Image by Author | Ideogram
We’ve all spent the last couple of years or so building applications with large language models. From chatbots that actually understand context to code generation tools that don’t just autocomplete but build something useful, we’ve all seen the progress.
Now, as agentic AI is becoming mainstream, you’re likely hearing familiar refrains: “It’s just hype,” “LLMs with extra steps,” “marketing fluff for venture capital.” While healthy skepticism is warranted —as it should be with any emerging technology— dismissing agentic AI as mere hype overlooks its practical benefits and potential.
Agentic AI isn’t just the next shiny thing in our perpetual cycle of tech trends. And in this article, we’ll see why.
What Exactly Is Agentic AI?
Let’s start with trying to understand what agentic AI is.
Agentic AI refers to systems that can autonomously pursue goals, make decisions, and take actions to achieve objectives — often across multiple steps and interactions. Unlike traditional LLMs that respond to individual prompts, agentic systems maintain context across extended workflows, plan sequences of actions, and adapt their approach based on results.
Think of the difference between asking an LLM “What’s the weather like?” versus an agentic system that can check multiple weather services, analyze your calendar for outdoor meetings, suggest rescheduling if severe weather is expected, and actually send those calendar updates with your approval.
The key characteristics that separate agentic AI from standard LLM applications include:
Autonomous goal pursuit: These systems can break down complex objectives into actionable steps and execute them independently. Rather than requiring constant human prompting, they maintain focus on long-term goals.
Multi-step reasoning and planning: Agentic systems can think several moves ahead, considering the consequences of actions and adjusting strategies based on intermediate results.
Tool integration and environment interaction: They can work with APIs, databases, file systems, and other external resources as extensions of their capabilities.
Persistent context and memory: Unlike stateless LLM interactions, agentic systems maintain awareness across extended sessions, learning from previous interactions and building on past work.
From Simple Prompts to Agentic AI Systems
My journey (and perhaps, yours, too) with LLMs began with the classic use cases we all remember: text generation, summarization, and basic question-answering. The early applications were impressive but limited. You’d craft a prompt, get a response, and start over. Each interaction was isolated, requiring careful prompt engineering to maintain any sense of continuity.
The breakthrough came when we started experimenting with multi-turn conversations and function calling. Suddenly, LLMs could not just generate text but interact with external systems. This was our first experience with something more sophisticated than pattern matching and text completion.
But even these enhanced LLMs had limitations. They were:
- Reactive rather than proactive,
- Dependent on human guidance for complex tasks, and
- Struggled with multi-step workflows that required maintaining state across interactions.
Agentic AI systems address these limitations head-on. Recently, you’ve likely seen implementations of agents that can manage entire software development workflows — from initial requirements gathering through getting scripts ready for deployment.
Understanding the Agentic AI Architecture
The technical architecture of agentic AI systems reveals why they’re fundamentally different from traditional LLM applications. While a standard LLM application follows a simple request-response pattern, agentic systems implement sophisticated control loops that enable autonomous behavior.
Standard LLM Apps vs.Agentic AI Systems | Image by Author | draw.io (diagrams.net)
At the core is what we can call the “perceive-plan-act” cycle. The agent continuously perceives its environment through various inputs (user requests, system states, external data), plans appropriate actions based on its goals and current context, and then acts by executing those plans through tool usage or direct interaction.
The planning component is particularly important. Modern agentic systems employ techniques like tree-of-thought reasoning, where they explore multiple possible action sequences before committing to a path. This allows them to make more informed decisions and recover from errors more gracefully.
Memory and context management represent another architectural leap. While traditional LLMs are essentially stateless, agentic systems maintain both short-term working memory for immediate tasks and long-term memory for learning from past interactions. This persistent state enables them to build on previous work and provide increasingly personalized assistance.
Tool integration has evolved beyond simple function calling to sophisticated orchestration of multiple services.
Real-World Agentic AI Applications That Actually Work
The proof of any technology lies in its practical applications. In my experience, agentic AI works great when you require sustained attention, multi-step execution, and adaptive problem-solving.
