What is agentic analytics? A guide to enterprise platforms.

A person does not need to navigate several dashboards, write queries, and assemble the findings by hand. They can state a goal instead. The agent plans the analysis, calls the available data and analytical tools, checks what it finds, and returns an answer grounded in those sources. If its permissions allow, it can also start a workflow.

A product leader might ask, "Why did checkout conversion fall for European mobile users on Tuesday?" An agentic system could define the comparison periods, locate the funnel step that changed, inspect affected segments, check related errors and behavior, estimate the business impact, and recommend where the team should investigate next.

That is a different job from generating a chart in response to a prompt. Plenty of natural-language analytics interfaces stop after producing one query or summary. An agent needs autonomy, multi-step reasoning, controlled tool access, business context, and a record of what it did.

What is agentic analytics?

Agentic analytics uses AI agents to pursue an analytical objective rather than answer one isolated prompt. The agent chooses the steps and tools needed, executes them within defined permissions, and combines the results into a finding that someone can inspect.

A typical operating loop looks like this:

1. A person, event, or schedule sets the objective.
2. The agent creates a plan covering the likely metrics, dimensions, periods, and data sources.
3. It uses approved tools, which may include SQL, semantic models, statistical methods, behavioral data, or APIs.
4. It evaluates intermediate results, tests possible explanations, and adjusts the plan when the evidence points elsewhere.
5. It summarizes what changed, the likely reasons, and the measured effect.
6. It recommends or starts an approved action, such as sending an alert, drafting a ticket, or updating a report.
7. It records the sources, queries, assumptions, tool calls, and actions used during the investigation.

A human question can start this loop, but it does not need to. A background agent can watch a defined metric and begin investigating when that metric moves beyond an agreed threshold.

Six defining characteristics of agentic analytics.

Goal-directed autonomy

The user describes the outcome rather than prescribing every analytical step. The agent decides how to pursue that outcome using the tools and permissions available to it.

The scope still needs boundaries. "Investigate a checkout conversion drop" is a workable goal if the system knows which conversion definition, properties, customer groups, and actions it may use.

Multi-step reasoning

An agent can split a broad question into smaller tasks. It might locate an anomaly, isolate the affected population, compare related metrics, and test several possible explanations before returning a finding.

Multi-step execution is a better test than the mere presence of an LLM. A system that translates one natural-language question into one SQL query is AI-assisted. It may be useful, but the workflow is still a single step.

Governed tool use

Agents need controlled access to warehouses, lakehouses, semantic models, statistical functions, digital analytics data, and workflow systems. They should reach those resources through authenticated interfaces that teams can inspect.

The model's internal knowledge is not a substitute for enterprise data. If an agent cannot point to the query, source, and definition behind a number, the number should not drive a production decision.

Proactive monitoring

An agent can run continuously or on a schedule. It might watch checkout completion, feature adoption, page performance, login errors, or customer satisfaction, then begin an investigation before someone notices a dashboard change.

Teams need to define what warrants investigation. If every small fluctuation launches a workflow, the result will be higher compute costs and a queue full of weak alerts.

Action orientation

An analytical workflow can continue into operational work. An agent may draft a Jira ticket, alert an incident channel, route a finding to a product owner, or request approval for another action.

Permissions should reflect the risk. Sending a private alert may require no approval. Changing a production system should require explicit review, a narrow scope, and a rollback path.

Business context

An enterprise agent must understand how the organization defines an active customer, completed order, qualified lead, failed login, and other business terms. Semantic models, ontologies, business rules, and domain-specific skills supply that context.

Without governed definitions, an agent can write technically valid queries that answer the wrong question. This is one of the fastest ways for a convincing demo to fail on real company data.

How agentic analytics works in an enterprise environment.

An agentic analytics product is usually a coordinated system rather than a standalone language model. Most implementations need five connected layers.

1. Data and signal layer

The agent needs timely access to the data that can answer its assigned questions. Depending on the use case, that data may include:

• Warehouse and lakehouse tables
• Product events and digital behavior
• Session and journey data
• Application logs and errors
• Experiment results
• Support tickets and survey responses
• Revenue, transaction, and operational records

Freshness should match the decision. A monthly planning workflow may work with daily updates. Incident triage may need signals within minutes.

More data does not automatically produce a better investigation. Teams should start with the sources that can confirm or reject a specific hypothesis, then add others when they close a known gap.

2. Semantic and context layer

This layer defines metrics, dimensions, relationships, access rules, and business terminology. It may be a semantic model, catalog, ontology, or collection of governed analytical skills.

