How to choose a framework for building agentic apps

We're entering a new era of software development, one shaped by agentic apps. Instead of scripting behavior line by line, teams are now building systems that can reason, act, and adapt in real time. This shift is redefining how we think about logic, control, and scale.

The upside is massive. Agentic systems promise to offload complexity, unlock automation, and deliver software that evolves alongside its environment. But that upside introduces a new kind of challenge. Developers are navigating a rapidly expanding universe of tools, language models, and architectural decisions. Building an agent that performs a single task can be relatively easy. Building one that can handle nuance, recover from failure, and scale across workflows is much harder.

This complexity isn't just technical. It's strategic. Choosing the right framework for building agentic apps is now one of the most important decisions a team can make. This article is a guide to making that choice with clarity and confidence.

Demo-first vs. production-first: What's at stake

Agentic systems promise new levels of adaptability and intelligence, but they also demand new kinds of tools, processes, and infrastructure. As teams begin experimenting with designing and building agents, they often face a fundamental fork in the road:

Do you want something that looks great in a demo—or something that actually works at scale?

Most frameworks on the market are optimized for showcasing flashy interactions and a quick time to demo, not supporting real-world systems under load. But the longer you delay choosing the right foundation, the more technical debt you accumulate.

At the heart of this decision is often a tradeoff between demo-ready and production-ready frameworks. Demo-ready frameworks are designed for speed and simplicity. They help teams spin up prototypes fast and create impressive proofs of concept. But that same simplicity becomes a liability when you introduce messy data, complex workflows, and real-time constraints. They often lack robust error handling, flexible model integration, and the operational requirements needed for production-grade deployment.

Production-ready frameworks are built with resilience, scale, and modularity in mind. These systems require more upfront design work and skills to leverage a new way of building but pay dividends in operational reliability. They support structured workflows, enforce state across sessions, and give you the flexibility to integrate multiple models, tools, and agents without extensive glue code.

This choice shapes several critical areas of your AI strategy:

• How fast you can iterate: Demo-first tools help you move quickly early on, but may slow you down later as complexity increases. Production-grade frameworks support faster iteration at scale by providing reusable components, observability, and strong foundations.
• How easy it is to onboard new models or workflows: A strong framework allows you to plug in new models, tools, or agent logic without rewriting everything from scratch. This matters as model proliferation accelerates and teams experiment with specialized agents using different tools for different use cases.
• How resilient your agents are in real-world conditions: In production, systems must handle unexpected inputs, fail gracefully, and recover quickly. Production-ready frameworks bake in monitoring, fallback paths, and memory persistence that demo systems often skip.
• How far your AI-native capabilities can scale: As your org grows from one agent to dozens, you'll need support for versioning, context management, orchestration, and long-term memory. Starting with a scalable foundation avoids painful migrations down the line.

Choosing the wrong foundation can cost more than time. It can lead to costly rewrites, brittle integrations, and stalled initiatives. And while it's tempting to defer this decision, kicking the can too far down the road almost guarantees rework when you're least able to afford it.

As Matt Wyman, CEO of Okareo, puts it: "Choosing the right agentic framework is an architectural decision that shapes an application's capabilities and reliability, much like the design of a building determines its strength and functionality."

In other words, this is your architectural moment. Treat it like one. Evaluate your current needs, but also think about where your AI systems need to be 12 months from now. The right choice now sets the stage for sustainable growth, faster iteration, and reliable systems that can handle whatever comes next.

What you actually need in production

Most teams building with LLMs today start with the model. They prompt it, wrap it in a UI, and call it an agent. But when that prototype hits real-world complexity—multi-step tasks, changing conditions, enterprise data, and failure recovery—it breaks. That's because a real agent is more than a model. It's a system.

A production-grade agent is a self-directed software process that can make decisions, take action, and adapt based on goals and context. It's not a prompt wrapper. It's an integrated system built from three foundational capabilities:

1. Models → Reasoning

A model is the reasoning component of an agent. It interprets goals, inputs, and context to decide what should happen next. This could be generating a response, selecting a tool to use, composing a plan, or adapting a workflow based on user input.

The model does not handle state, workflows, or integration logic. It alone does not know what has already happened or what comes next. It simply evaluates the current context and produces a next step, often in the form of natural language, code, or a structured instruction.

Therefore, to reason effectively, the model requires high-quality input, including prior events, relevant knowledge, and the constraints of the environment. In other words, the model can only be as useful as the context it receives. That context is assembled and delivered by the app and graph layers.

2. Apps → Coordination and memory for agent behavior

The app layer handles what surrounds the model. It is responsible for turning the model's output into real-world actions, managing the flow of logic over time, and ensuring continuity across sessions.

