We’ve come a long way from RPA: How AI agents are revolution…

In the past year, the race to automate has intensified, with AI agents emerging as the ultimate game-changers 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-augmented generation (RAG) applications to autonomous multi-agent AI is crucial. As Gartner noted in a recent survey, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024.

As Google Brain founder Andrew Ng aptly stated: “The set of tasks that AI can do will expand dramatically because of agentic workflows.” This marks a paradigm shift in how organizations view the potential of automation, moving beyond predefined processes to dynamic, intelligent workflows.

The limitations of traditional automation

Despite their promise, traditional automation tools are constrained by rigidity and high implementation costs. Over the past decade, robotic process automation (RPA) platforms like UiPath and Automation Anywhere have struggled with workflows lacking clear processes or relying on unstructured data. These tools mimic human actions but often lead to brittle systems that require costly vendor intervention when processes change.

Current gen AI tools, such as ChatGPT and Claude, have advanced reasoning and content generation capabilities but fall short of autonomous execution. Their dependency on human input for complex workflows introduces bottlenecks, limiting efficiency gains and scalability.

The emergence of vertical AI agents

As the AI ecosystem evolves, a significant shift is occurring 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’re proactive — capable of making suggestions before you ask for them. They accomplish tasks across 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 reimagine them entirely, bringing new possibilities to life. Here’s what makes vertical AI agents the next big thing in enterprise automation:

• Elimination of operational overhead: Vertical AI agents execute workflows autonomously, eliminating the need for operational teams. This is not just automation; it’s a complete replacement of human intervention in these domains.
• Unlocking new possibilities: Unlike SaaS, which optimized existing processes, vertical AI fundamentally reimagines workflows. This approach brings entirely new capabilities that didn’t exist before, creating opportunities for innovative use cases that redefine how businesses operate.
• Building strong competitive advantages: AI agents’ ability to adapt in real-time makes them highly relevant in today’s fast-changing environments. Regulatory compliance, such as HIPAA, SOX, GDPR, CCPA and new and forthcoming AI regulations can help these agents build trust in high-stakes markets. Additionally, proprietary data tailored to specific industries can create strong, defensible moats and competitive advantages.

Evolution from RPA to multi-agent AI

The most profound shift 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 Gartner survey, this shift will enable 15% of day-to-day work decisions to be made autonomously by 2028. These agents are evolving from simple tools into true collaborators, transforming enterprise workflows and systems. This reimagination is happening at multiple levels:

• Systems of record: AI agents like Lutra AI and Relevance AI integrate diverse data sources to create multimodal systems of record. Leveraging vector databases like Pinecone, these agents analyze unstructured data such as text, images and audio, enabling organizations to extract actionable insights from siloed data seamlessly.
• Workflows: Multi-agent systems automate end-to-end workflows by breaking complex tasks into manageable components. For example: Startups like Cognition automate 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.
  • Real-world case study: In a recent interview, Lenovo’s Linda Yao said, “With our gen AI agents helping support customer service, we’re seeing double-digit productivity gains on call handling time. And we’re seeing incredible gains in other places too. We’re finding that marketing teams, for example, are cutting the time it takes to create a great pitch book by 90% and also saving on agency fees.”
• Reimagined architectures and developer tools: Managing AI agents requires a paradigm shift in tooling. Platforms like AI Agent Studio from Automation Anywhere enable developers to design and monitor agents with built-in compliance and observability features. These tools provide guardrails, memory management and debugging capabilities, ensuring agents operate safely within enterprise environments.
• Reimagined co-workers: AI agents are more than just tools — they are becoming collaborative co-workers. For example, Sierra leverages AI to automate complex customer support scenarios, freeing up employees to focus on strategic initiatives. Startups like Yurts AI optimize decision-making processes across teams, fostering human-agent collaboration. According to McKinsey, “60 to 70% of the work hours in today’s global economy could theoretically be automated by applying a wide variety of existing technology capabilities, including gen AI.”

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

The accuracy imperative and economic considerations

As AI agents progress from handling tasks to managing workflows and entire jobs, they face a compounding 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 machines thinking; we should be afraid of machines acting 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% accuracy in executing a single task achieves only 72% overall accuracy when performing two tasks (0.85 × 0.85). As tasks combine into workflows and jobs, accuracy drops further. This leads to a critical question: Is deploying an AI solution that’s only 72% correct in production acceptable? What happens when accuracy declines as more tasks are added?

Addressing the accuracy challenge

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

• Robust evaluation frameworks: Define clear success criteria and conduct thorough testing with real and synthetic data.
• Continuous monitoring and feedback loops: Monitor AI performance in production and utilize user feedback for improvements.
• Automated Optimization Tools: Employ tools that auto-optimize AI agents without relying solely on manual adjustments.

Without strong evaluation, observability, and feedback, AI agents 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 agile: The rapid evolution of AI makes long-term roadmaps challenging. Strategies and systems must be adaptable to reduce over-reliance on any single model.
• Focus on observability and evaluations: Establish clear success criteria. Determine what accuracy means for your use case and identify acceptable thresholds for deployment.
• Anticipate cost reductions: AI deployment costs are projected to decrease significantly. A recent study by a16Z found that the cost of LLM inference has dropped by a factor of 1,000 in three years; the cost is decreasing by 10X every year. Planning for this reduction opens doors 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 coworkers. From agentic RAG 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 co-founder and CEO of Zenolabs.AI.