The context layer should answer questions such as:

• What counts as a conversion?
• Which timestamp determines the reporting date?
• Are employee and test accounts excluded?
• How is revenue attributed?
• Which users may access regional customer data?
• What threshold makes a change operationally meaningful?

Definitions also need owners and version histories. If the company changes its conversion calculation, teams must be able to determine which definition an older agent result used.

3. Agent orchestration layer

The orchestration layer receives the objective, creates a plan, selects tools, and tracks intermediate work. A complex implementation may use several specialized agents, such as one for data retrieval, another for statistical analysis, and another for interpreting the result in a business domain.

Multiple agents add coordination work and more points of failure. Buyers should ask why a workflow needs separate agents and how the platform resolves disagreement between them.

4. Evaluation and control layer

Enterprise teams need to test whether conclusions are accurate, reproducible, and permitted. Controls should include:

• Inspection of queries and source data
• Permission enforcement at each tool call
• Approved metric definitions
• Confidence or uncertainty indicators
• Human review checkpoints
• Evaluation sets built from known questions
• Logs of prompts, outputs, tool calls, and actions
• Feedback and correction workflows

Evaluation cannot be a one-time launch exercise. Data schemas change, business definitions move, and model updates can alter how the same request is interpreted.

5. Delivery and action layer

Findings may appear in chat, a dashboard, email, a mobile application, or a collaboration tool. Some implementations also connect results to ticketing, incident management, experimentation, and other workflow systems.

The interface matters less than the handoff. A useful result reaches the person who owns the problem, includes enough evidence to review it, and states what the agent could not verify.

Agentic analytics vs. traditional and AI-assisted analytics.

Traditional BI, AI-assisted analytics, and agentic analytics can exist in the same organization. They solve different kinds of work.

A practical distinction is:

• Traditional analytics reports what happened.
• AI-assisted analytics lets someone request that answer in plain language.
• Agentic analytics can investigate why it happened and prepare the next step.

The boundaries are not fixed. Many enterprise platforms contain a mix of these capabilities, and a natural-language interface does not prove that the underlying system can plan or execute an investigation independently.

Best agentic analytics platforms for enterprise teams.

There is no universal best platform. Vendors approach agentic analytics from different parts of the data stack, including digital experience analytics, lakehouse infrastructure, semantic modeling, embedded analytics, search-driven BI, and workflow automation.

A useful buying question is, "Which platform fits the data, users, controls, and analytical job?" Counting AI functions on a product page will not answer it.

The table below compares eight enterprise options based on their documented positioning and capabilities as of July 2026\. Product names, packaging, integrations, and commercial terms change, so buyers should verify them with each vendor.

Quantum Metric: Felix Agentic

Felix Agentic is an autonomous insight engine within Quantum Metric. It focuses on web and mobile behavior, journey friction, digital performance, and business impact rather than general enterprise BI.

Felix Chat supports plain-language investigations. Background Agents monitor selected KPIs and analyze defined changes. Copilot supplies contextual assistance within dashboards and Interactions. Skills store business-specific metrics and analytical instructions, while Workflows run analyses on a schedule, in response to a trigger, or when requested.

The relevant evaluation questions are specific. Can the system identify the customer group affected by a conversion change? Can a reviewer inspect the supporting sessions, events, errors, and metric definitions? Can the team control which findings become alerts or tickets?

Felix Agentic is intended for digital experience work. It does not replace a warehouse, lakehouse, or company-wide BI estate.

Databricks: Genie

Databricks Genie is suited to enterprises that already operate on the Databricks platform. Its main advantage is proximity to lakehouse data and Unity Catalog governance.

Genie supports natural-language access to governed data. Databricks also provides a broader environment for building agent workflows with domain context.

A useful test goes beyond SQL generation. Teams should load their actual tables, synonyms, metric definitions, and access rules, then ask questions that contain internal terminology. Sparse metadata will limit the quality of the answers even when the generated query is syntactically correct.

Dremio: Agentic Lakehouse

Dremio's agentic AI approach gives compatible AI agents governed access to enterprise data through its lakehouse, semantic layer, and Model Context Protocol server.

It may fit enterprises that want an infrastructure layer for several agent interfaces. Apache Iceberg support and query-in-place patterns may also suit teams trying to limit data copies.

Dremio provides data access and semantic infrastructure. The enterprise still needs to design the user experience, evaluation process, agent permissions, and operational handoffs built on that infrastructure.

Tellius

Tellius combines natural-language interaction with automated insight discovery, root-cause analysis, cohort analysis, and AutoML.

