of revenue to use, which relationships between tables are even valid.
There’s a gap between dashboards that wait for you and chatbots that answer you. Jedify Data Agents fill that gap.
Describe the desired outcome. Get the agent
Data Agents are autonomous analysts you build in plain language. They can monitor your business, investigate changes, answer complex questions, and generate recurring analysis – all grounded in Jedify’s context graph, which gives them the business context they need to understand your data.
Creating one starts with a conversation, not a configuration screen. There’s no workflow to build or code to write – you describe the analysis you want, in as much detail as matters to you: what to track, how to define it, what “unusual” looks like, and how you want the output structured. For example:
“Summarize weekly sales performance by region and product line. Flag any metric that moves more than 15% week-over-week, and investigate the top contributing factors before reporting them. Compare against the same week last quarter, and skip metrics we’ve already flagged as seasonal.”
From there, you can refine the agent through conversation – adjusting what it focuses on, how it approaches the analysis, and what the output should look like. Once it works the way you want, run it immediately, schedule it to run automatically, or do both.
Scheduling an agent doesn’t turn it into a static report. Open it at any time and continue the conversation. Ask why something happened, break a result down by region, compare it with last week’s run, or dig deeper into something it found.
The report talks back.
Agents that actually know what your data means
Autonomous agents are becoming easy to build. Trustworthy autonomous analysts are not.
The hard part isn’t getting an LLM to execute a scheduled prompt. It’s giving that model enough company-specific context to understand what the business actually means, identify the right data, follow the right relationships, and respect the same governance rules as the people using it.
That’s where Jedify’s context graph comes in.
Instead of asking an agent to infer business meaning from raw tables and column names, Jedify’s context graph connects your business concepts to the underlying data. Each entity carries its business definition, underlying query, and documented relationships to other entities.
So when an agent needs to understand what “net revenue” or “active customer” means, it can work from your organization’s definition rather than trying to reconstruct one from the raw schema. It can also reason across different warehouses, models, and taxonomies, automatically routing each part of an investigation to the appropriate data source.
Users don’t need to know where a particular field lives or which tables should be joined. They can focus on the business problem.
That grounding lets an AI agent go from answering “What was revenue?” to investigating “Revenue dropped. Why?”.
From “what happened” to “why it happened”
Underneath Data Agents is Jedify’s Deep Research engine, built for questions that require more than retrieving a single metric. An agent can break a problem into multiple analytical steps, investigate potential drivers, and reason across different pieces of evidence.
That’s what makes Data Agents useful for work that traditionally lands on an analyst’s desk – not just looking up a number, but understanding what happened and why.
That work doesn’t need to start with a manual request. Agents can run on demand, on a schedule, or based on defined thresholds, producing rich analytical artifacts with analysis and visualizations. Results can be consumed in Jedify, delivered through Slack or email, or shared with others.
Instead of an analyst rebuilding the same Monday morning report, the analysis can already be there. Instead of waiting for someone to notice that a KPI crossed a defined threshold, an agent can automatically run the investigation you set up.
The goal is to move recurring analytical work from something people repeatedly request to something the system simply does.
Build your own agents – or let AI Studio build for you
Not every business workflow fits neatly into an agent you create yourself. Some require a more specialized solution designed around a company’s unique processes, data, and users.
That’s where Jedify AI Studio comes in.
AI Studio builds custom agentic solutions around specific business needs. These can span functions across the organization: a Product Engagement Agent for Product teams, Revenue Forecasting for Finance, Meeting Prep for sales reps, QBR generation for Customer Success, Sales Activity Capture for RevOps, and other specialized workflows.
That gives organizations two ways to put agents to work: build your own Data Agents conversationally inside Jedify, or work with AI Studio to create custom agentic solutions for more specialized workflows.
Both share the same foundation and understanding of your business. Instead of one generic AI assistant trying to know everything, organizations can build a collection of purpose-built analysts and agentic applications for different teams and workflows.
Agents Hub: one home for your agentic solutions
As companies adopt more agents, another problem emerges: fragmentation. One agent lives in Slack. Another is behind a bookmarked link. Another has its own application. Soon, nobody knows exactly what exists or where to find it.
That’s why we built Jedify Agents Hub.
Agents Hub brings the Data Agents your team creates and the custom agentic solutions AI Studio builds for you into the same experience. Your agents, analytics, generated artifacts, and conversations can all live inside Jedify, accessible from the same Home page.
AI Studio applications show up as cards directly on Home, so users can discover and open them without digging through Slack threads or bookmarked links.
More importantly, these aren’t disconnected AI experiments. Data Agents you build and solutions AI Studio builds for you can operate from the same context graph and shared understanding of your business.
Their outputs aren’t dead ends, either. Open a previous analysis and ask: Why did this account’s score jump? Break this down by region. What changed since last week’s run?
This isn’t simply another way to query a database. It’s a step toward an analytical workforce of specialized agents that can monitor, investigate, report, and interact with people across the business.
Your agents can meet you where you work
Agents Hub gives your agents a home, but that doesn’t mean everyone has to work inside Jedify.
Data Agent results can be delivered through Slack and email. MCP clients like Claude and ChatGPT can list and display your active agents, letting users choose an agent and ask it questions directly from the tools where they already work.
That means the analytical capabilities you’ve built in Jedify can increasingly become available inside the tools where people already work. Instead of forcing every business user into another interface, the analyst can come to their workflow.
For data teams, that means fewer repetitive requests and more leverage from the analytical knowledge they’ve already built. For business users, it means having specialized analysts available when questions arise – without needing to know SQL, understand the warehouse, or wait in the data team’s queue.
Start with the work you’re already repeating
Getting started doesn’t require an automation project. Start with a job your data team already does repeatedly: the report someone rebuilds every week, the KPI people constantly ask about, or the investigation that starts whenever a particular number moves.
Describe that job to Jedify. Build the agent through a conversation. Run it, look at the result, and tell it what to change. Refine it until it works the way you want. Then turn on the schedule.
We see this as the beginning of a much larger shift in how people interact with enterprise data: from analysis people repeatedly request to analysis intelligent systems can continuously perform on their behalf.
The destination isn’t simply faster BI. It’s a world where companies can give AI agents the context to understand their business and trust them to perform meaningful analytical work within the workflows and guardrails they define.
Your data shouldn’t just answer questions. It should go to work.
