Agentic AI: What's needed from your data?

According to the IBM Institute for Business Value (IBV), 70% of executives say agentic AI is critical to their future, and 61% of CEOs report they’re actively deploying or scaling AI agents in their organizations.

Agentic AI is no longer an option. It’s a strategic imperative for future-proofing your business.

In this article, we’ll break down:

• What agentic AI actually is (and isn’t)
• Where it’s gaining traction today
• What data conditions must be true for it to work
• How dbt helps teams build the trusted, structured foundation agentic systems require

Key features of agentic AI

Agentic AI refers to systems that can act independently to achieve goals with minimal human input. These agents, often built on large language models (LLMs), simulate human-like decision-making, enabling them to reason, plan, and execute tasks in real time.

Unlike traditional AI models, agentic AI exhibits goal-driven behavior, autonomy, and adaptability. “Agentic” implies not just intelligence, but agency — the ability to assess, decide, and act based on evolving inputs, often without direct instruction.

Some key features of agentic AI include:

• Autonomy
• Reasoning/decision making
• Autonomous and continuous learning
• Multi-agent collaboration

Autonomy

Autonomy sets agentic AI apart from previous iterations of AI applications. While GenAI-powered chatbots can generate responses and even make recommendations (or reservations), they don’t include enough data or checks and balances to trust them to make important decisions in lieu of humans.

In contrast, AI agents go beyond conversation. They autonomously execute multi-step workflows, call APIs, access external systems and data, and dynamically adapt to evolving inputs without human intervention.

Think less chatbot, more digital teammate.

Reasoning/decision making

AI agents also reason autonomously, without human intervention. They don’t just execute tasks, they make decisions, learn from outcomes, reason across time horizons, and can collaborate with other AI agents.

Behind the agent is an LLM acting as a reasoning engine, orchestrating tasks, generating solutions, and coordinating models for specialized functions like content creation, recommendations, and visual processing. Techniques like retrieval-augmented generation (RAG) boost performance by injecting up-to-date knowledge and domain-specific data into the reasoning loop, increasing accuracy and reducing hallucination risk.

Autonomous and continuous learning

Agentic AI doesn’t just follow instructions — it observes, adapts, and improves based on real-world interactions. While traditional and GenAI systems improve through incremental learning — where the model is updated with new data to make better predictions and improve performance — only agentic AI agents engage in lifelong learning.

For example: An agentic customer support assistant could analyze customer interactions and begin adjusting product recommendations automatically based on patterns it identifies — all without manual updates from your team.

Multi-agent collaboration

In complex environments, agentic systems scale through multi-agent collaboration. Each agent specializes in a specific function — say, financial forecasting or marketing ops — and communicates with others through an orchestration layer to tackle broader goals.

This modular approach supports scalability and fault tolerance: agents can be added or swapped out without rearchitecting the entire system.

The state of agentic AI

Agentic AI isn’t just theoretical — it’s here, scaling fast. Valued at $5 billion today, the agentic AI market is projected to hit $50 billion by 2030.

Enterprises are already embedding these systems into core business functions to automate decisions, streamline operations, and unlock new efficiencies. According to Microsoft’s Work Trend Index, executives cite customer service, marketing, and product development as top areas for near-term AI investment — with HR, finance, and sales close behind.

And the ecosystem is maturing. Several major platforms now offer the tools to build and deploy AI agents that reason, act, and collaborate across systems.

Current applications for agentic AI

Here are just a few of the areas where companies are already using agentic AI:

• Customer Service: Resolving tickets, updating accounts, and escalating issues autonomously (e.g., Klarna’s AI agent handles two-thirds of support chats).
• Sales: Prospecting, personalizing outreach, qualifying leads, and scheduling meetings.
• Marketing: Orchestrating campaigns, optimizing send times, and generating content.
• HR: Automating onboarding, answering policy questions, and managing internal mobility.
• Finance: Auditing expenses, detecting fraud, and generating forecasts.

Key agentic AI platforms

Agentic AI platforms are rapidly evolving, giving businesses the tools to build autonomous agents that reason, act, and collaborate across workflows. Here’s how leading players are enabling this shift:

• Salesforce Agentforce embeds customizable, decision-making agents directly into Salesforce workflows. These agents can reason, take multi-step actions, and integrate with tools like Slack and Data Cloud.
• Microsoft Azure AI Foundry supports multi-agent orchestration using modular tools like Azure Functions and the Model Router. Paired with Copilot Studio, businesses can build and deploy agents for multi-agent collaboration across Microsoft 365 apps using natural language.
• OpenAI GPTs allow companies to create tailored agents that reason, act, and integrate with APIs—all without writing code. The Agents SDK and Responses API enable developers to build multi-agent workflows with memory, tool use, and real-time decision-making.
• IBM watsonx Orchestrate powers agentic systems using Granite models optimized for enterprise use. IBM’s platform supports prebuilt and custom agents that automate tasks across HR, finance, and IT, with orchestration across Salesforce, Microsoft, and other enterprise tools.

But here’s the catch: No matter how advanced your platform is, agents are only as smart as the data they use. To reason effectively, adapt to real-world inputs, and deliver value, agentic AI systems need access to clean, contextual, and trustworthy data.

