After the world got wowed, shocked, and then used to (in that order) generative AI in 2024, working professionals quickly hit the ceiling on what was possible with large language models. From hallucinations, to misinformation, to a host of data security, LLMs were good enough at doing what they did, but the world slowly grew to expect more.
Now that AI solutions have well and truly seen adoption across all verticals, it’s easy to spot the shortfalls of traditional LLMs. The fact remains that genAI can do a lot more for working professionals, especially in terms of intelligent automation and improving efficiency while pushing KPIs. That’s where AI agents come in.
Instead of being stochastic parrots like the LLMs that came before them, AI agents are more advanced, and approach problems in a more human-like manner. By definition, AI agents are advanced systems that can independently handle intricate tasks due to their goal-based approach. When combined with large language models (LLMs), AI agents are uncovering new possibilities in automation and enhancing informed decision-making.
In this blog, we’ll give you a brief on what AI agents actually are, how they work, and more. If you're looking to stay ahead of the curve and want ammunition for your next leadership meeting - or just want to streamline your team's workflow - keep reading. Let's get started.
How AI Agents Work: The Mechanics Behind the Magic
At the core of AI agents are the large language models (LLMs) that we know, love, and sometimes hate. However, what sets AI agents apart from these neural networks AI systems is their ability to go beyond simple text generation. AI agents are designed to understand context, make decisions, and take actions autonomously to achieve specific goals.
The operation of AI agents can be broken down into three key stages:
Goal Initialization and Planning:
Agents begin by understanding and breaking down the given goal. This stage employs various planning approaches, from straightforward task decomposition to sophisticated multi-plan selection and memory-augmented planning strategies. The plan typically creates a directed graph or sequence of steps to achieve the goal.
In multi-agent systems, this often involves collaborative decision-making among team members.
Reasoning and Tool Utilization:
In this stage, agents transform plans into action through sophisticated decision-making and tool implementation.
Agents must constantly evaluate situations, make informed decisions, and select appropriate tools from their available arsenal. This includes interfacing with external data sources, accessing APIs, and executing specific functions to accomplish subtasks. The agent's ability to adapt its reasoning and tool selection based on real-time feedback and changing circumstances is crucial for successful task completion.
In multi-agent systems, different agents might specialize in different tools or tasks. Tools are matched to specific subtasks identified in the planning phase.
Learning and Reflection:
This stage serves as a critical feedback loop for continuous improvement. Agents evaluate their performance by analysing success states, reviewing action trajectories, and identifying areas for improvement. This stage goes beyond simple success/failure analysis to include deeper evaluation of strategy effectiveness, tool utilization efficiency, and overall approach optimization.
For multi-agent systems, this includes assessing team dynamics, communication effectiveness, and role performance, often leading to dynamic adjustments in team structure and strategy for future tasks.
Agentic AI vs. Traditional AI
The distinction between agentic AI and traditional AI is crucial for understanding the potential they have to leapfrog existing systems, especially in terms of their general capabilities:
Traditional AI: A Specialized Tool
Traditional AI behaves like a specialized tool, executing specific tasks such as analyzing data or generating basic content. These systems work within rigid frameworks - they might process information or perform calculations, but they can't adjust strategies based on real-time changes or unexpected inputs.
Think of traditional AI as an assembly-line robot in a car factory. It's excellent at performing pre-programmed tasks, such as tightening bolts or painting car doors, but if a new car model is introduced or a supply chain issue arises, it can't adapt on its own. It needs human intervention to adjust its workflow.
A real-world example of this would be a traditional AI-powered recommendation system. While it can suggest products based on past purchases and automate certain processes, it can't capitalize on a sudden change in context that influences the recommendations it should make. The system won't dynamically adjust its approach, and will require human intervention to do so.
Agentic AI: A Self-Thinking Strategist
Agentic AI transforms operations by functioning as an autonomous strategist. These systems can dynamically adapt their approaches, combine multiple tools, and make independent decisions based on changing conditions. For instance, an agentic AI could analyze data, adjust its methodology, and even create personalized solutions - all while continuously learning and optimizing for better results in response to changing environments.
