Intelligent Agents: When Data Turns into Action
As companies face data overload, shorter decision cycles, and the massive integration of generative AI into business processes, a new requirement emerges: moving from analysis to frictionless action.
But how can we go beyond dashboards to automate decisions? What are the key technologies behind intelligent agents? And most importantly, what concrete business benefits can this data-driven automation deliver?
We asked Nabil Ben Hassine, Head of Cloud Data Solutions at VISEO, to share his expert insights.
From Insight to Action: The Missing Link
Most organizations today are equipped to analyze data—dashboards, KPIs, data visualizations abound. Yet in 80% of cases, action remains manual, slow, contextual, and ad hoc.
This creates growing tension: teams are overwhelmed by information but struggle to turn insights into concrete, measurable, and automatable decisions. Meanwhile, customer expectations accelerate, business events multiply, and responsiveness becomes a competitive edge.
The Data-Driven Agent: An Autonomous Player in Your Information System
To overcome this gap, a new kind of software is emerging: the data-driven agent.
Not just a script, chatbot, or advanced dashboard, this is an autonomous entity that perceives, reasons, and acts within an information system. It leverages available data, understands context, and makes decisions aligned with defined business goals.
Use cases include:
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Automatically responding to negative customer feedback in a support ticket
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Correcting inconsistencies in stock data across a supply chain
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Launching a personalized reassurance campaign after a delivery issue
But for this to work, two technological pillars are essential:
Pillar 1: Graph Databases to Model Business Relationships
Agents cannot act effectively without understanding how elements relate to one another. This is where graph databases come in.
Unlike relational databases, which store data in rows and columns, graph databases use nodes and edges to represent relationships—for instance, between a customer, their orders, support interactions, related products, and marketing campaigns.
This modeling enables agents to:
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Detect root causes
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Anticipate ripple effects
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Identify risk patterns or high-potential opportunities
Pillar 2: Generative AI to Understand Unstructured Data
Understanding relationships is not enough. A capable agent must also comprehend human language—emails, comments, reports, or support tickets.
Generative AI applied to semantic search helps agents to:
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Grasp message intent
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Extract key insights from documents
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Link textual content to known business entities (products, issues, customers, etc.)
A Practical Use Case: End-to-End Automation
Let’s take a typical e-commerce or retail scenario:
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A customer leaves a negative review about a product.
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The agent uses semantic analysis to identify a known defect.
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It cross-references other similar complaints, pinpoints the affected product batch and supplier (via the graph database), and automatically triggers:
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A personalized discount for the customer
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A ticket to the quality assurance team
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A temporary suspension of the product’s sale
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No human intervention. Response time: seconds.
Data-Driven Decision Automation: More Than a Buzzword
This is no longer science fiction or the exclusive domain of big tech. With the right tools and architecture, companies can automate operational-level decisions today—and scale up to more complex ones.
This requires:
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Strong data governance
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The ability to model complex relationships
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Alignment between IT, business, and AI teams
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A use case-driven, test-and-learn approach
Why Now?
Three factors make this shift possible in 2025:
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Mature graph database technologies, now easily integrable into standard IT systems
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Widespread generative AI models capable of understanding and generating business language
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Business pressure to reduce time and cost, driving automation beyond the IT department
A Paradigm Shift
Data-driven automation changes the game. It allows not only faster reactions, but also the ability to scale business intelligence by embedding agents that can understand, decide, and act in complex environments.
Those who structure their data, secure their models, and identify the right use cases will lead the race. In a world where responsiveness is king, data is no longer just for informing—it’s for acting.