AI agent for data analysis: How autonomous analysts work

TL;DR

• An AI data analysis agent retrieves evidence from live business systems rather than answering from training data.

• The autonomous analyst follows seven steps: define the question, discover data sources, retrieve selectively, validate and clean, explore patterns, test hypotheses, and produce ranked recommendations.

• Composio provides infrastructure for AI agents to connect with 1,000+ business applications, so developers skip building and maintaining individual integrations.

Answering even a straightforward business question rarely involves a single dataset or tool. A question such as "Why did enterprise customer retention decline last quarter?" may require someone to compare product usage data from Amplitude, acquisition information from Google Analytics, customer records from Salesforce, subscription data from Stripe, support conversations from Zendesk, and historical revenue trends stored in Snowflake or BigQuery. Each system stores only one piece of the overall picture, meaning analysts spend more time gathering and preparing information than interpreting it.

What does an AI data analysis agent do?

An AI data analysis agent is an autonomous software system that combines the reasoning capabilities of a large language model with the ability to interact directly with external databases, APIs, and analytics platforms. Unlike a traditional chatbot, which generates responses from information already available in its training data, an autonomous analyst actively retrieves current business information before drawing conclusions. Business data changes continuously. Even the most capable model cannot explain why conversion rates declined yesterday without access to the systems where that information is stored.

How autonomous analysts work

An autonomous analyst decomposes each problem into a sequence of smaller reasoning tasks, building understanding progressively before reaching a conclusion.

A human analyst would rarely know the answer immediately. They would develop a plan, determine which systems contain the relevant information, retrieve multiple datasets, compare historical performance, investigate potential anomalies, validate their findings against additional evidence, and only then prepare a report for stakeholders. Consider a question such as:

"Why did enterprise conversions decline during June?"

An AI analysis agent follows a similar process, decomposing the original question into logical subproblems and refining its understanding as each piece of evidence becomes available.

Step 1: Understand the business question

Every successful data analysis begins with a well-defined question. That's also where many analyses fail: business questions are often broad, ambiguous, or framed around symptoms rather than underlying problems. Before retrieving a single row of data, an autonomous analyst must first determine exactly what it is trying to explain, which metrics are relevant, what time period should be examined, and what constitutes a clear answer.

This planning stage influences every subsequent decision the agent makes. If the original objective is misunderstood, the agent may retrieve the wrong datasets, analyze irrelevant metrics, or draw conclusions that fail to address the user's actual concern.

The agent first converts a high-level request into a structured investigation plan before touching a single database. It identifies the business objective, determines which performance indicators should be measured, establishes an appropriate comparison period, and considers which external factors could reasonably influence the observed results.

For example, suppose a marketing director asks:

"Why are we getting fewer demo requests?"

Although the question appears straightforward, it leaves many important details unanswered. Does "fewer" refer to week-over-week performance or year-over-year performance? Are demo requests declining across every acquisition channel or only paid advertising? Did website traffic decrease, or are visitors simply converting less frequently? Has the sales team changed qualification criteria?

The AI responds by creating an investigation plan that might look something like this:

• Measure demo request volume over the past six months.

• Compare conversion rates against historical averages.

• Analyze traffic sources to determine whether visitor quality changed.

• Review landing page performance.

• Check CRM progression from visitor to qualified lead.

• Identify any product releases, pricing changes, or operational events that coincide with the decline.

• Compare findings across different customer segments.

How Composio helps with planning

Although the planning stage happens before data retrieval, the agent still benefits from understanding which tools are available within the organization. Through our platform, the agent can discover the systems it has permission to access and incorporate those into its investigation strategy.

For example, if the organization has connected Google Analytics, Snowflake, Amplitude, and Microsoft Power BI, the agent knows it can retrieve website traffic, historical warehouse data, product engagement metrics, and executive dashboards without requiring additional engineering work. If Mixpanel is connected instead of Amplitude, the reasoning process is unchanged, because we abstract away most of the differences between individual integrations.