Its strongest fit is diagnostic work. Typical questions include which factors contributed to a change, which cohorts behaved differently, and which variables were associated with an outcome.

Buyers should use their own questions during evaluation and include cases with weak or conflicting evidence. They should also check how Tellius maps internal business definitions and how clearly it distinguishes correlation from a supported causal claim.

GoodData: GoodData.AI

GoodData.AI approaches agentic analytics through a governed semantic layer, AI-assisted interaction, APIs, and tools for composing agents.

The platform may suit software companies that embed analytics into customer-facing products. It also supports enterprises that need separate tenants or user groups to work from controlled metric definitions.

Buyers should test how much modeling work their source data requires, how tenant rules are enforced, and what happens when a shared definition changes. A semantic foundation can constrain an agent to approved logic, but somebody still has to maintain that logic.

Cube: Cube D3

Cube is primarily a semantic-layer platform rather than a complete BI application. Cube D3 extends that foundation into natural-language and agent-driven analytics.

Cube is best considered as shared metric infrastructure. It can provide consistent calculations to BI tools, embedded applications, and AI agents, which reduces the chance that each interface computes a metric differently.

Organizations evaluating Cube also need to choose the downstream interface, orchestration system, and workflow tools that will consume its semantic model. Those surrounding systems will determine how much autonomy the final implementation has.

ThoughtSpot: Spotter

ThoughtSpot Spotter builds on ThoughtSpot's search-oriented analytics experience. It allows business users to ask questions in natural language, explore findings, and receive AI-generated insights.

ThoughtSpot may fit organizations that prioritize self-service BI and embedded analytics. During evaluation, teams should separate conversational assistance from autonomous work. Ask whether Spotter can plan and execute the full multi-step investigation required by the proposed use case, then inspect the evidence behind its answer.

Domo

Domo combines data integration, business intelligence, applications, AI capabilities, and workflow automation.

Its integrated approach may reduce the number of separate products required to connect data, analysis, and operational processes. That breadth does not guarantee depth for every analytical task.

Buyers should test the reasoning, permissions, and traceability of the exact workflow they plan to deploy. A general platform comparison cannot substitute for that exercise.

Enterprise agentic analytics use cases for digital and product teams.

Agentic systems fit work that requires repeated investigation, crosses several dimensions, or needs a shorter path from detection to action. A narrow, recurring workflow usually makes a better starting point than a general request to "analyze the business."

Funnel and conversion diagnosis

An agent can monitor checkout, registration, quote, login, or onboarding funnels. When performance moves beyond a defined threshold, it can:

• Locate the step where abandonment increased
• Compare affected devices, browsers, regions, and acquisition channels
• Check for related errors or performance changes
• Find behaviors associated with abandonment
• Estimate affected users, conversions, or revenue
• Alert the responsible product or engineering team

The evidence matters more than the summary. Reviewers should be able to inspect the segments, sessions, calculations, and time windows used. The agent should describe contributing factors without turning correlation into causation.

Anomaly detection and root-cause analysis

Product teams often monitor connected metrics such as active users, feature adoption, latency, errors, conversion, and retention. An agent can start with an anomalous metric and inspect relevant dimensions to narrow the cause.

Suppose overall conversion falls by 4%. The aggregate number does not say where to look. An agent might find that most of the change came from Android users on one application version after a release, while iOS and web conversion stayed flat. That gives the team a bounded problem to verify.

Thresholds need care. A statistically unusual movement may have little business effect, while a small percentage change in a high-volume funnel may cost a large amount of revenue.

Customer journey synthesis

Journey analysis often combines aggregate metrics with detailed behavioral evidence. Agents can summarize recurring paths, friction signals, and outcomes across many sessions.

Teams might ask:

• Which journeys most often precede a support request?
• Where do customers return to an earlier step?
• Which errors appear in sessions that fail to convert?
• How do journeys differ between new and returning customers?

Session-level evidence should remain available for review. A neat journey summary can hide several distinct behaviors if the agent groups them too aggressively.

Experiment and feature-impact analysis

An agent can retrieve experiment results, inspect segment differences, summarize statistical outcomes, and translate them into product terms.

It can also monitor performance after the original experiment ends. This may expose a result hidden by the overall average, such as a navigation change that improves desktop discovery but increases mobile abandonment.

Agents should not replace statistical review. Teams still need decision thresholds, sample-size requirements, rules for excluding invalid traffic, and safeguards against repeated-testing errors.

Release and incident triage

After a release, an agent can monitor selected workflows, detect deviations, compare behavior before and after deployment, and assemble the supporting evidence.