Customer support automation has evolved beyond simple chatbots to agentic systems that can research issues, coordinate with multiple internal systems, and even escalate complex problems to human agents with detailed context and suggested solutions.
Development workflow automation is yet another promising application. You can build an agent that can take a high-level feature request, analyze existing codebases, generate implementation plans, write code across multiple files, run tests, fix issues, and even prepare deployment scripts. The key difference from code generation tools is their ability to maintain context across the entire development lifecycle.
Intelligent data processing is yet another example where agents can be helpful. Rather than writing custom scripts for each data transformation task, you can create agents that can understand data schemas, identify quality issues, suggest and implement cleaning procedures, and generate comprehensive reports — all while adapting their approach based on the specific characteristics of each dataset.
These applications succeed because they handle the complexity that human developers would otherwise need to manage manually. They’re not replacing human judgment but augmenting our capabilities by handling the orchestration and execution of well-defined processes.
Addressing the Skepticism Around Agentic AI
I understand the skepticism. Our industry has a long history of overhyped technologies that promised to revolutionize everything but delivered marginal improvements at best. The concerns about agentic AI are legitimate and worth addressing directly.
“It’s Just LLMs with Extra Steps” is a common criticism, but it misses the emergent properties that arise from combining LLMs with autonomous control systems. The “extra steps” create qualitatively different capabilities. It’s like saying a car is just an engine with extra parts — technically true, but the combination creates something fundamentally different from its components.
Reliability and hallucination concerns are valid but manageable with proper system design. Agentic systems can implement verification loops, human approval gates for critical actions, and rollback mechanisms for errors. In my experience, the key is designing systems that fail gracefully and maintain human oversight where appropriate.
Cost and complexity arguments have merit, but the economics improve as these systems become more capable. An agent that can complete tasks that would require hours of human coordination often justifies its computational costs, especially when considering the total cost of ownership including human time and potential errors.
Agentic AI and Developers
What excites me most about agentic AI is how it’s changing the developer experience. These systems serve as intelligent collaborators rather than passive tools. They can understand project context, suggest improvements, and even anticipate needs based on development patterns.
The debugging experience alone has been transformative. Instead of manually tracing through logs and stack traces, you can now describe symptoms to an agent that can analyze multiple data sources, identify potential root causes, and suggest specific remediation steps. The agent maintains context about the system architecture and recent changes, providing insights that would take considerable time to gather manually.
Code review has evolved from a manual process to a collaborative effort with AI agents that can identify not just syntax issues but architectural concerns, security implications, and performance bottlenecks. These agents understand the broader context of the application and can provide feedback that considers business requirements alongside technical constraints.
Project management has benefited enormously from agents that can track progress across multiple repositories, identify blockers before they become critical, and suggest resource allocation based on historical patterns and current priorities.
Looking Forward: The Practical Path to Agentic AI
The future of agentic AI isn’t about replacing developers—it’s about amplifying our capabilities and allowing us to focus on higher-level problem-solving. The agentic AI systems we’re building today handle routine tasks, coordinate complex workflows, and provide intelligent assistance for decision-making.
The technology is mature enough for practical applications while still rapidly evolving. The frameworks and tools are becoming more accessible, allowing developers to experiment with agentic capabilities without building everything from scratch.
I recommend you start small but think big. Begin with well-defined, contained workflows where the agent can provide clear value. Focus on tasks that require sustained attention or coordination across multiple systems — areas where traditional automation falls short but human oversight remains feasible.
To sum up: the question isn’t whether agentic AI will become mainstream — it’s how quickly we can learn to work effectively with these new collaborative partners, if you will.
Conclusion
Agentic AI represents a significant step in how we build and interact with AI systems. Of course, these systems are not perfect, and they require thoughtful implementation and appropriate oversight. But they’re also not just pure hype.
For developers willing to move beyond the initial skepticism and experiment with these systems, agentic AI offers genuine opportunities to build more intelligent, capable, and autonomous applications.
The hype cycle will eventually settle, as it always does. When it does, I believe we’ll find that agentic AI has quietly become an essential part of our development toolkit — not because it was overhyped, but because it actually works.
Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.