Example: A SaaS trial decline

Imagine a SaaS company that notices a sharp drop in free trial signups compared to the previous month. Instead of immediately querying Google Analytics, the autonomous analyst first determines what information it needs to collect before drawing conclusions.

Its investigation plan might include:

• Compare overall trial volume month over month.

• Measure changes in website traffic.

• Analyze traffic sources individually.

• Review landing page conversion rates.

• Compare desktop and mobile performance.

• Examine recent product releases.

• Check whether CRM qualification rates changed.

• Review advertising spend across major campaigns.

By organizing the investigation before collecting data, the agent avoids wasting time analyzing metrics that are unrelated to the original problem.

Step 2: Discover where the data lives

Once the agent has clearly defined the objective, the next challenge is determining where the required information exists. Organizations rarely maintain all of their operational data in a single system; instead, they distribute information across specialized applications, each optimized for a different business function.

For human analysts, locating the correct datasets is often one of the most time-consuming parts of an investigation. Documentation may be outdated, multiple versions of the same metric may exist across different dashboards, and similar tables may contain subtly different definitions.

Autonomous analysts face the same challenge, but instead of relying on institutional knowledge, they can inspect the tools available to them and determine which systems are most likely to contain the required information.

For example, answering a question about declining enterprise conversions might require information from several independent systems:

Although these systems all contribute to the same investigation, each represents only one piece of the overall picture. An autonomous analyst must understand which platform is authoritative for each metric before collecting data.

How Composio helps with discovery

Developers need to manually teach the AI how to authenticate with every analytics platform. We eliminate that by providing a unified catalog of connected tools. The agent can inspect the integrations available to it, understand their capabilities, and determine which ones are relevant for the investigation it planned in the previous step.

For example, an organization may have connected:

• Snowflake

• Google BigQuery

• Google Analytics

• Amplitude

• ClickHouse

• Metabase

• Microsoft Power BI

• Databricks

The agent doesn't need custom code for each platform. It simply knows these resources are available and can select the most appropriate ones based on the analytical task. When the organization adds another supported analytics platform through our catalog, the agent gains access without requiring engineers to redesign the reasoning workflow.

Step 3: Retrieve the data

Once the agent knows what it needs to investigate and where each system stores the relevant information, it can begin retrieving data from the organization's connected systems. This is the first stage where the autonomous analyst starts interacting directly with external applications rather than simply planning its investigation.

The information needed to answer a question typically spans multiple datasets, each stored in a different format, refreshed on a different schedule, and governed by different permission models. An autonomous analyst must determine which data to retrieve. It must also decide how much information to collect, which filters to apply, whether historical comparisons are necessary, and how to combine the retrieved datasets once they arrive.

For example, suppose a company's Head of Growth asks:

"Why has monthly recurring revenue growth slowed during the past quarter?"

The agent may need to collect subscription data from the company's data warehouse, marketing acquisition metrics from Google Analytics, product engagement events from Amplitude, customer expansion opportunities from Salesforce, and cancellation reasons from the support platform.

The agent retrieves only what its investigation plan calls for. It may first request monthly recurring revenue grouped by customer segment, followed by trial-to-paid conversion rates, followed by customer acquisition costs, and finally churn metrics for enterprise accounts. As each dataset is collected, the agent evaluates whether additional information is required before continuing.

The agent evaluates after each retrieval whether another system should be queried before drawing conclusions. Autonomous analysts apply the same reasoning process experienced analysts use, continuously deciding whether they have sufficient information.

How Composio helps with retrieval

Without an integration platform, retrieving data from multiple business systems quickly becomes one of the largest engineering challenges in an autonomous analysis workflow. Every application exposes different authentication mechanisms, request formats, pagination rules, rate limits, and response structures.

We remove much of this complexity by providing standardized access to connected applications. Instead of writing individual integrations for every analytics platform, developers expose those tools through our platform, allowing the AI agent to retrieve information using a consistent interaction model. Our integration layer handles authentication, OAuth token management, API permissions, and connection lifecycle management, allowing the agent to focus on deciding which information to retrieve rather than how to execute each request.