Let's take the same analogy that we used before; that of a car factory. If traditional AI is an assembly line robot, agentic is like a smart factory robot equipped with AI-driven decision-making. Instead of just following preset instructions, it can detect disruptions, adjust production schedules, and reconfigure workflows in real time. If a new car model is introduced or a material shortage occurs, it can autonomously adapt its processes without needing human intervention.
Going back to our real-world example, an agentic AI-powered system can do more than follow predefined rules and automate simple tasks. Equipped with the right tools, agentic AI systems can monitor environmental changes, detect shifts in context, and instantly adjust its strategies, all without waiting for a human to intervene.
Types of AI Agents and Their Reasoning Framework
AI agents come in various types, each with its own level of sophistication and capability. Understanding these types is crucial for marketers looking to use AI in their strategies:
Simple Reflex Agents
These agents take immediate action based only on the current input without considering past interactions or context. They operate on condition-action rules that make them quick but limited in their capability. While these agents are simple to implement, they fail in complex scenarios or scenarios where partial information is available.
They are suitable for straightforward tasks like scheduling social media posts at specific times.
Model-Based Reflex Agents
These agents maintain an internal memory of past observations and experiences, along with a model of how the world works. The agent uses this knowledge along with its memory to make educated guesses and decisions even when partial information is available.
They're useful for tasks like customer segmentation based on changing behaviour patterns.
Goal-Based Agents
Goal-based agents make decisions considering both the current state and the desired goals, unlike reflex agents that simply react to immediate conditions in their environment. They are capable of reasoning about the future consequences of their actions and adjust their behaviour by modifying their explicit knowledge, making them more flexible.
While they might be slower than reflex agents, they can easily adapt to new goals or changing conditions without extensive reprogramming of rules. They employ complex planning and search algorithms to determine the best sequence of actions for reaching their objectives.
These agents work towards specific objectives, making them ideal for campaign planning and optimization.
Utility-Based Agents
Unlike goal-based agents that simply aim for "success or failure," utility-based agents use a sophisticated scoring system to evaluate how good each possible outcome really is, considering multiple factors at once.
They can handle complex real-world situations by calculating the expected value of different options - like weighing the probability of success against potential costs and benefits for each choice. These agents excel at managing trade-offs by assigning numerical values to different outcomes and choosing actions that maximize overall satisfaction or "utility."
These agents are perfect for optimizing ROI across multiple channels.
Learning Agents
Learning agents consist of four key components:
- Performance element that selects actions,
- Learning element that makes improvements
- Critic that provides feedback
- Problem generator that suggests exploratory actions.
Unlike simpler agents, learning agents can improve over time by modifying their knowledge and procedures based on their experiences and feedback from the environment. The learning element can modify any knowledge component, from understanding how the world evolves to learning the consequences of its actions, all while working within a fixed performance standard. The key to learning is the modification of agent components based on feedback to improve overall performance, though this process becomes more complex in partially observable environments
The most advanced type, these agents continuously improve their performance through experience, ideal for long-term strategy development and adaptation.
Reasoning Frameworks: ReAct and ReWOO
Two prominent reasoning frameworks used in AI agents are ReAct (Reasoning and Action) and ReWOO (Reasoning Without Observation):
ReAct (Reasoning and Action)
ReAct is a framework that enables large language models (LLMs) to combine reasoning and acting capabilities. It works by having the model generate both verbal reasoning traces ("thoughts") and concrete actions in an interleaved manner.
The key benefits of using the ReAct Framework are:
- Improved accuracy by grounding decisions in external information rather than just internal knowledge
- Better interpretability since humans can follow the model's reasoning process
- More control since humans can inspect and edit the reasoning traces to guide the model.
ReAct achieved better performance than previous approaches on tasks like question answering, fact verification, and interactive decision making. It's particularly useful for complex tasks that require ongoing adjustment.
ReWOO (Reasoning Without Observation)
ReWOO introduces a novel approach to making Large Language Models more efficient when working with external tools. Instead of the traditional back-and-forth where a model reasons, calls a tool, waits for a response, and then reasons again, ReWOO splits the process into three distinct steps.
- A Planner creates a complete blueprint of all necessary steps without waiting for any tool responses.
- A Worker executes these plans by gathering evidence from external tools.
- A Solver takes both the original plans and the gathered evidence to produce the final answer.
This decoupled approach significantly reduces computational overhead by eliminating the need to repeatedly process the same context information.