That’s where your enterprise data becomes the catalyst for truly intelligent behavior.

What does agentic AI need from your data?

Low-code and no-code platforms make it easier than ever to build and deploy agentic AI. But here’s the real constraint: agents are only as good as the data they have access to.

Before an AI agent can reason, act, or collaborate, it needs something to reason with. And that something isn’t just “data” — it’s governed, structured, contextualized, and accessible data. Without this foundation, even the most advanced AI agents will produce inconsistent or inaccurate results.

So what, exactly, does agentic AI need from your data? We believe the five foundational requirements are:

• Strong data governance
• Structured, transformed data
• Semantic clarity and consistency
• Governed, accessible interfaces
• Feedback loops and observability

Strong data governance

Bad data leads to bad decisions—only now, they happen faster.

Agents rely on governed, high-integrity data to reason accurately and comply with internal policies and external regulations. Governance ensures the right people (or agents) have the right access to the right data, and that access is monitored, auditable, and secure.

It’s not just about compliance — it’s about ensuring trust in every autonomous decision.

Structured, transformed data

Raw data ≠ ready data. Before agents can reason over data, it must be modeled, tested, and contextualized.

That’s where dbt shines. dbt transforms messy source data into structured, analytics-ready models with built-in testing, lineage, and semantic meaning —exactly what AI agents need to work reliably.

Semantic clarity and consistency

AI agents can’t make smart decisions if key metrics mean different things across your org.

Terms like “revenue,” “churn,” or “active user” must be precisely defined — and consistently applied. A shared semantic layer, whether provided through a data governance tool, data platform, or like the one dbt provides, ensures agents reason over consistent definitions, avoiding metric drift across departments.

Governed, accessible AI interfaces

Agents access data via APIs, semantic layers, or metadata endpoints — but access should never mean exposure.

Platforms like Snowflake, Databricks, and Salesforce offer native access controls. With dbt’s new Model Context Protocol (MCP) Server, AI agents can securely retrieve models, lineage, and semantic context from your dbt project — enabling fine-grained, policy-aware access to production-grade data.

Validation and observability

Autonomous agents improve through continuous interaction — but without monitoring and feedback, they can easily go off the rails.

Observability ensures that you know what the agent did, why it did it, and what happened next. Techniques like testing, logging, alerting, and human-in-the-loop review are essential for catching hallucinations, avoiding bias drift, and managing error propagation in multi-agent systems.

Feedback loops don’t just protect you — they make your agents smarter over time.

How dbt can help accelerate your agentic AI

Agentic AI systems rely on structured, trustworthy, and context-rich data to reason and act effectively. dbt plays a critical role in ensuring your data is AI ready. dbt’s powerful capabilities can help you accelerate time-to-value of your agentic AI initiatives further in all five of the above areas.

Supporting strong data governance

dbt encodes governance directly into the transformation layer. By combining modular SQL modeling with version control, testing, and documentation, dbt projects become transparent, auditable systems of record.

dbt data lineage provides a holistic view of how data moves through an organization by mapping dependencies between different assets, such as models, sources, and tests. dbt also integrates with platforms like Atlan, Alation, and Secoda to surface lineage, ownership, and policies.

Creating structured, transformed data

dbt transforms messy inputs into clean, analytics-ready models—with built-in documentation, testing, and lineage—giving agents the structure they need to reason clearly and reliably. dbt offers cloud-native scalability with cloud services like Snowflake, BigQuery, and Databricks to enable dynamic scaling, ensuring AI models receive optimized, high-performance data.

Building semantic clarity and consistency

The dbt Semantic Layer defines shared business concepts like “active customer” or “net revenue” in a governed, reusable format. This ensures agents across teams apply the same logic, avoiding metric drift and conflicting outputs.

Creating governed interfaces for multi-agent orchestration

With the advent of multi-agent agentic collaboration, the need for well-governed interfaces between your data and AI systems has become even more critical. The dbt MCP Server enables this by exposing dbt models, lineage, and semantic context via the open Model Context Protocol (MCP).

With MCP Server, instead of relying on fragile integrations, agents can query dbt directly in a standardized, governed format. Paired with orchestration platforms like Kestra, agents can trigger dbt runs alongside model training, validation, and reporting tasks to enable fully autonomous, governed workflows.

Enabling validation and observability

While AI agents may be autonomous, they still need supervision. dbt’s native testing capabilities evaluate the validity of AI responses before agents act on them.

dbt’s built-in tests and logging capabilities catch anomalies, validate assumptions, and support human-in-the-loop review—creating a feedback loop that helps agents learn and improve safely over time. dbt also supports structured evaluation workflows when paired with platforms like Snowflake Cortex AI, enabling teams to compare AI responses against ground truth and trigger alerts when accuracy drops below defined thresholds.

Conclusion

Agentic AI is moving fast — and so is the demand for high-quality, trusted data to power it. While the long-term potential of AI agents is still unfolding, businesses are already seeing real results.

dbt has long been the industry standard for building reliable, well-governed datasets. With its support for transformation, validation, lineage, and AI-ready interfaces, dbt is a natural fit for companies looking to scale AI adoption with confidence.

Ready to future-proof your AI strategy? Book a demo to see how dbt can help.