From Text Generators to Task Executors: Understanding Agentic Behavior in LLMs

Large Language models usually focus on analyzing and creating text responses such as summarizing the content, generating information response for questions asked, or analyzing a sentiment. However, with the recent evolution of Agentic automation, LLMs are now much more advanced and can act on their own to get things done, which can be referred to as agentic behavior.

It is now important to shift paradigms of AI to Agentic AI capabilities to optimize decision-making and reduce human intervention even in complex tasks.

In this blog, we explore what agentic LLMs are, how they behave, how you can build them, and why this matters for the future of automation.

Agentic LLMs vs Traditional LLMs: What’s the Key Difference?

Agentic LLMs aren’t just reactive—they’re proactive. Unlike traditional LLMs, which simply generate responses when asked, agentic models can:

• Set and pursue goals
• Make independent decisions
• Use tools and APIs
• Store and recall memory
• Detect and correct their own errors

Traditional LLMs operate through fixed prompts and follow a linear sequence as they generate responses only when asked. They have predefined steps before and after LLM calls and follow the same set of sequences every time to generate a response. This results in limited problem-solving capabilities.

On the other hand, Agentic LLMs have capabilities to make decisions independently. They solve problems on their own, achieve goals and make choices based on the available tools and APIs. They have memory to learn from human actions and handle situations effectively.

How to Build Agentic LLMs with Prompting, Reasoning, Tools, and Memory?

Making LLMs agentic requires a thoughtful mix of prompting techniques, reasoning strategies, memory structures, and the right tools. Let’s break down the key components.

Agentic LLMs aren’t just reactive—they’re proactive. Unlike traditional LLMs, which simply generate responses when asked, agentic models can:

1. Better Prompting: Prompting is the most important part of agents. Using effective prompting plays a vital role in generating required responses. Using techniques like ReAct and Chain-of-Thought (CoT) prompting technique can be optimized.
2. Reasoning and Acting: Responsible for taking decision on when to take actions and when to use existing knowledge. It gives LLMs the ability to act like decision-makers rather than just responders.
3. Chain of Thoughts: Responsible for breaking down problem into smaller steps.

   Example 1:

   Prompt: I’m hungry. What should I eat for dinner?

   Chain of Thought:

   • I should first think about what ingredients I have.
   • I have rice, eggs, and vegetables.
   • I’m also tired, so I want something quick.
   • Fried rice would be easy and tasty.
   • Let me check if there’s a quick recipe online.

   ReAct:

   Action: Search[quick fried rice recipe]

   Observation: Found a 15-minute recipe with just rice, eggs, soy sauce, and veggies.

   Answer: Make quick egg and vegetable fried rice. It’s fast, uses what you have, and sounds delicious!

   Example 2:

   Prompt: What should I do this weekend?

   Chain of Thought:

   • I want to relax and have fun.
   • Weather might affect outdoor plans.
   • I’ll check the forecast first.

   ReAct:

   Action: Search[weather this weekend in my city]

   Observation: It’s sunny and 75°F on Saturday.

   Answer: Great weather for a picnic or a hike! You could visit a local park with friends and bring some snacks.

4. Reasoning and Acting: Reasoning and Acting: This component decides when to take action and when to use existing knowledge. It gives LLMs the ability to act like decision-makers rather than just responders.
5. Reasoning and Acting: Frameworks: Choosing right framework is very essential to have your agent successfully accomplish the task with all available components. Using tools like LangGraph, AutoGen, Copilot Studio, UiPath Agent Builder can enable developers to build a promising agent.
6. Reasoning and Acting: Connecting the Right Tools: Only agent may not be sufficient to complete your end-to-end task. So, it is important to use the right tools and automations to complete your agentic lifecycle and automate end to end process with agent + RPA by linking LLMs to things like search engines, databases, and software APIs.
7. Reasoning and Acting: Giving Memory to Agents: Without memory agent works the same as traditional LLM. Memory makes it agentic LLM different than traditional LLM as Agent can learned from human actions and stores those responses to utilize them in future for similar situations. Using Vector databases, embeddings, and other memory techniques makes your LLM agentic!
8. Reasoning and Acting: Self-Correction for Better Performance: Helping agents learn and fix mistakes is important for getting accuracy in responses. Better exception handling and information on how to handle a specific situation will make your agent more productive.

UiPath Agent Builder: Low-Code Platform to Build AI Agents

If you're looking for a simple way to create agentic LLMs, UiPath Agent Builder is one of the most accessible platforms available today.

• Goal acceptance: What it is: A platform to easily build AI agents (Low-Code).
• Planning: Key Features: Easy to use, understands language (NLP), connects to tools, allows human checks, customizable (use different LLMs), manages tasks (Orchestration), ensures safety (AI Trust Layer).
• Memory: Impact: More people can build AI agents; speeds up automation.
• Tool usage: AI Agent Activator Partner: Associating with a trusted UiPath fast track agentic automation partner like Accelirate can enable enterprises to build and deploy production-ready Agents in just 5 weeks!

UiPath agent Builder is making headlines as the platform provides capability to build low code agents with ease of use. It allows to incorporate tools and automation integration with Agents. With context grounding capability and ensuring safety with AI trust layer it is user friendly to create our own agents.

UiPath also provides Maestro for agentic orchestration to build, test, monitor and control your agents.

Where Can You Use Agentic LLMs in Real Life?

Agentic LLMs are already making an impact across domains:

• Automating tasks and workflows.
• Creating smart digital assistants that help proactively.
• Assisting with research and discovery.
• Building agents for changing environments (like games).

Challenges and Considerations in Agentic AI

Agentic LLMs come with unique challenges. It’s crucial to understand them before deploying.

• Goal acceptance: Accuracy: LLMs can be wrong or "hallucinate". Who's responsible?
• Safety: Need to control them and make sure they align with human goals, avoiding harm or bias.
• Security: Can be misused; need to protect data.
• Ethics: Need clear rules and understanding of how they decide things.

What’s Coming Next in Agentic LLMs?

The field of agentic LLMs is evolving rapidly. We can expect:

• Smarter reasoning and decision-making.
• Better memory for LLMs.
• Improved safety methods.
• Agents working together in teams.
• More focus on building AI responsibly.

Moving from Static LLMs to Active Agents

Agentic behavior makes LLMs much more capable. Traditional LLM are better at static tasks, however, when it comes to dynamic tasks and where decision-making is involved, you can make your LLMs agentic. But with great capability comes great responsibility. It’s important to design agentic systems carefully, with proper checks, memory, and safety layers. Want to explore agentic LLMs in your automation strategy? Start by building small and experimenting with enterprise agentic platforms like with Accelirate.