Example: An onboarding redesign

A product team wants to understand why activation rates dropped after launching a redesigned onboarding flow. Before analyzing user behavior, the autonomous analyst retrieves information from several connected systems:

• Product event data from Amplitude showing how users moved through the onboarding flow.

• Website acquisition data from Google Analytics to determine whether traffic quality changed.

• Customer records from Snowflake to identify user segments.

• Dashboard summaries from Power BI to compare current activation metrics against historical performance.

Rather than collecting every available metric, the agent retrieves only the datasets relevant to activation performance, allowing it to begin analysis immediately while avoiding unnecessary processing.

Step 4: Clean, validate, and prepare the data

Retrieving data is only the beginning of the analytical process. Before the agent can draw conclusions, it must determine whether the information it has collected is accurate, complete, and suitable for analysis. Raw business data is rarely perfect. Missing values, duplicate records, inconsistent formatting, conflicting definitions, delayed updates, and incomplete tracking are common across almost every organization.

The agent treats newly retrieved datasets as evidence to be verified: it examines the structure of each dataset, identifies anomalies, checks whether important fields are missing, compares metrics against historical baselines, and determines whether the information is sufficiently reliable to support further analysis.

Consider a scenario where the agent retrieves customer acquisition data from Google Analytics and subscription records from Snowflake. Closer inspection shows campaign names use different naming conventions, each system records timestamps in a different time zone, and customer identifiers do not match across the two systems. Ignoring these inconsistencies could reverse any conclusions drawn from the combined data.

Validation also involves confirming that the retrieved information answers the original business question. If one of those datasets turns out to be incomplete or unavailable, the agent may need to adjust its investigation, retrieve additional information from another connected system, or explicitly acknowledge the limitation before continuing.

Once the agent validates the data, it prepares it for analysis by standardizing formats, aligning timestamps, resolving duplicate records where appropriate, calculating derived metrics, and organizing the information into a consistent structure.

How Composio helps with validation

Although data cleaning is fundamentally an analytical task rather than an integration challenge, we still play an important supporting role. Because the agent can access multiple connected systems through a common integration layer, it can retrieve supplementary information whenever inconsistencies are detected.

For example, if customer identifiers appear incomplete in one dataset, the agent can retrieve additional records from the organization's data warehouse. If dashboard metrics conflict with raw event data, it can query another connected analytics platform to verify the discrepancy. Our platform enables the agent to compare evidence across multiple connected systems before deciding whether the data is sufficiently reliable for further analysis.

This ability to cross-reference information matters more as analytical workflows incorporate dozens of independent data sources. We allow the agent to move between these systems as needed, maintaining secure, authenticated access throughout the investigation without requiring developers to manually coordinate connections.

Step 5: Explore the data and find patterns

With clean and validated data available, the autonomous analyst can begin the stage most people traditionally associate with data analysis: exploring the data to understand what happened. The objective is not simply to summarize the available information but to identify relationships, anomalies, trends, and behavioral changes that explain the original business question.

Experienced analysts rarely examine metrics in isolation because individual numbers provide very little context. A decline in revenue, for example, may appear concerning until it is viewed alongside customer acquisition, pricing changes, product usage, seasonality, and historical growth trends.

Autonomous analysts approach exploration in much the same way. They continuously compare different datasets, asking whether observed changes occur together, whether one metric appears to influence another, and whether the timing of different events suggests a possible relationship. Each observation generates additional hypotheses that the agent investigates before arriving at a conclusion.

Suppose an ecommerce company asks:

"Why did average order value decrease during the past two months?"

A single metric doesn't explain the cause. Instead, the agent begins exploring related factors that might contribute to the decline. The investigation could include questions such as:

• Did the product mix change?

• Did more customers purchase discounted products?

• Did mobile customers behave differently than desktop customers?

• Did specific acquisition channels bring lower-value shoppers?

• Were promotional campaigns running during this period?

• Did repeat customers purchase less frequently?

• Were certain geographic regions affected more than others?

Each of these questions represents a potential explanation that must be supported or rejected based on evidence. The agent systematically evaluates each possibility by comparing the relevant datasets and looking for statistically significant differences.

