AI agents are everywhere right now, but most people still don’t really know what they are.
Some think they’re just smarter chatbots. Others assume they’re some backend-only dev tools. The reality is: AI agents are quickly becoming one of the most important building blocks in modern software.
If you’re building apps or even just thinking about shipping faster, it’s worth understanding exactly what an AI agent is, how it works, and why it’s showing up in nearly every new tool, product, and framework.
Because this isn’t just about adding AI to your app. This is about AI helping you build the app itself.
Let’s break it down.
So, What Is an AI Agent?
An AI agent is a system designed to make decisions autonomously. It takes in information (text input, code context, APIs), reasons through a goal, and executes actions to achieve that goal.
This usually involves:
- Perception – understanding user intent or environmental context
- Planning – deciding the next steps logically
- Action – executing code, calling functions, or building components
- Memory – keeping track of context, past actions, and preferences
It’s more than just a smart autocomplete. Where a standard LLM predicts the next word, an AI agent predicts and executes the next best step.
Unlike a single-shot LLM response, an agent runs as a loop: observe → think → act → repeat. And with multi-modal and web-integrated agents, that loop is now extremely capable.
What Makes Agents Different From LLMs or Copilot?
This question comes up a lot.
LLMs (like GPT) are powerful, sure. But they’re stateless and reactive. They generate text based on a prompt, then move on.
AI agents are stateful, interactive, and goal-oriented.
They:
- Set sub-goals and plan step-by-step
- Call tools/functions intelligently
- Monitor and retry if things go off track
- Adapt their approach based on external state (APIs, files, memory)
At Create, we’ve seen agents reason through entire app architectures, choose between databases, scaffold full CRUD logic, and self-correct broken logic, all in real-time.
And we’re not the only ones betting big on agents:
- GitHub Copilot is quietly moving toward agentic patterns
- LangChain and OpenAI are investing in multi-agent frameworks
How AI Agents Are Changing the App Dev Stack
Let’s get practical.
In a traditional flow, you’d gather requirements, write specs, design the UI, spin up backend logic, test endpoints, write docs, and the list goes on.
AI agents condense that into one tight feedback loop.
At Create, we’ve built an agentic dev system that understands what you’re trying to build, breaks it down into smaller components, and reasons its way through the process, function calls, data models, state handling, and all.
Some things our agent handles out of the box:
- Parses user intent from natural language
- Decides what tech stack fits the use case (e.g., Next.js + Supabase vs. Express + Mongo)
- Plans the app flow (frontend > backend > DB > auth > API)
- Writes modular code that can be edited or extended manually
- Remembers design choices and context across sessions
It’s not just about speed. It’s about building with better defaults, cleaner logic, and fewer edge-case failures.
According to Gartner, by 2026, over 80% of new app development will use generative or agentic AI in some form.
How We Know the Agent Is On the Right Track
Traditional software has tests. Models have benchmarks. But agents:
They’re a different beast.
You can’t always assert a “correct” output. For many prompts, there are 5 valid answers. What you care about is: Did it work? Was it useful? Did the agent actually make progress toward the goal?
That’s why we built our own evaluation system at Create. We call it Create Evals.
Every day, we run our agent through curated prompt sets and real-world use cases, not synthetic demos, and track:
- Whether the app actually functions end-to-end
- If logic holds up under test
- How coherent the UX feels
- And whether changes introduced regressions
tl;dr: We don’t guess if our AI agent is improving. We measure it.
That data loops directly into product and model updates. It’s not just analytics—it’s infrastructure for building smarter agents.
Why AI Agents Are a New Way to Build
Yes, they make things faster. But that’s not the real story.
Teams using AI agents have seen:
- 30–50% faster delivery timelines
- Up to 2x more releases per sprint
- Reduce bugs in early-stage code (Source: McKinsey, The Economic Potential of Generative AI, 2024)
But more importantly, they change how we build.
Instead of designing everything up front, developers can co-create with an agent. Explain your goals. Build iteratively. Let the system handle the repetitive and complex logic while you focus on product thinking.
At Create, we’ve seen non-technical founders ship MVPs in days, and senior engineers use agents to prototype, test, and deploy entire features in hours, not weeks.
This isn’t about removing developers. It’s about supercharging them
Conclusion
If we want to move beyond "write code faster" to "build software better," AI agents are the path forward.
They won’t replace devs, but they will change what devs spend time on. They’ll unlock new builders, compress cycles, and reshape how we go from idea to product.
And if you're curious to see what that feels like in practice?
Start with Create.
It’s more than an AI agent. It’s the system around the agent that makes it actually useful.
