What is agentic AI?

Agentic AI is an artificial intelligence system that can achieve a defined goal with limited human supervision. In other words, agentic AI does not just generate content. It plans, decides, and executes multi-step tasks.

Agentic AI systems are made up of AI agents, which are machine learning models designed to replicate aspects of human decision-making in order to solve problems dynamically.

In a multi-agent architecture, each agent is responsible for a defined subtask that contributes to the overall objective. An orchestration layer coordinates these agents, aligning their actions so they work together toward a shared goal.

These agents are models designed to:

• Interpret objectives
• Break goals into structured steps
• Retrieve relevant data
• Interact with tools and systems
• Execute actions in sequence
• Evaluate results

The term “agentic” refers to agency, meaning the capacity to act independently and purposefully.

If you are asking what agentic AI is, the key distinction is simple:

• Generative AI produces outputs.
• Agentic AI achieves goals.

What is the difference between agentic AI and generative AI?

Many people searching for what is agentic AI also want to understand how it differs from generative AI. Generative AI models, such as large language models, focus on creating text, images, or code based on learned patterns.

Agentic AI builds on generative AI but adds orchestration and action.

A generative AI system can:

• Draft a clinical summary
• Translate regulatory content
• Summarize a study protocol

An agentic AI system can:

• Retrieve the protocol and statistical analysis plan
• Cross-check endpoints against objectives
• Extract structured datasets
• Apply compliance rules
• Draft documentation
• Flag inconsistencies
• Prepare a review-ready output

Generative AI answers. Agentic AI executes.

How does agentic AI work?

Although implementations vary, most agentic AI systems follow a structured lifecycle. In this article, we break down how AI agents work at Narrativa and walk you through the five steps in detail.

1. Goal definition
2. Context retrieval
3. Reasoning and task planning
4. Tool orchestration
5. Human-in-the-loop validation

What are the advantages of agentic AI?

Agentic AI introduces capabilities that standalone generative models cannot provide.

Autonomous

Agentic AI systems can manage multi-step workflows without constant human intervention. They maintain long-term objectives and track progress.

Goal-driven

They optimize for outcomes rather than isolated responses. This is critical in compliance-heavy environments.

Tool-enabled

Agentic AI can interact with enterprise systems. It can query databases, call APIs, and retrieve structured data.

Specialized

Different agents can specialize in specific tasks such as data extraction, compliance validation, drafting, or quality control.

Adaptable

With feedback loops and guardrails, agentic AI systems can refine performance over time.

Agentic AI use cases in pharma

The pharmaceutical industry provides a strong use case for agentic AI because workflows are structured, regulated, and multi-system.

Clinical development

• Automated drafting of Clinical Study Report sections
• Cross-checking endpoints against protocols
• Ensuring alignment with statistical analysis plans
• Validating consistency across tables and listings

Regulatory affairs

• Retrieval of prior submissions
• Comparison against regulatory guidelines
• Structured dossier preparation
• Identification of missing or inconsistent data

Pharmacovigilance

• Monitoring safety data across multiple systems
• Supporting aggregate reporting
• Detecting signal inconsistencies

Manufacturing and quality

• Deviation analysis
• SOP cross-referencing
• Structured investigation support

In these scenarios, agentic AI coordinates processes rather than generating isolated text.

Agentic AI in regulated pharma environments

At Narrativa, agentic AI is designed specifically for regulated life sciences environments.

This means:

• Structured goal definition
• Controlled tool orchestration
• Compliance-aware logic
• Full traceability
• Human-in-the-loop validation

Rather than deploying generic AI agents, Narrativa builds agentic AI solutions aligned with clinical, regulatory, and operational workflows.

In pharma, autonomy without compliance is not acceptable. Agentic AI must operate within scientific rigor and regulatory guardrails.

Agents are not only going to change how everyone interacts with computers. They’re also going to upend the software industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons.

Bill Gates, Microsoft co-founder.

Agentic AI FAQs

What does agentic AI mean?

Agentic AI refers to artificial intelligence systems that can act independently to achieve specific goals.
Instead of only generating responses, agentic AI plans and executes multi-step tasks using connected tools
and data sources.

How is agentic AI different from generative AI?

Generative AI creates content such as text, images, or code. Agentic AI goes further by using AI models to
plan actions, retrieve data, apply rules, and complete structured workflows autonomously.

Is agentic AI autonomous?

Yes, agentic AI systems are autonomous within defined guardrails. They can manage complex tasks with limited
human supervision, but in regulated industries they typically operate with human validation and oversight.

Where is agentic AI used?

Agentic AI is used in industries that require structured workflows and compliance. In pharma, it supports
clinical documentation, regulatory submissions, pharmacovigilance, and quality processes.

Why is agentic AI important in regulated industries?

Regulated industries require traceability, validation, and coordination across systems. Agentic AI combines
reasoning, orchestration, and tool integration to support compliant, auditable workflows.