From Generative AI to Agentic AI: What Is the Next Stage of AI?

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General

Discussion Content

Artificial intelligence is moving beyond systems that simply generate text, images, or code. A growing area of interest is agentic AI, where AI systems can plan tasks, use tools, make decisions, and complete multi-step workflows with limited human intervention.

This shift raises an important question: Are AI agents becoming the next major stage of practical AI development?

Generative AI vs. Agentic AI

Generative AI primarily responds to user prompts by producing content. For example, a large language model can generate a report, summarize a document, write code, or answer a question.

Agentic AI builds on these capabilities by adding elements such as:

• Goal-oriented task execution
• Planning and reasoning
• Tool and API usage
• Memory or contextual information
• Multi-step workflows
• Feedback and evaluation
• Human approval when required

A simplified workflow might look like:

User Goal → Planning → Tool Selection → Action → Evaluation → Result

Where Could Agentic AI Be Useful?

The potential applications extend across many industries.

Finance

AI agents could assist with financial reporting, variance analysis, forecasting, reconciliation workflows, and management reporting. Human review would still be important for high-impact financial decisions.

Business Intelligence

An AI system could interpret a business question, identify relevant data, perform an analysis, and help create an explanation of the results.

For example:

"Why did sales decrease in the Middle East region this quarter?"

Instead of simply generating a written answer, an agentic system could potentially retrieve relevant data, perform calculations, identify major changes, and present the findings.

Healthcare

AI agents could potentially support administrative workflows, information retrieval, documentation, and research assistance. However, healthcare applications require strong privacy, validation, safety, and human oversight.

Cybersecurity

Agentic systems may help investigate alerts, correlate information from different sources, and recommend possible responses. Security teams would need appropriate controls before allowing automated actions.

Software Development

AI agents are already influencing software development by helping developers explore codebases, generate code, write tests, investigate errors, and automate parts of development workflows.

What Are the Technical Challenges?

Agentic AI is not simply about giving an LLM more autonomy. Several technical problems need to be addressed.

1. Reliability

An agent can make incorrect assumptions or select an inappropriate action. Systems therefore need validation and evaluation mechanisms.

2. Security

Giving an AI system access to APIs, databases, files, or other tools introduces additional security risks.

3. Observability

Developers need to understand what an agent did, which tools it used, and why a particular result was produced.

4. Cost and Performance

Multi-step reasoning and repeated model calls can increase latency and operational costs.

5. Human Oversight

For high-impact tasks, completely autonomous decision-making may not be appropriate. Human approval can remain an important part of the workflow.

A Possible Architecture

A basic agentic application could contain several components:

1. User
2. AI Model
3. Planner / Reasoning Layer
4. Tool Selection
5. APIs / Database / Search / Code
6. Result Evaluation
7. Final Response

The exact architecture will depend on the application, model, tools, security requirements, and level of autonomy.

How Does This Affect Developers?

Developers working with AI may increasingly need skills beyond prompt engineering.

For example, understanding Python, APIs, data handling, machine learning, LLM application development, evaluation, and security can be valuable when building practical AI systems. These skills can also form part of a broader path toward AI engineering, where understanding how AI models are developed and deployed becomes increasingly important. A useful AI Engineer career guide provides additional context on these skills and career pathways. AI Engineer career guide

Useful areas include:

• Python or another programming language
• APIs and web services
• Data handling
• Database fundamentals
• LLM application development
• Retrieval-augmented generation (RAG)
• Evaluation and testing
• Authentication and security
• Observability
• Workflow orchestration

The important shift is from simply asking an AI model a question toward building reliable systems around AI models.

Discussion

I'm interested in how developers and data professionals see this transition.

What do you think is the most important technical challenge for agentic AI: reliability, security, evaluation, cost, or something else?

Also, for those already experimenting with AI agents:

What tools, frameworks, or architectures have you found useful for building practical agentic AI applications?