If the agent notices that average order value declined only among customers arriving from paid social campaigns, it can immediately investigate campaign-level performance instead of continuing to analyze unrelated traffic sources. If it discovers that order values remained stable across every acquisition channel but dropped only for first-time customers, it can shift its attention toward onboarding experiences or promotional incentives.

How Composio helps with exploration

Exploratory analysis often requires the agent to move rapidly between different business systems as new questions emerge. Traditional dashboards are designed to answer predefined questions; autonomous analysts continuously generate new hypotheses throughout the investigation. Every hypothesis may require additional data from another connected application.

Consider an agent investigating declining customer retention. During its initial analysis, it notices that retention fell primarily among customers acquired through organic search. To understand why, it retrieves website engagement metrics from Google Analytics. Those metrics reveal that users are spending less time on onboarding pages than in previous months. The agent then queries Amplitude to inspect onboarding completion rates, before retrieving customer support conversations to determine whether new users are reporting common points of confusion.

Without an integration layer, this workflow would require separate implementations for every analytics platform involved. Through our platform, the agent can query any connected system without building a separate integration for each one, retrieving additional evidence whenever a new hypothesis needs to be tested.

This flexibility allows the analytical process to resemble the workflow of an experienced consultant. The investigation is driven by evidence rather than predetermined reports.

Example: A churn spike

Imagine a B2B SaaS company that notices customer churn rising during the previous quarter. The autonomous analyst begins exploring potential explanations.

It compares churn across:

• Customer size

• Industry

• Geographic region

• Subscription plan

• Product usage frequency

• Customer support activity

• Onboarding completion

• Time since signup

During this exploration, the agent discovers that churn remained stable across nearly every segment except customers who signed up within the previous six months. It then shifts its investigation toward onboarding experiences and early product adoption instead of continuing to analyze long-term customer behavior. The analysis then shifts toward onboarding experiences and early product adoption instead of continuing to analyze long-term customer behavior.

Step 6: Test hypotheses and identify root causes

Exploratory analysis generates observations, but observations alone do not explain why something happened. Identifying a correlation between two metrics is not the same as establishing that one caused the other.

After exploring the available data, the agent begins testing hypotheses that could plausibly explain the original business problem. It systematically evaluates competing explanations until the available evidence supports one hypothesis more strongly than the others.

Suppose the agent has determined that free trial conversions declined during June. Several possible explanations immediately emerge. The decline could be caused by:

• Lower-quality traffic from advertising campaigns.

• A pricing change that discouraged prospects.

• A technical issue affecting the signup form.

• A redesign of the landing page.

• Reduced product performance during onboarding.

• Seasonal buying behavior.

• Changes to CRM qualification criteria.

An autonomous analyst continues investigating until it has ruled out competing possibilities or gathered sufficient evidence to rank them by likelihood.

For example, if website traffic remained constant while conversion rates fell only on mobile devices, the agent might inspect session recordings or frontend deployment logs to determine whether a usability issue was introduced. If conversion rates declined across every device but only among visitors from paid advertising, it may investigate campaign targeting and audience composition instead. Every new finding either strengthens or weakens one of the competing hypotheses.

Importantly, the agent also recognizes when the available evidence is insufficient. Rather than fabricating certainty, it can report that several explanations remain plausible and recommend additional data collection before a definitive conclusion can be reached.

Trustworthy autonomous analysts distinguish between conclusions supported by evidence and educated speculation. Business decisions often involve incomplete information, and reliable AI systems must distinguish between conclusions supported by evidence and educated speculation.

How Composio helps with root cause analysis

Root cause analysis frequently requires the agent to investigate systems that were not part of the original data retrieval plan. New hypotheses emerge throughout the investigation, and validating them often means accessing additional applications that contain supporting evidence.

For example, an agent investigating declining onboarding completion might begin with Google Analytics and Amplitude before discovering that support ticket volume spiked after a recent product release. To verify whether these events are connected, the agent retrieves customer support data from another connected platform. If support conversations consistently mention the same onboarding issue, the evidence elevates that hypothesis above the remaining candidates.