A controlled workflow might:

1. Detect an error-rate increase that exceeds an agreed threshold.
2. Isolate the affected application version and customer segment.
3. Calculate the effect on conversion or task completion.
4. Compare the timing with the deployment record.
5. Draft an incident ticket containing the evidence.
6. Request human approval before escalation or remediation.

This is a good proof-of-concept candidate because the workflow is bounded, the evidence can be reviewed, and the team can measure time saved against its current incident process.

Voice-of-customer synthesis

Agents can group themes across surveys, support tickets, reviews, and in-product feedback. Connecting those themes to behavioral and operational data can help teams separate frequently mentioned complaints from problems with measurable customer or business effects.

Text data brings its own controls. Customer comments may contain names, account details, payment information, or health data. Access restrictions, retention rules, and redaction should be tested before the agent processes production text.

Self-service product questions

Product managers and business users can ask routine questions without entering an analyst queue. Examples include:

• What changed in onboarding completion this week?
• Which customer segment adopted the new function fastest?
• Where are customers encountering promotion errors?
• Which pages generate the most help requests?
• Did the navigation change affect product discovery?

Analysts still govern metrics, review high-impact findings, and investigate ambiguous questions. The useful shift is that they spend less time recreating routine cuts of the same data.

How to evaluate agentic analytics platforms.

The largest implementation risk is the gap between a controlled demonstration and production. Demos tend to use clean data, familiar questions, and preconfigured definitions. Production data contains conflicting metrics, incomplete metadata, restricted fields, schema changes, and questions the vendor has never seen.

A useful proof of concept uses representative data and deliberately difficult cases.

1. Accuracy and groundedness

Test whether the system applies approved definitions and produces results that another person can reproduce.

Ask vendors to demonstrate:

• The query, source data, and filters behind each answer
• How the agent handles ambiguous terms
• Whether repeated questions produce consistent results
• How it communicates insufficient evidence
• How a correction becomes reusable business context
• Whether calculations can be checked independently

Build an evaluation set containing known answers, ambiguous requests, incomplete data, and adversarial prompts. Record partial failures rather than reducing every answer to pass or fail. A correct metric with the wrong time window is still wrong, but the cause and remedy differ from a fabricated source.

2. Governance and security

The agent should inherit or enforce enterprise controls, including:

• Role- and attribute-based access
• Row- and column-level restrictions
• Data masking
• Regional and tenant boundaries
• Audit logs
• Service-account controls
• Prompt and response retention policies
• Approval requirements for external actions

Check permissions at every tool call. A secure chat interface is insufficient if the agent's underlying service account can retrieve data the user cannot access.

Tests should include deliberate attempts to cross tenant, regional, and role boundaries. Review the logs afterward to confirm that blocked requests are recorded clearly.

3. Semantic readiness

Assess whether the organization has consistent definitions for its main metrics. If it does not, determine whether the platform helps create and govern them or assumes they already exist.

A semantic model should accommodate:

• Shared metrics and dimensions
• Business-specific calculation rules
• Time and attribution logic
• Entity relationships
• Versioning and ownership
• Exceptions for different domains
• Machine-readable descriptions for agents

Semantic readiness is often less mature than teams expect. Ask two departments to define "active customer" or "conversion" before the proof of concept. If they return different formulas, the agent needs an explicit rule for which one applies in each context.

4. Domain fit

Match the platform to the primary analytical job:

• Digital experience diagnosis: Quantum Metric Felix Agentic
• Lakehouse-native analytics: Databricks Genie
• Open agent access to lakehouse data: Dremio
• Root-cause and ML-supported analysis: Tellius
• Governed embedded analytics: GoodData
• Shared semantic infrastructure: Cube
• Search-oriented self-service BI: ThoughtSpot
• Integrated data, BI, and workflow environment: Domo

These are starting points rather than exclusive categories. A large enterprise may use several products at different layers, such as Cube for shared metrics and another platform for the user-facing agent.

5. Agent observability

Teams should be able to reconstruct how the system produced a result. Look for records of:

• The original objective
• The generated plan
• Data sources accessed
• Queries and tool calls
• Intermediate findings
• Metric definitions applied
• Model and configuration versions
• Human approvals
• Downstream actions

This record supports debugging and compliance reviews. It also lets analysts distinguish a data problem from a planning error or an unsupported interpretation.