Use Cases of AI Agents Across Industries
While AI agents have applications that span multiple sectors, their transformative impact varies distinctly by industry:
- Healthcare Diagnostics: AI agents can analyze medical images, patient histories, and symptom patterns to assist physicians with diagnoses, reducing error rates and accelerating treatment decisions.
- Financial Risk Assessment: From detecting fraudulent transactions to evaluating loan applications, AI agents can continuously monitor financial activities and adjust risk models based on emerging patterns.
- Manufacturing Quality Control: AI agents equipped with computer vision can inspect products on assembly lines, automatically adjusting tolerance parameters based on environmental conditions and material variations.
- Legal Document Analysis: AI agents can review contracts and legal precedents, identifying potential liabilities and suggesting clause modifications while adapting to new case law developments.
- Agricultural Yield Optimization: By monitoring soil conditions, weather patterns, and crop health indicators, AI agents can autonomously adjust irrigation systems and recommend precisely timed interventions for pest management.
- Energy Grid Management: AI agents can balance power distribution networks in real-time, responding to demand fluctuations and integrating renewable energy sources while minimizing wastage and preventing outages.
- Retail Inventory Management: AI agents can track product movements, predict seasonal demands, and autonomously adjust ordering quantities based on multiple factors including weather forecasts and social media trends.
Use Cases of AI Agents in Marketing
While AI agents have applications across various sectors, their impact on marketing is particularly significant:
- Customer Experience Enhancement: AI agents can serve as virtual assistants, providing personalized product recommendations and support, significantly improving customer satisfaction and engagement.
- Content Creation and Optimization: From generating ad copy to optimizing blog posts for SEO, AI agents can streamline the content creation process while ensuring quality and relevance.
- Market Research and Analysis: AI agents can continuously monitor market trends, competitor activities, and consumer behavior, providing real-time insights for strategy adjustment.
- Campaign Management: From planning to execution and optimization, AI agents can manage entire marketing campaigns, adjusting strategies based on performance metrics in real-time.
If you want to read more about the potential of AI agents for marketing, read our blog on the topic.
Yarnit's Role in AI Innovation: Empowering Marketers with Advanced AI Agents
To better equip marketers with the latest and greatest in AI innovation, we at Yarnit came up with a new product ‘Ask Yarnit’. Ask Yarnit is the first agentic AI marketing team, using 10 expert AI agents working in concert to accomplish complex marketing tasks with a simple ‘Ask’. Yarnit provides marketers with a comprehensive suite of tools designed to enhance every aspect of their marketing efforts.
Our implementation of agentic AI incorporates both the ReAct and ReWOO frameworks, allowing for flexible and efficient problem-solving across various marketing challenges. Ask Yarnit utilizes goal-based, utility-based agents and learning agents to ensure that marketing strategies are not only executed effectively but also optimized for maximum outcomes.
Key features of Yarnit's AI agent system include:
- Multi-Agent Collaboration: Yarnit's system employs multiple specialized agents that work together seamlessly, covering everything from SEO optimization to creative content generation.
- Contextual Intelligence: By integrating with a company's existing knowledge base and real-time market data, Yarnit's agents provide highly relevant and personalized marketing solutions.
- Continuous Learning: The platform's AI agents constantly refine their strategies based on performance data and user feedback, ensuring ever-improving results.
- Scalable Personalization: Whether for startups or large enterprises, Yarnit's AI agents adapt to provide tailored marketing strategies that resonate with specific target audiences.
Conclusion
As we've explored throughout this article, AI agents are changing marketing in ways we couldn't have imagined just a few years ago. From simple reflex agents to sophisticated learning agents, agentic AI offers varying levels of capabilities to match your specific marketing needs. Whether you're looking to enhance customer experiences, streamline content creation, gain deeper market insights, or optimize your entire campaign management process, these intelligent assistants are changing the game.
Ready to experience the power of agentic marketing firsthand? Give Ask Yarnit a try - our platform brings together 10 expert AI agents working in concert to tackle your most complex marketing challenges with a simple "Ask." Stop struggling with outdated tools and start experiencing what a true AI marketing team can do for your business.
Want to stay ahead of the competition and transform your marketing efficiency? The future is already here - all you need to do is Ask Yarnit.
