Insights
Beyond bots: Reimagining automation with agentic AI
Tom Knight, Principal Consultant, UST & Tyler Hugus, Automation Delivery Lead – EMEA, UST
Agentic AI is redefining automation—from bots that follow rules to agents that think, decide, and adapt. Blending reasoning, memory, and real-time context enables smarter workflows and delegated decision-making. It’s not just about doing tasks faster—it’s about transforming how work gets done. The future of automation starts here.
Tom Knight, Principal Consultant, UST & Tyler Hugus, Automation Delivery Lead – EMEA, UST
In the early days of automation, success was measured by how well a bot could mimic a human task. Structured data, clear rules, and predictable outcomes were the foundation. But as business processes become more dynamic and data more complex, that foundation is starting to crack. The next evolution isn’t just about automating tasks - it’s about enabling intelligent decision-making. That’s where agentic AI comes in.
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From reactive automation to proactive intelligence
Agentic AI represents a shift from reactive bots to proactive agents. These agents don’t just follow instructions - they perceive, reason, execute, and learn. They’re designed to operate autonomously or semi-autonomously across multi-step processes, making decisions in real time based on context.
This isn’t just a technical upgrade - it’s a strategic one. In automation, we often ask, “What can I automate?” Agentic AI reframes that question: “What decisions can I delegate?” Whether it’s improving customer experience, validating data, or managing sales channels, agentic AI helps focus on the areas of change that matter most.
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The technology that makes it possible
At the heart of agentic AI are Large Language Models (LLMs) like ChatGPT, Gemini, and Claude. These models respond to structured prompts and can be fine-tuned with examples to improve performance. Prompt engineering, which involves assigning roles, extracting data, and summarizing content, is key to achieving useful outcomes.
But LLMs alone aren’t enough. Retrieval-Augmented Generation (RAG) brings enterprise knowledge into the mix. Instead of relying on the model’s training data, RAG uses a vector database to ground responses in your organization’s context - reducing hallucination and improving relevance.
Agentic RAG goes further by adding reasoning, memory, and tool use. It enables iterative query refinement, multi-agent collaboration, and real-time problem solving. Think of it as giving your automation strategy a brain - and a memory.
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Real-world applications
This isn’t theory. Organizations are already deploying agentic AI in practical ways:
- Internal copilots route queries across internal systems like Teams, Outlook, and proprietary databases - without relying on RPA.
- Sales proposal agents read CRM data, build proposal plans, generate content, and loop in humans for review. It’s fast, semi-autonomous, and grounded in a business context.
- Loan origination agents process documents end-to-end - classifying, extracting, validating, and deciding - without human interaction. NLP and long-term memory improve accuracy over time.
These examples show that agentic AI isn’t just replacing manual work - it’s rethinking how work gets done.
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Planning for what’s next
As with any emerging technology, there’s a risk of chasing hype. But there’s also a risk in standing still. Building long-term strategies around a single tool or platform is no longer viable. Agentic capabilities are also being integrated into vendor offerings, such as platforms like Automation Anywhere or foundation models themselves. With that in mind, we believe organizations should prioritize understanding protocols such as MCP (Model Context Protocol) and A2A (Agent-to-Agent) to facilitate the development of scalable, interoperable AI ecosystems.
- MCP, developed by Anthropic, is a standardized method for signposting your internal knowledge stores, making them reusable and easy to integrate into your agentic workflows.
- A2A enables different AI agents to communicate with each other, allowing one agent to initiate the process and then hand it off to another to complete specific tasks that only they can handle, eliminating the need for a person to intervene and provide instructions.
We believe the technology is only as good as the value it produces for your processes. It’s a new set of tools that probably didn’t exist when you first started needing to complete that task. It’s time to re-imagine your tasks and processes with them.