Jedify Launches Data Agents to Automate Business Analytics

Jedify has launched Data Agents, a product that lets business teams build autonomous AI analysts using plain-language instructions. Powered by the company’s context graph technology, the agents can monitor business metrics, investigate anomalies and deliver recurring analysis through Slack, email and MCP-enabled platforms, including Claude and ChatGPT.

Jedify moves analytics beyond dashboards and queries

The launch targets a limitation in conventional business intelligence (BI): dashboards typically answer questions that analysts have anticipated and configured in advance. When business conditions change, teams may still need to request new reports, define queries or ask data specialists to investigate the cause.

Jedify says its Data Agents can take on more of that work autonomously. Users describe the analysis they need in natural language, and the platform creates and refines an agent conversationally, without requiring code or a workflow builder.

The agents use Jedify’s context graph to interpret an organization's metrics, data relationships and business rules. The company says this context layer helps agents understand what business measures mean and how they relate, rather than attempting to infer meaning from raw database tables and schemas each time a question is asked.

That distinction is central to the product. Instead of simply returning a response to a prompt, Data Agents are designed to monitor selected metrics, investigate changes and identify potential reasons behind them.

Agents can investigate changes and deliver recurring analysis

Data Agents supports three execution modes: on-demand requests, scheduled runs and triggers based on defined conditions. This allows teams to automate recurring analysis or configure agents to investigate when a metric crosses a specified threshold.

Jedify describes the product as proactive rather than reactive. Agents can surface anomalies, performance gaps and underperforming markets without waiting for an employee to open a dashboard or ask a question. Its deep research engine is intended to help investigate likely drivers behind changes, giving users more context than a basic metric alert.

The company also says agents are permission-aware by default. They inherit the requesting user's existing data permissions, meaning they should not expose rows, columns or metrics that the user is not authorized to access.

For larger deployments, Jedify argues that its pre-resolved context graph reduces the need for agents to explore raw schemas at query time. It says this approach uses fewer tokens per question than schema-inferencing agents, although the announcement does not provide comparative benchmarks or specific cost savings.

Access through Slack, email and MCP

The agents are available through Jedify’s Agents Hub and can also be exposed as tools through the Model Context Protocol (MCP). This allows teams to call them from Claude, ChatGPT, Slack or other agent platforms they operate. Results can be delivered through Slack and email.

The distribution model is intended to make analytics available within existing work environments rather than requiring employees to adopt another dashboard interface.

Jedify is positioning the product for both data specialists and business teams. Data and analytics departments can reduce repeated requests to build reports, while employees in finance, revenue operations, sales, customer success and product teams can create agents for their own analytical questions.

Early customers describe automated analysis use cases

RubyPlay is among the organizations using the product. Avi Baruch, the company's vice president of data and business applications, said his team configured an agent to run daily and deliver analysis automatically. He also described an instance in which it flagged gaps in target-market coverage.

ESL FACEIT Group (EFG) is another customer. Its VP of Enterprise Technology cited an agent identifying a 10% monthly change in a cost line as an example of the kind of specific finding that can prompt action.

These customer examples illustrate potential applications, but the announcement does not provide independent performance data or quantify the overall impact on analytics workloads.

Market Landscape

Enterprise analytics is moving beyond static dashboards toward conversational interfaces, automated monitoring and AI-assisted investigation. However, the ability of an AI agent to produce useful analysis depends on more than access to data. It must also understand business definitions, relationships, permissions and the context behind a metric.

Context graphs are one approach to that challenge. By organizing business meaning and data relationships for AI systems, they aim to reduce ambiguity when agents interpret enterprise information. The effectiveness of this model will depend on the accuracy and maintenance of the underlying context.

Strategic Outlook

Jedify Data Agents reflects a broader shift from analytics tools that respond to individual requests toward systems that continuously monitor business performance and bring relevant findings to users.

For enterprises evaluating autonomous analytics, important considerations include data access controls, the reliability of root-cause explanations, integration with existing data platforms and the ability to validate agent-generated findings before acting on them.

Jedify says Data Agents is available now through its Agents Hub, with a free starting option. The company is also promoting the product to supply chain planning leaders at the Gartner Supply Chain Planning Summit 2026, where it plans to demonstrate its approach to continuous business analysis.

Top Insights

• Jedify Data Agents lets business users create autonomous analysts through plain-language instructions, without writing code or building workflows.

• Agents can run on demand, on schedules or in response to defined conditions to monitor metrics and investigate anomalies.

• Jedify uses its context graph to provide agents with business definitions, metric relationships and organizational rules.

• Agents can deliver findings through Slack and email or be accessed from Claude, ChatGPT and other MCP-enabled platforms.

• RubyPlay and ESL FACEIT Group describe using the product for recurring analysis and identifying specific business performance changes.

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