How AI agents think: planning, memory, and tool use
Imagine a digital solution using different tools and cognitive capacities to fulfill tasks, solve problems, and achieve complex goals independently or with minimal human guidance. Sounds like an excerpt from a sci-fi novel? Welcome to a new reality. These systems, known as AI agents, have already opened a new chapter in the large language model (LLM) and generative AI narrative. But what makes these solutions truly intelligent?
Dissecting an AI agent’s brain
The intelligence underlying a typical agentic AI system is built on several fundamental pillars, including perception, reasoning, decision-making, action execution, learning, and adaptation. However, three key elements transform AI agents into proactive, careful strategists and smart, autonomous decision-makers: planning, memory, and tool use.
Planning
Planning is central to effective AI agents and stems from LLMs’ ability to predict upcoming tokens. It allows agentic AI to assess complex environments, identify the best course of action to achieve established goals, and make informed decisions in real time. Planning also helps AI agents break down tasks into smaller steps for easier execution, anticipate potential constraints, and adjust their approach based on unforeseen changes or user interactions.
To determine the most effective paths to achieving their goals, AI agents use the following techniques:
Heuristic search: This method relies on mathematical estimates to help agentic AI systems quickly identify the best solution among numerous options without comprehensively assessing them.
Probabilistic planning: This allows LLM agents to evaluate the likelihood of various outcomes, choose an appropriate plan of action, and make rational decisions even under uncertain conditions.
Reinforcement learning: This mechanism enables agents to identify optimal solutions through trial and error. When agentic AI interacts with an environment, it obtains feedback in the form of penalties or rewards and fine-tunes its actions accordingly.
Memory
Memory is another high-level capacity that distinguishes agents from traditional AI systems. Depending on the intricacy of the assigned tasks, agentic solutions employ:
Short-term memory (STM), which helps AI agents maintain responsiveness and continuity during real-time interactions. STM includes context windows and working memory.
The context window refers to the number of tokens that an LLM can retain at once. A larger context window allows the LLM to produce more accurate and natural responses.
Working memory is essential for complex activities like planning and multistep reasoning, helping agents juggle several inputs simultaneously to complete tasks.
An example of AI agents’ STM would be a chatbot coherently responding to follow-up questions.
Long-term memory (LTM), which is often implemented through retrieval augmented generation (RAG), enables LLM-based agents to store, retrieve, and use historical data. LTM can be episodic, semantic, procedural, or consensus.
Episodic memory allows agents to capture specific experiences and situations from past interactions.
Semantic memory enables agentic AI systems to store common knowledge and general facts.
Procedural memory helps LLM agents save and recall skills, patterns, and learned behaviors to perform tasks without relearning each time.
Consensus memory enables real-time information sharing among multiple AI agents, keeping all participants of the multiagent ecosystem aligned on completed subtasks, decisions, progress, and more.
An example of AI agents using LTM is tracking customer profiles over several years in banking to provide personalized financial recommendations.
Tool use
Another key capacity that enhances AI agents’ decision-making process is their ability to use tools. Agentic AI systems invoke both external and internal functions—such as APIs, web searches, and image generation systems—to complete specific subtasks and achieve defined goals. These smart solutions assess the context to determine when a particular tool is most suitable, ensuring efficient decision-making and task execution.
How can you use AI agents’ intelligence for your business?
With human-like cognitive capabilities, LLM-based agents demonstrate their effectiveness for global companies across various domains. Their intelligence helps boost organizational productivity, cut operating and staffing expenses, deliver ultra-personalized customer support, and maximize profits—everything a modern business needs. Want to benefit from these solutions 24/7? Contact our agentic AI experts now! From tailored consulting and strategy to post-deployment monitoring, we simplify the process of AI agent adoption, making it cost-effective and painless for your business.