Apps enable two core capabilities. Coordination governs how agents interact with external systems, sequence actions, handle retries, and adapt to changing conditions. This is how integration and flow management are handled in real-time scenarios. Memory stores what has already happened, including prior inputs, outputs, user choices, tool responses, and more, so agents can act with continuity and context. This allows agents to behave as persistent systems that evolve and respond intelligently to ongoing interaction.

This layer ensures that agents aren't just reactive. They are stateful, adaptable, and operational. It is what enables agents to function as reliable processes capable of carrying out complex tasks over time.

3. Graphs → Knowledge for differentiation and depth

A graph is the structured knowledge environment that informs and constrains the agent's decisions. It includes entities, relationships, facts, policies, and logic that define the environment the agent operates in.

Unlike documents or raw vector embeddings, graphs represent information as connected, queryable relationships. They expose how different things relate to each other, such as how a product links to its inventory, warranty terms, and associated regulations.

This structure provides a unique and timely worldview that evolves alongside your data. It enables agents to reason with precision, enforce constraints, and avoid hallucinations. Knowledge graphs allow agents to differentiate themselves not just by capability, but by context. A graph ensures that the agent operates correctly within your domain, with traceable logic and explainable decisions.

This loop is what allows agents to behave like systems, not scripts. Remove any piece and the system fails. Without graphs, agents hallucinate or misinterpret. Without apps, they forget what matters or fall apart under real-world pressure. Without models, they cannot reason or adapt to changing goals.

Building for production requires assembling these three primitives from the start. When models, apps, and graphs work together as one system, you get agents that are grounded, adaptive, and production-ready.

How the right foundation changes everything

The difference between a demo and a durable agentic system often comes down to the foundation you choose. A framework that cleanly separates reasoning, coordination, and knowledge will unlock speed, reliability, and adaptability that a demo-first tool simply can't match. Here's how the right foundation reshapes what your agents can do and how fast your team can build them.

1. Less glue code, cleaner system design

In many early implementations, developers write large amounts of custom code to connect models with tools, APIs, or data sources. This glue code is fragile, difficult to debug, and time-consuming to maintain. A well-structured architecture removes the need for this by separating concerns. The app layer provides reusable components for tool dispatch, memory, and orchestration. This reduces complexity, speeds up development, and results in more reliable systems.

2. Built-in observability across the stack

Many teams only monitor what the model generates, but this leaves blind spots across the system. A properly designed agentic system provides visibility into the full execution path. The app layer tracks tool usage and task sequences. The model layer logs decision steps. The graph layer records what knowledge was accessed and how it was used. With observability built in across all components, teams can better understand system behavior, identify bottlenecks, and debug issues more efficiently.

3. Workflow flexibility without rewrites

In rigid systems, even small changes require rewriting logic or prompts. With the right foundation, the app layer manages workflows in a more flexible and modular way. You can add new steps, change decision rules, or introduce new tools without modifying model behavior. This allows your team to adapt quickly to new requirements, expand functionality, and respond to unexpected scenarios without disrupting existing systems.

4. Agents that stay context-aware over time

Context is what enables agents to respond intelligently. In production, agents need to remember past interactions and understand the broader environment they operate in. The app layer maintains session memory so agents know what has already happened. The graph layer provides structured, up-to-date knowledge about people, products, policies, or relationships. The model reasons over this full context to make decisions that are not only accurate but aligned with the situation. This results in agents that feel coherent, helpful, and consistent over time.

From demos to durable systems: Building AI that lasts

We began with a shift: from logic-driven, step-by-step coding to agentic systems that reason, act, and adapt. The opportunity is enormous, but so is the complexity. It's easy to build a demo. It's much harder to build something that works when workflows are unpredictable, data is messy, and real users are involved.

Throughout this piece, we've explored what separates short-lived experiments from production-ready systems. Many frameworks promise intelligence but fall short on the fundamentals: they lack memory, orchestration, and error resilience. Choosing the wrong foundation early leads to tech debt, brittle architectures, and long-term rework.

Hypermode was built to solve these problems—not by adding more tools, but by rethinking the foundation. It gives you the infrastructure that agentic systems actually need in the wild: agents that remember and adapt, workflows that evolve in real time, structured knowledge that grounds decisions, and full visibility into performance and behavior. It's not a black box. It's a flexible, open platform you can build on with confidence.

Whether you're running one agent or orchestrating hundreds, Hypermode integrates with what you already have and elevates it—turning prototypes into products.

If you're ready to make that leap, start building at Hypermode