What Is Agentic AI?

Agentic AI refers to artificial intelligence systems that can autonomously plan, decide, and act to achieve a goal — without requiring step-by-step human instructions. Unlike traditional AI that responds to a single prompt with a single answer, an agentic system breaks down complex tasks, uses tools, adapts to feedback, and orchestrates multi-step workflows on its own.

The shift from "AI that answers" to "AI that acts" is the defining transition in enterprise AI adoption in 2025–2026. Chatbots answer questions. Agents do work — they query databases, update records, generate reports, trigger workflows, and escalate when they encounter something outside their scope. This makes data governance not just important for agentic AI, but a prerequisite: agents that act on ungoverned data can cause damage at machine speed.

What Makes AI Agentic

Not every AI system that uses a large language model (LLM) is agentic. The distinction lies in the degree of autonomy the system has in deciding what to do, how to do it, and when to stop.

A traditional LLM interaction is stateless: the user sends a prompt, the model generates a response, the interaction ends. An agentic system adds three capabilities on top of the language model:

• Planning — the agent decomposes a high-level goal into subtasks and decides the order to execute them. It doesn't just respond to instructions — it generates its own plan of action.
• Tool use — the agent can invoke external tools: databases, APIs, code interpreters, web search, file systems. This grounds the agent in real data and gives it the ability to take actions beyond text generation.
• Autonomous iteration — the agent evaluates the results of its actions, decides whether the goal has been met, and adjusts its approach if not. This reasoning loop continues until the task is complete or the agent determines it needs human input.

The combination of these three capabilities is what transforms a language model from a sophisticated autocomplete into a system that can independently accomplish work.

How AI Agents Work

At a fundamental level, an AI agent operates through a reasoning-action loop — sometimes called the "observe-plan-act-evaluate" cycle. The agent receives a goal, observes the current state of the world (through tool calls or provided context), plans the next step, executes it, evaluates the result, and repeats until the goal is achieved.

The LLM as the Reasoning Engine

The large language model at the core of an agent serves as the reasoning and planning engine. It interprets the goal, decides which tools to use, generates the parameters for tool calls, and evaluates results. The LLM doesn't "think" in the human sense — it generates a plan by predicting the most likely useful sequence of actions based on its training data and the current context.

Modern agentic frameworks (LangGraph, CrewAI, Anthropic's agent SDK) structure this as a state machine: each step produces an observation that updates the agent's context, and the LLM decides the next action based on the accumulated state. This allows the agent to adapt mid-execution — if a database query returns unexpected results, the agent can reformulate its approach rather than failing.

Tool Integration: MCP and Function Calling

Tools are what give agents their capabilities. Without tools, an agent is just an LLM generating text. With tools, it can query databases, call APIs, read documents, execute code, and update systems.

Two primary mechanisms enable tool integration:

• Function calling — the LLM generates a structured JSON call to a predefined function, which the application executes and returns results to the agent. This is the foundational mechanism supported by all major LLM providers (OpenAI, Anthropic, Google).
• Model Context Protocol (MCP) — an open standard that provides a universal interface between AI agents and data sources. Instead of building custom integrations for each tool, MCP defines a standard protocol for tool discovery, invocation, and resource access. This is becoming the preferred approach for enterprise tool integration because it separates the agent logic from the tool implementation.

The choice of tools available to an agent directly determines what it can do — and what it can access. This is where data governance becomes critical: an agent with access to ungoverned data can produce confidently wrong results, or worse, take autonomous actions based on incorrect information.

Memory and Context

Agentic systems need to maintain state across the reasoning loop. This happens at multiple levels:

• Short-term memory — the conversation context and accumulated observations within a single task execution. This is typically the LLM's context window, augmented by the tool results collected during the session.
• Long-term memory — persistent storage that allows the agent to recall information from previous interactions. Implemented through vector databases, knowledge graphs, or structured storage systems.
• Shared memory — in multi-agent systems, a common workspace where agents deposit and retrieve information for coordinated work.

Agentic Patterns

Not all agentic systems look the same. The level of autonomy and complexity varies significantly based on the use case. These patterns represent increasing levels of agentic behavior.

Single-Agent Task Execution

The simplest agentic pattern: one agent, one goal, a set of tools. The agent receives a task (e.g., "analyze last quarter's sales data and summarize trends"), plans the steps, executes tool calls (query database, process data, generate summary), and returns the result. This pattern is suitable for well-defined tasks with clear completion criteria.

