Understanding the Overlap Between Agentic and Generative AI

Read full article from Descope here:  https://www.descope.com/blog/post/agentic-vs-generative-ai/?utm_source=nhimg

Artificial intelligence isn’t one technology — it’s a continuum of capabilities. On one end, we have Generative AI, focused on creating content. On the other, Agentic AI, which acts, plans, and executes tasks.

As organizations rush to embed AI into workflows, understanding how these two paradigms differ and intersect is critical. Generative AI builds; agentic AI executes. Together, they define how machines will think, act, and collaborate with humans in the next decade.

In this article, we’ll explore the differences between agentic and generative AI, how they overlap, and why securing their autonomy has become one of the most urgent challenges in enterprise technology.

What Is Generative AI?

Generative AI (GenAI) refers to systems that produce original content — from text and code to images, video, and music — by learning from large datasets and predicting the most probable outputs.

These models, such as OpenAI’s ChatGPT, Anthropic’s Claude, Midjourney, or Sora, use transformer-based architectures to recognize linguistic or visual patterns and generate novel responses.

Key Capabilities of Generative AI

• Creation-focused: Produces outputs such as documents, designs, summaries, or source code.
• Prompt-driven: Operates through direct user instructions (prompts).
• Pattern-based learning: Uses probability to predict the next most likely token or pixel.
• Iterative improvement: Refines outputs via fine-tuning, reinforcement learning, or retrieval augmentation.

Adoption and Growth

The rise of ChatGPT in late 2022 marked a cultural and technological inflection point. Within just two months of launch, it reached 100 million users, the fastest adoption curve for any consumer app in history.

According to a 2025 AP-NORC poll, 65% of Americans have used a generative AI tool, with 74% of adults under 30 incorporating it into daily tasks. From workplace productivity to personal creativity, generative AI has rapidly become mainstream.

Common Use Cases

• Text and content generation
• Data summarization and coding assistance
• Media creation (images, video, design)
• Chatbots and digital assistants
• Knowledge synthesis for research and analysis

Generative AI empowers creativity — but it stops short of true autonomy.

What Is Agentic AI?

Agentic AI builds on generative AI but adds the ability to act. While generative models produce outputs, agentic models use those outputs to make decisions, plan tasks, and interact with tools or systems.

In other words, agentic AI extends generative AI from “creation” to “execution.”

How It Works

Under the hood, agentic AI uses the same large language models as generative systems. The difference lies in orchestration:

• Planning is generation. The model generates step-by-step instructions.
• Decision-making is generation. The model predicts the best next action.
• Execution is generation. The model outputs structured data that triggers tool or API calls.

Agentic frameworks like AutoGPT, LangChain, and CrewAI orchestrate these behaviors through tool use, memory, and state management. Meanwhile, enterprise ecosystems such as Google Vertex AI Agents, IBM watsonx Orchestrate, and Microsoft Copilot are embedding agentic capabilities into productivity and automation platforms.

Current Maturity

Despite rapid innovation, agentic AI is still early-stage. Multi-step reasoning often breaks after a few iterations, and complex workflows introduce reliability issues.
Most production deployments today remain narrowly scoped, supporting activities like:

• Automated financial reporting
• Customer email triage
• Code documentation
• Research summarization

The potential for “autonomous digital workforces” is real, but the technology isn’t yet robust enough for unsupervised operations.

Agentic vs. Generative AI: The Relationship Explained

Rather than competitors, agentic and generative AI are complementary layers of the same system.

In short, generative AI is the creative core, while agentic AI is the operational shell that gives it hands and feet.

Emerging protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication are defining how these systems securely connect, share context, and collaborate.

Challenges and Security Considerations

1. Generative AI Risks

• Hallucinations and inaccuracies: Outputs can invent false information.
• Copyright and IP concerns: Models trained on unlicensed data raise ownership issues.
• Data leakage: Prompt data can inadvertently expose sensitive information.

2. Agentic AI Risks

• Cascading failures: A single hallucination can trigger multiple faulty actions.
• Overreach: Poorly scoped agents may execute unintended or noncompliant actions.
• Low reliability: Many agents fail in multi-step reasoning (up to 70% failure in some benchmarks).

3. Shared Risks: Security and Governance

Both AI types introduce new identity, authentication, and compliance challenges.
As agents gain the ability to autonomously access systems, execute API calls, or exchange data, Identity and Access Management (IAM) becomes mission-critical.

Organizations must treat AI agents like human users — provisioning, monitoring, and deprovisioning their access. Adaptive IAM ensures that only trusted and verified agents can act within enterprise systems.

The Security Imperative: Governing AI Access

With agentic AI, every agent is effectively a non-human identity (NHI) — capable of action, authentication, and potential misuse.

Protecting these entities demands a new security model that includes:

• Token-based authentication and rotation for AI agents
• Fine-grained policy controls for tool and API access
• Continuous monitoring of agent activity and decision chains
• Zero Trust enforcement to prevent unauthorized or unintended actions

Descope leads this frontier by providing Identity and Access Management (IAM) for AI agents and MCP servers
It allows developers to:

• Turn AI applications into secure OAuth providers
• Connect agents to external tools safely
• Enforce policy-based access control with full traceability
• Maintain seamless authentication for both humans and agents

By embedding IAM directly into the agentic architecture, organizations can balance autonomy with assurance — enabling innovation without compromising control.

Conclusion: Toward the Age of Autonomous Collaboration

The convergence of generative and agentic AI signals the next major evolution in digital systems.
Generative AI gives machines the power to create. Agentic AI gives them the ability to act.

Together, they are redefining productivity, creativity, and enterprise operations — but they also demand a new era of identity governance and AI trust management.

The future won’t be about AI that simply generates answers; it will be about AI that takes initiative — responsibly, securely, and under measurable control.