
## Introduction

The landscape of artificial intelligence is rapidly evolving, and at its forefront are [AI agents](/blog/what-are-ai-agents) - tools that can autonomously perform tasks, drive conversations, and enhance business processes. We've seen AI agents progress from cumbersome, code-heavy frameworks to the rise of [no-code platforms](/blog/no-code-ai-development) and, more recently, to Agent-First Apps. This evolution makes AI more accessible than ever, empowering businesses of all sizes to transform their operations.

## Overview of AI Agent Evolution

AI agents have undergone a remarkable transformation. In the early days, building agents required extensive coding and deep technical knowledge, making them accessible to only a few. For example, frameworks like LangChain and LLamaIndex require proficiency and a solid understanding of Python to build agents. However, the rise of no-code platforms has shifted the paradigm. Businesses no longer need specialized programming skills to harness the power of AI. Today, anyone with an idea can leverage no-code tools like Salesforce's Agentforce and Google's Vertex AI Agent Builder to create intelligent assistants.

Beyond accessibility, we're moving towards pre-built agents and, ultimately, Agent-First Apps - where intelligent agents are at the core of a business's operational model. This evolution is the next frontier, offering companies new ways to improve customer experiences, automate workflows, and streamline operations.

## Importance of These Trends

The trends we're about to explore are fundamentally reshaping business operations, customer experiences, and employee workflows. They're also making AI accessible to a broader audience, giving businesses the tools to innovate faster and more effectively. From simplifying AI development to tailoring solutions for specific industries, these trends democratize AI and open new possibilities for organizations worldwide.

![AI Agent Evolution Timeline showing the progression from code-first frameworks to no-code platforms](/images/blog/7-Trends-Shaping-the-Future-of-AI-Agents-Future.jpg)
*The evolution of AI agents from complex frameworks to accessible no-code platforms*

### Trend 1: The Shift from Code-First Towards No-Code Agent Builders

The first significant trend is the shift towards no-code platforms, simplifying AI development. The recent introduction and popularization of these no-code agent builders, such as Salesforce's Agentforce and Google's Vertex AI Agent Builder, educate customers that no-code agent builders can build complex agents, allowing even non-technical users to build sophisticated AI agents.

The shift from building agents in code to no-code agent builders. This democratization of AI is being driven by the increasing demand for attaining ROI on AI. Companies want to innovate faster, solve customer problems more efficiently, and integrate AI into everyday workflows.

### Trend 2: Adopting Pre-Built Agents to Jumpstart AI Projects

With the rise of pre-built agents, businesses no longer need to start from scratch - even with no-code tools. Many companies prefer to deploy ready-made support, sales, and customer service agents. This trend particularly appeals to organizations lacking technical skills, as pre-built agents offer faster time-to-market, reduced complexity, and cost-effective solutions.

Pre-built agents need to be customizable to be widely adopted and used by many customers. If no-code agent builders are available, even better, as they allow businesses to start with a pre-built agent, test it, customize it, iterate, and deploy - continuously improving the agent to meet their unique needs - without involving AI Engineers.

### Trend 3: Verticalization of AI Agents

Another trend shaping the future of AI is the verticalization of AI agents. Startups are increasingly building agents tailored to specific industries - such as healthcare, legal, financial services, and software development - addressing unique workflows and challenges within those sectors.

Companies like Abridge (focused on healthcare) and Harvey (targeting legal services) are prime examples of this specialization. Vertical agents offer tailored solutions, handling industry-specific nuances more effectively and improving efficiency and outcomes for their users.

### Trend 4: The Rise of Agent-First Apps in SaaS

The evolution of Software as a Service (SaaS) is shifting towards Agent-First Apps, where AI agents are becoming the primary interface to the apps, enabling employees to accomplish a lot quickly. Rather than relying on manual processes, businesses are increasingly looking to AI agents to handle workflows, customer interactions, and internal processes.

Platforms like Zapier and Retool are moving towards agent-driven automation, signaling a broader shift toward AI-led workflows. This trend transforms SaaS from traditional user-driven models into intelligent systems capable of autonomously understanding and executing complex tasks.

### Trend 5: Large and Well-Funded Startups Focus on Enterprises

Large and well-funded startups primarily focus on enterprises or enterprise use cases, leaving small and medium-sized businesses (SMBs) underserved. The current generation of no-code Agent Builders often requires integration with enterprise software such as Salesforce or cloud providers like Google Cloud, which can demand thousands of dollars in implementation services.

For example, Sierra.ai charges $100,000 as a starting price for its projects, which typically require a consulting engagement. Additionally, companies like Accenture, IBM, and even hyperscalers are charging upwards of $50,000 to do AI transformation projects or implement Generative AI MVPs.

### Trend 6: Leveraging API Platforms to Build Cross-App Conversational AI Agents

Workato's expansion into conversational AI agents (Workato Genies) signals a shift towards agentic orchestration, where AI agents are not built on a single app ecosystem but are being built across multiple app ecosystems using cross-platform APIs to handle complex, cross-functional workflows. These AI agents engage users through conversational interfaces like Slack and Microsoft Teams, making it easier for employees to work across multiple SaaS clouds such as ServiceNow, Salesforce, Workday, and more.

### Trend 7: Generative UI - Bringing Rich Interactions to Chat-Based Agents

Last but not least, generative UI is an important step in transforming chat conversations. Users no longer need to sacrifice rich interactivity when engaging with agents through chat. Instead, they can experience app-like functionality directly in a conversation.

Salesforce's Generative Canvas for Salespeople is an example of this transformation. Thanks to generative AI-powered UX integrated into Salesforce CRM, the Generative Canvas provides dynamically generated experiences that utilize the entire screen. These experiences include text, tables, charts, trends, and other rich visual Lightning components to represent key metrics.

## Conclusion: The Future of AI Agents - A Unified Platform for All

From no-code agent builders to pre-built solutions and agent-first apps, these trends shape the future of AI development and adoption. Businesses of all sizes can leverage these technologies to improve efficiency, reduce costs, and offer personalized experiences to their customers.

As these trends converge, AI agents are becoming integral to business operations, automating complex workflows, improving customer interactions, and enabling organizations to innovate faster. The future of AI is about bringing the power of agents to everyone - making intelligent automation the new normal for businesses worldwide.


Agentman Blog

Posts tagged with "future of ai"

From No-Code Builders to Agent-First Apps - 7 Trends Shaping AI Agents