Tool-Augmented Reasoning

The agent uses tools not just to take actions, but to enhance its reasoning process. For example, an agent tasked with answering a complex question might first search a knowledge base, then query a database for supporting data, then cross-reference the results before generating an answer. Each tool call informs the next reasoning step. This pattern is common in retrieval-augmented generation (RAG) systems.

Multi-Agent Orchestration

Complex workflows are decomposed across multiple specialized agents, each with their own tools and expertise. A coordinator agent (or an orchestration framework) manages the workflow: assigning subtasks, collecting results, handling dependencies, and synthesizing the final output. For example, a data quality review might involve a profiling agent, a rules-checking agent, and a reporting agent working in sequence.

Autonomous Workflows

The most advanced pattern: agents that operate continuously, monitoring conditions and taking actions when triggers are met. An autonomous data stewardship agent might monitor data quality metrics, flag anomalies, generate incident reports, and notify the responsible data steward — all without human initiation. This pattern requires the strongest governance guardrails because the agent operates without per-action human approval.

Data Governance for Agentic AI

Agentic AI doesn't just consume data — it acts on data autonomously. This changes the governance equation fundamentally. When a human analyst queries a database, they apply judgment about data quality, relevance, and context. When an agent queries the same database, it lacks that institutional knowledge unless governance infrastructure provides it.

Why Agents Need Governed Data

An agent making autonomous decisions based on data is only as trustworthy as the data it accesses. Three governance capabilities become critical:

• Data quality signals — agents need machine-readable indicators of data quality (freshness, completeness, accuracy scores) to decide whether a dataset is reliable enough for the task at hand. Without quality metadata, agents treat all data as equally trustworthy.
• Business context — an agent querying a field called margin needs to know whether it means gross margin, net margin, or contribution margin. Business glossary terms and semantic metadata provide the context that prevents agents from making confidently wrong interpretations.
• Access governance — agents should only access data they are authorized to use for their specific purpose. Role-based access controls, data classification, and purpose-bound access policies prevent agents from reading sensitive data they don't need.

The EU AI Act and Agentic Systems

The EU AI Act establishes risk-based requirements for AI systems that directly impact agentic deployments. High-risk AI systems (which include many enterprise decision-making applications) must demonstrate data governance, transparency, human oversight, and accuracy. For agentic systems specifically:

• Article 10 requires that training and operational data meet quality criteria — a requirement that depends entirely on data governance infrastructure.
• Article 14 mandates human oversight mechanisms — the human-in-the-loop checkpoints that agentic systems must implement for high-stakes decisions.
• Article 13 requires transparency — agents must be able to explain their reasoning and data sources, which depends on data lineage and audit trails.

How Dawiso Enables Agentic AI

Dawiso provides the data governance infrastructure that agentic AI systems depend on for trustworthy operation. Through its MCP Server, AI agents can access Dawiso's catalog, glossary, and metadata programmatically — using the same Model Context Protocol standard that the broader AI ecosystem is converging on.

What this means in practice: an AI agent tasked with analyzing business data can first search Dawiso's catalog to find relevant datasets, check their quality scores and freshness, look up business glossary definitions for field-level context, and trace data lineage to understand where data comes from — all through standard MCP tool calls, before it ever queries the actual data source.

The Business Glossary gives agents the semantic layer they need to interpret data correctly. The Data Catalog provides discovery and quality signals. Interactive Data Lineage enables agents to trace data provenance — critical for both trustworthiness and regulatory compliance. And AI-powered features help governance teams keep metadata current as the data landscape evolves, ensuring that agents always have access to up-to-date context.

Conclusion

Agentic AI represents a fundamental shift from AI that answers questions to AI that autonomously accomplishes work. The reasoning loop — observe, plan, act, evaluate — combined with tool integration gives agents capabilities that go far beyond text generation. But this autonomy creates a critical dependency on data governance: agents that act on ungoverned data can cause harm at scale.

For organizations adopting agentic AI, the message is clear: invest in data governance before you invest in agent capabilities. A well-governed data landscape — with cataloged assets, quality metrics, business glossary terms, and proper access controls — is the foundation that makes agentic AI trustworthy. Without it, you're giving autonomous systems the keys to a building where nobody labeled the rooms.