Agentic AI refers to artificial intelligence systems that work like autonomous agents. They can make decisions, plan actions, and adapt to new situations on their own without needing detailed step-by-step instructions. It’s like having an AI intern helping you with manual or reasoning tasks. Well, ‘Intern’ for now, considering the pace of AI’s growth, it soon will be an AI expert!
Imagine a travel bot that not only books flights but also adjusts hotel and car rental plans. It makes these adjustments based on unexpected changes in your itinerary. Essentially, it’s like having a smart digital assistant that manages tasks by itself, much like a human agent would!
Long back, I started updating quotes of AI Agents and AGI. During that time, I found a presentation by Andrew NG on Difference between prompting an LLM Agent and AI Agent.
Although, in his presentation, he also mentioned how much time these AI agents take to come up with the result.
But today, with release of DeepSeek, Alibaba’s Qwen2.5-Max, or OpenAI’s o3-mini model, it is clear that it is possible to improve reasoning while being cost-effective.
This signifies a paradigm shift in how we think about, build, and deploy software solutions. From rigid state machines, we will move to dynamic and autonomous agent systems. This change isn’t merely a technological upgrade. If you’re into software development space, you must upskill and make the most of this shift.
In this blog post, we’ll explore how AI agents are redefining our design and engineering processes. We will draw parallels with historical breakthroughs, and discussing their implications for enterprise applications.
From State Space to Agent Space: A New Paradigm
Let us first understand the ‘state space’ approach to software development, which is what we do today.
What is the ‘state space’ approach to software development?
The state space approach in software development is a method where a system is modeled as a collection of distinct states and the transitions between them. Each state signifies a specific configuration or condition of a system. Then, this system changes from one state to another based on defined events or inputs. This approach is especially common when designing systems that have clear, sequential workflows, like, finite state machines.
For example, consider a travel support bot that begins in a “Greeting” state, where it welcomes the user.
- When the user requests help with booking travel, the bot transitions to a “Collecting Details” state. It asks for the destination, dates, and preferences.
- Based on the provided details, it moves to a “Searching Options” state. Here, it fetches flight, hotel, or car rental options.
- Finally, once the user selects an option, the bot transitions to a “Booking Confirmation” state to finalize the travel arrangements.
State space approach helps in clearly defining the expected behavior of the system at every step. Every possible state and transition is defined. This allows for easier predictions of how the system will react to any given input. You can also systematically test each pathway to make sure the system behaves as expected.
Understanding the limitations of traditional state machines
Traditionally, software systems, especially those involving conversational or task-oriented bots, have relied on state machines.
Going back to our travel support bot, the conventional approach might involve mapping out every possible state (e.g., searching flights, booking hotels, or arranging car rentals) and defining transitions between these states based on user input. This method works when interactions are simple, but it becomes increasingly unwieldy as user queries become more complex.
In a traditional state space, each potential scenario is mapped to a specific state.
For example, consider a complex scenario for a travel support bot. What it if needs to combine flight bookings with hotel and car rental options? The number of states and transitions can grow exponentially. This complexity can make the system difficult to manage, maintain, and extend.
When the complexity of user queries increases, this approach leads to hundreds of predefined states. These may still not account for every edge case. The result is a system that is brittle, hard to scale, and difficult to maintain. Each new user interaction can potentially expose gaps in the state definitions.
What do you think about the limitations of State space approach? – let us know in the comments of any restrictions you faced versus customer demands and available technology tools.
Embracing AI Agent Space approach for software development
The emerging concept of agent space provides a radical alternative.
Instead of defining a fixed set of states, developers now focus on creating autonomous agents. Each AI agent is responsible for a specific function or domain.
For example, consider the travel bot again. Rather than managing an unwieldy state machine, the system can be broken down into four specialized agents:
- Supervisor Agent to coordinate tasks
- Dedicated agents for flights, hotels, and car rentals.
This agent-based architecture is similar to the groundbreaking idea of breaking down signals into frequencies. Infinite time-series data is simplified into a limited number of frequencies. Likewise, we translate state space into agent space. This translation turns a complex array of potential interactions into a simpler, modular system. Each agent has specialized reasoning skills, enabling it to adapt quickly to user requests without relying on fixed states.
Why is AI Agents Space better than State Space?
Dynamic adaptability and robustness of AI Agent Space approach makes them better than State Space approach.
AI agents, particularly those built on large language models (LLMs), can reason, plan, and orchestrate responses dynamically. Even if a user query falls outside of the most common scenarios, the system can still adapt and generate a meaningful response.
Have built AI agent systems for yourself or clients? Let us know in the comments and we would love to share it as an example for this guide.
Limitations of AI Agent Space approach to develop software
Building such AI Agent systems is not without its challenges. LLMs, while incredibly powerful, are also inherently unreliable in certain contexts. This necessitates robust engineering practices and new methodologies grounded in first-principles thinking.
Developers are now tasked with designing these agents. They must also create orchestration layers. These layers guarantee smooth interaction among the agents. When done correctly, this creates systems that are not just reactive, but truly intelligent. These systems are capable of reasoning about the problem at hand. They plan a course of action and execute tasks with minimal human intervention.
AI shifts focus from AI models to software Applications
The transformation brought by AI agents isn’t confined to technical architectures. It also shifts where value is generated in the software ecosystem.
Take, for example, the evolution of companies like Jasper. Initially, people dismissed Jasper as merely a “thin GPT wrapper.” Later, they discovered Jasper’s true value in its application layer. It offers the seamless integration of AI with enterprise workflows. Here’s how Jasper’s CEO explains this shift:
A couple years ago, Jasper was written off as just a “thin GPT wrapper”.