Because we provide access to a broad ecosystem of business applications through a unified integration layer, the agent can expand its investigation naturally as new evidence emerges without developers anticipating every possible analytical path in advance.

Step 7: Generate recommendations instead of just reporting metrics

Finding patterns matters, but analysis only produces results when it influences decisions. The ultimate objective of an autonomous analyst is not simply to explain what happened. It is to help organizations determine what should happen next.

Traditional business intelligence tools excel at reporting historical performance. Dashboards can show declining conversion rates, increasing churn, or slowing revenue growth, but they generally stop there.

Autonomous analysts extend the workflow beyond reporting by translating analytical findings into actionable recommendations. Once the investigation has identified the most likely explanation for a business problem, the agent considers which interventions are most likely to improve future outcomes based on the available evidence.

Suppose the investigation concludes that onboarding completion declined because mobile users encountered increased loading times after a recent product release. Rather than ending the report with this observation, the autonomous analyst can recommend practical next steps. It can suggest prioritizing frontend performance improvements, running an A/B test comparing onboarding flows, monitoring activation metrics during the rollout, and temporarily redirecting advertising spend toward higher-converting acquisition channels.

Importantly, these recommendations are grounded in evidence gathered during the investigation rather than generic best practices. Because the agent has already examined traffic sources, customer behavior, product usage, and historical performance, it can tailor its suggestions to the organization's specific circumstances instead of relying on generalized advice.

How Composio helps with recommendations

For example, an investigation into declining customer retention may reveal reduced product engagement in Amplitude, increased support requests in customer service software, lower expansion revenue in Snowflake, and weaker onboarding completion rates measured through Google Analytics. Because the autonomous analyst can combine evidence across these connected systems through Composio, its recommendations reflect a holistic understanding of the business.

For example, an investigation into declining customer retention may reveal reduced product engagement in Amplitude, increased support requests in customer service software, lower expansion revenue in Snowflake, and weaker onboarding completion rates measured through Google Analytics. Because the autonomous analyst can combine evidence across these connected systems through our platform, its recommendations reflect a holistic understanding of the business rather than isolated observations from individual dashboards.

Because the investigation drew on evidence from across the business, the recommendations can span departments: adjusting onboarding communications, modifying advertising targeting, updating customer success playbooks, and monitoring retention cohorts through the next release.

Example: Enterprise retention

After investigating a decline in enterprise customer retention, the autonomous analyst concludes that newly onboarded customers are adopting core product features at a lower rate than customers onboarded in earlier cohorts.

Based on this finding, it recommends:

1. Simplify the onboarding flow for enterprise accounts.

2. Introduce guided walkthroughs for underutilized features.

3. Alert customer success managers when onboarding milestones are missed.

4. Measure feature adoption weekly following the product update.

5. Reevaluate retention metrics after one complete customer lifecycle.

Each recommendation directly addresses evidence uncovered during the investigation rather than representing generic retention advice.

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Glossary

Autonomous analyst: An AI agent that combines the reasoning capabilities of a large language model with direct access to external tools, APIs, and databases, allowing it to plan investigations, retrieve data, and generate findings without step-by-step human instruction.

Tool calling: A mechanism that allows a large language model to invoke external functions or APIs during a conversation. Instead of generating a text response, the model emits a structured request that triggers a specific action, such as querying a database or retrieving a dashboard, and incorporates the result into its reasoning.

OAuth token refresh: The process of automatically replacing an expired short-lived access token with a new one using a long-lived refresh token. OAuth tokens from providers such as Google and Slack typically expire after 60 minutes; without managed refresh, agents running continuously will fail when tokens expire mid-workflow.

Schema: The structured definition of a dataset or API response, including field names, data types, and relationships between values. LLM-friendly schemas use clear, descriptive field names and consistent formatting so the model can interpret returned data without additional transformation.

Data warehouse: A centralized repository that stores large volumes of structured business data aggregated from multiple operational systems. Common examples include Snowflake, Google BigQuery, and ClickHouse. Autonomous analysts query data warehouses to retrieve historical metrics and cross-system records during an investigation.