6. Human-in-the-loop controls

Define autonomy levels before deployment. One workable model is:

• Level 1, answer: The agent returns a finding with evidence.
• Level 2, recommend: It proposes an action for review.
• Level 3, prepare: It drafts a ticket, message, or workflow.
• Level 4, execute with approval: A person authorizes the action.
• Level 5, execute automatically: The agent acts within narrow, reversible boundaries.

Most teams should begin at Levels 1 through 3\. Higher autonomy requires measured accuracy, clear ownership, monitoring, and a way to stop or reverse the action.

7. Integration and actionability

Determine whether the platform fits the tools people already use. Relevant integrations may include Slack, Microsoft Teams, Jira, ServiceNow, experimentation systems, data catalogs, and incident-management platforms.

Ask whether the agent returns a narrative answer or can pass structured evidence downstream. A ticket containing the affected application version, customer segment, metric calculation, and source links is easier to review than a paragraph saying conversion appears to be down.

8. Total cost of ownership

Agent costs extend beyond the software license. Model:

• Platform and AI licensing
• Data processing and query consumption
• Model inference or API charges
• Storage and data-movement costs
• Semantic modeling and administration
• Evaluation and monitoring work
• Integration development
• Compute generated by background agents

Use realistic query volumes and monitoring schedules. A background agent that checks 40 metrics across several segments every few minutes can create far more compute activity than a dashboard opened by a person twice a day.

A practical agentic analytics proof-of-concept plan.

A controlled evaluation can expose production risks before a broad rollout. The goal is to learn where the system fails, how reviewers catch those failures, and what operating cost the workflow creates.

Select one valuable, bounded workflow

Choose a recurring problem with a measurable cost, such as checkout diagnosis, release monitoring, or feature-adoption analysis. Avoid an enterprise-wide "answer anything" objective.

Define the start and end of the workflow. For release monitoring, the agent might begin when a deployment record appears and finish after it drafts an evidence-backed incident ticket or confirms that monitored metrics remain within expected ranges.

Build a representative question set

Include 25 to 50 questions across several categories:

• Known questions with verified answers
• Multi-step diagnostic questions
• Ambiguous business terms
• Questions requiring restricted data
• Questions with no valid answer
• Questions spanning several periods or segments
• Attempts to bypass governance rules

Use wording collected from the people who will use the system. A question set written entirely by the implementation team often misses the abbreviations, vague references, and internal terms found in day-to-day requests.

Establish success metrics

Measure:

• Answer accuracy
• Metric-definition accuracy
• Source traceability
• Time to a useful finding
• Analyst review effort
• False-positive alert rate
• Percentage of findings that prompt action
• Query and model cost
• User trust after evidence review

Set thresholds before reviewing the results. Otherwise, teams tend to excuse failures after seeing a polished answer.

Run in observation mode

Allow the agent to analyze and recommend without changing systems. Compare its output with analyst findings and record recurring failure patterns.

Observation mode should run long enough to encounter ordinary data issues, including delayed pipelines, missing events, and schema changes. A two-day test on static data will miss most of them.

Expand autonomy gradually

Once the system meets its agreed thresholds, permit low-risk actions such as sending internal alerts or drafting tickets. Keep high-impact and irreversible actions behind explicit approval.

Review performance after each change in autonomy. A reliable analysis agent can still fail when a downstream integration maps a field incorrectly or sends the result to the wrong queue.

The future of agentic analytics.

Several product categories are moving toward agentic analytics from different directions. BI vendors are adding agents to self-service tools. Data platforms are opening governed enterprise data to agent workflows. Semantic-layer providers are making business definitions readable by machines. Domain platforms are building agents around specialized data and analytical methods.

The chat interface will become the least defensible part of these products. Enterprise performance will depend on the quality and timeliness of the data, governed definitions, reliable tool execution, reviewable evidence, permissions, and integration with the workflow where a person makes the decision.

Agentic analytics does not eliminate dashboards, analysts, or data engineering. Dashboards still handle recurring monitoring. Analysts own metric governance and difficult interpretation. Data teams maintain the pipelines, models, and access controls underneath the agent. The agent takes on more of the repetitive work between detecting a signal and assembling an investigation.

For buyers, a successful demo answer is a weak test. Run the system against conflicting definitions, missing data, restricted fields, and an unfamiliar question. Then inspect every query and tool call it made.

Agentic analytics sources and further reading.

• Quantum Metric: Felix Agentic
• Databricks: Genie documentation
• Dremio: Agentic AI
• Tellius: Agentic analytics platform
• GoodData: Agentic analytics guide
• Cube: Agentic analytics with Cube D3
• ThoughtSpot: Agentic analytics explained
• Domo: Agentic analytics overview