Timothy Young, CEO at Jasper AI (source)
A year ago, when I joined Jasper to help it scale into the enterprise, many friends laughed citing how the “wrapper” offered no value.
I have always disagreed. Now, with the launch of DeepSeek, the latest open-source model from China, it is clear that long term a lot of value will move to the application layer. The models are just a new piece of infrastructure to build upon.
The context, workflows, and UX are the product. How you fuse enterprise data, logic and process it consistently with an LLM is the value.
Over time, the conversation has shifted focus. Initially, it centered on the raw capabilities of AI models. Now, it emphasizes the broader context of their application.
This means, business leaders are less concerned with the underlying models and more focused on the outcomes. Will AI application imrpove growth? Does it increase team’s creativity? Can we rapidly generate content? Are the insights generated actionable?
Hence, now it’s not enough to have a state-of-the-art model.
The true innovation lies in how AI models are embedded into products that work harmoniously with existing business processes.
Example of value shift that supports development of AI Agent-led software development
Enterprise applications are a prime example of this value shift.
AI models like those powering Jasper (and newer open-source alternatives such as DeepSeek) are groundbreaking. However, the success of such products depends on their ability to integrate seamlessly into enterprise environments.
This integration is not just about technical compatibility. It’s about delivering a coherent user experience and workflow that translates directly into business outcomes.
Consider a scenario where a company deploys an AI-powered marketing solution. The software must blend sophisticated natural language understanding with deep integrations into existing marketing tools, databases, and customer relationship management systems. Here, the application’s context, workflow, and user interface define its success—far beyond what the underlying model alone could achieve.
The true art lies in merging enterprise data with the logic and processes. Combining these elements with the capabilities of LLMs creates a product that drives real business value.
When building modern software applications, did you experience hurdles in integrating AI models into the existing system? Let us know your experience in the comments and we can include your insight in this guide.
The Barbell Pattern of Value Creation
A pattern that has emerged in the tech industry is the “barbell” effect in value creation.
On one end of the spectrum, significant value accrues at the infrastructure level. Consider companies like NVIDIA. They offer the hardware and foundational technologies.
On the other end, applications that leverage these technologies (like Jasper, Fireflies, or Perplexity) supply specialized solutions. These solutions are high-value and delivered directly to end-users.
This barbell model underscores an important point. Innovations in AI models are critical. However, the ultimate impact on software development and business operations comes from the applications built on top of these models. Companies that effectively bridge the gap between raw AI capabilities and enterprise workflow demands will lead. They are poised to succeed in the new age of AI-powered software.
How do these shifts impact the future of software development?
When designing the next leading software application enabled with AI, consider below points:
A Shift in Engineering Methodologies
The shift from state space to agent space signifies more than just an architectural change. It requires a complete rethinking of engineering methodologies. Traditional software development emphasizes static states and predefined workflows. This approach must now evolve. It needs to accommodate systems that are dynamic, self-organizing, and capable of autonomous decision-making.
Engineers must adopt new practices that prioritize
- Modularity
- Scalability
- Robust error handling
This involves not only designing sophisticated agents but also developing reliable orchestration layers that manage inter-agent communication and conflict resolution. Moreover, as LLMs continue to improve, ensuring their reliability and consistency in production environments will be paramount.
The Role of Human Oversight
Despite the significant advances in AI, human oversight remains critical.
Autonomous agents may handle routine tasks and manage complex workflows. However, humans still play a vital role. They set goals, monitor performance, and intervene when necessary. Human expertise and AI-driven automation will likely shape the next era of software development. Together, their strengths can be leveraged to create systems. These systems will be efficient, adaptable, and intelligent.
Building Trust in AI-Powered Systems
For enterprise applications, trust is paramount.
Business leaders and end-users need to have confidence that AI systems will deliver consistent and reliable results. This trust is built not just on technological prowess. It relies on the ability to integrate AI into existing workflows seamlessly. This integration ensures that the transition to AI-powered systems does not disrupt daily operations.
As more enterprises adopt AI solutions, the emphasis will shift. It will move from the capabilities of individual models to the overall quality and reliability of the integrated systems.
Conclusion: A New Era in Software Development
TLDR – modern software applications will be built on top of amazing AI model infrastructures using AI Agent Space approach.
By using the power of agent space, we can create systems that are not only reactive but truly intelligent. This paves the way for a future where software can dynamically adapt to the ever-changing needs of its users.
Hence, these modern software will be more flexible, scalable, and capable of handling the complexities of modern user interactions.
Making new software will focus more on end-user experience. This simplifies the development process where the focus shifts on delivering real-world business value.
Are you feeling the change in how we develop modern software applications?
If you are a software developer – what shift are you observing in customer demands? How are you adpating to the changing needs?
Let us know in the comments – I would love to build up on your advise (with recognition ofcourse!) to document this shift.
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Learn more about AI concepts:
- Learn what is tree of thoughts prompting method
- Make the most of ChatGPT – 5 Free ChatGPT Features For Prompt Management
- Join prompt engineering and Generative AI communities to stay in touch with the latest in artificial intelligence.
- Learn what influencers and experts think about AI’s impact on future of work – 15+ Generative AI quotes on future of work, impact on jobs and workforce productivity
- Looking for ChatGPT alternatives? – Explore 30 User-Friendly ChatGPT UI tools
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