We’ve come a long way since RPA: How AI agents are revolutionizing automation

In the past year, the race for automation has intensified, with AI agents emerging as the ultimate game-changer for enterprise efficiency. While generative AI tools have made significant strides over the past three years — acting as valuable assistants in enterprise workflows — the spotlight is now shifting to AI agents capable of thinking, acting and collaborating autonomously. For enterprises preparing to embrace the next wave of intelligent automation, understanding the leap from chatbots to retrieval-enhanced generation (RAG) applications to autonomous multi-agent AI is critical. As Gartner noted in a recent study33% of enterprise software applications will include agent AI by 2028. up from less than 1% in 2024.

As Google Brain founder Andrew Ng aptly said, “The set of tasks that AI can perform will expand dramatically due to agentic workflows.” This marks a paradigm shift in how organizations see the potential of automation, moving beyond pre defined processes to dynamic, intelligent workflows.

The limitations of traditional automation

Despite their promise, traditional automation tools are limited by rigidity and high implementation costs. Over the past decade, robotic process automation (RPA) platforms such as UiPath and Automation everywhere have struggled with workflows without clear processes or relying on unstructured data. These tools mimic human actions, but often result in fragile systems that require costly supplier intervention when processes change.

Current Gen AI toolssuch as ChatGPT and Claude, have advanced reasoning and content generation capabilities, but fall short of autonomous execution. Their reliance on human input for complex workflows creates bottlenecks, limiting gains in efficiency and scalability.

The emergence of vertical AI agents

As the AI ​​ecosystem evolves, there is a significant shift toward vertical AI agents—highly specialized AI systems designed for specific industries or use cases. As Microsoft founder Bill Gates said in a recent blog post: “Agents are smarter. They are proactive – able to make suggestions before you ask for them. They perform tasks in various applications. They improve over time because they remember your activities and recognize intent and patterns in your behavior. “

Unlike traditional software-as-a-service (SaaS) models, vertical AI agents do more than optimize existing workflows; they completely rethink them, giving life to new possibilities. Here’s what makes vertical AI agents the next big thing in enterprise automation:

• Eliminate operating costs: Vertical AI agents execute workflows autonomously, eliminating the need for operations teams. It’s not just automation; it is a complete replacement of human intervention in these areas.
• Unlock new opportunities: Unlike SaaS, which optimizes existing processes, vertical AI fundamentally rethinks work processes. This approach brings entirely new capabilities that did not exist before, creating opportunities for innovative use cases that redefine the way businesses operate.
• Building strong competitive advantages: The ability of AI agents to adapt in real time makes them highly relevant in today’s rapidly changing environments. Compliance with regulatory requirements such as HIPAA, SOX, GDPR, CCPA, and new and upcoming AI regulations can help these agents build credibility in high-stakes markets. In addition, proprietary data tailored to specific industries can create strong, defensible moats and competitive advantages.

Evolution from RPA to multi-agent AI

The most profound change in the automation landscape is the transition from RPA to multi-agent AI systems capable of autonomous decision-making and collaboration. According to a recent study by Gartnerthis change will allow 15% of daily work decisions to be made autonomously by 2028. These agents are evolving from simple tools into true collaborators, transforming enterprise workflows and systems. This rethinking happens on multiple levels:

• Recording systems: AI agents like Otter AI and Relevance AI integrate different data sources to create multimodal systems of record. Using vector databases like Pinecone, these agents analyze unstructured data like text, images, and audio, allowing organizations to seamlessly extract actionable insights from clustered data.
• Work processes: Multi-agent systems automate end-to-end workflows by breaking down complex tasks into manageable components. For example: startups like Cognition automating software development workflows, streamlining coding, testing and deployment while Observe.AI handles customer inquiries by delegating tasks to the most appropriate agent and escalating when necessary.
  • A real-world case study: In a recent interviewLinda Yao of Lenovo said, “With our generation of AI agents helping support customer service, we’re seeing double-digit performance gains in call handling time. We’re seeing incredible gains elsewhere, too. We find that marketing teams, for example, cut the time it takes to create a great pitch book by 90% and also save on agency fees.”
• Reimagined architectures and developer tools: Managing AI agents requires a paradigm shift in tools. Platforms like AI Agent Studio from Automation Anywhere enable developers to design and monitor agents with built-in compliance and monitoring capabilities. These tools provide guardrails, memory management, and debugging capabilities, ensuring that agents run safely in enterprise environments.
• Rethought colleagues: AI agents are more than tools — they become collaborators. For example, Sierra uses AI to automate complex customer support scenarios, freeing employees to focus on strategic initiatives. Startups like Yurts AI optimize decision-making processes in teams by fostering collaboration between humans and agents. According to McKinsey“60 to 70 percent of work hours in today’s global economy could theoretically be automated by applying a wide variety of existing technological capabilities, including generation AI.”

Future perspective: As agents gain better memory, advanced orchestration capabilities, and enhanced reasoning, they will seamlessly manage complex workflows with minimal human intervention, redefining enterprise automation.

Accuracy imperative and economic considerations

As AI agents progress from task processing to managing workflows and entire tasks, they face a complex accuracy challenge. Each additional step introduces potential errors, multiplying and degrading overall performance. Geoffrey Hinton, a leading figure in deep learning, warns: “We should not be afraid of machine thinking; we should fear machines that act without thinking. This highlights the critical need for robust evaluation frameworks to ensure high accuracy in automated processes.

Case in point: an AI agent with 85% single-task accuracy achieves only 72% overall accuracy across two tasks (0.85 × 0.85). As tasks are combined into workflows and jobs, accuracy decreases even more. This leads to a critical question: Is it acceptable to deploy an AI solution that is only 72% correct in production? What happens when accuracy decreases as more tasks are added?

Addressing the accuracy challenge

Optimizing AI applications to reach 90 to 100% accuracy is essential. Enterprises cannot afford inferior solutions. To achieve high accuracy, organizations must invest in:

• Robust evaluation frameworks: Define clear success criteria and perform thorough testing with real and synthetic data.
• Continuous monitoring and feedback: Monitor AI performance in manufacturing and use user feedback for improvements.
• Automated optimization tools: Use tools that automatically optimize AI agents without relying solely on manual settings.

Without strong evaluation, monitoring and feedback, AI agents you risk underperforming and falling behind competitors who prioritize these aspects.

Lessons learned so far

As organizations update their AI roadmaps, several lessons have emerged:

• Be nimble: The rapid development of AI makes long-term roadmaps a challenge. Strategies and systems must be adaptable to reduce over-reliance on any single model.
• Focus on observability and ratings: Establish clear criteria for success. Define what accuracy means for your use case and identify acceptable implementation thresholds.
• Anticipate cost reductions: AI implementation costs are expected to drop significantly. A recent study of a16Z found that the cost of LLM degrees had fallen by a factor of 1,000 in three years; costs decrease by 10 times every year. Planning for this reduction opens the door to ambitious projects that were previously cost prohibitive.
• Experiment and iterate quickly: Adopt an AI-first mindset. Implement processes for rapid experimentation, feedback and iteration, aiming for frequent release cycles.

Conclusion

AI agents are here as our colleagues. From agent-based RAGs to fully autonomous systems, these agents are poised to redefine enterprise operations. Organizations that embrace this paradigm shift will unlock unparalleled efficiency and innovation. Now is the time to act. Are you ready to lead the charge into the future?

Rohan Sharma is the co-founder and CEO of Zenolabs.AI.