In the last part, we explored how agent workflows and execution patterns help autonomous agents operate efficiently. But for agents to become truly intelligent, capable of planning, reflecting, and adapting, they must be equipped with tools and memory. These are not just enhancements; they are core building blocks of Agentic AI.
In this blog, we’ll dive deep into how tools extend an agent’s abilities beyond its language model and how memory enables it to “think over time”, just like humans do.
Table of Contents
Tool Use in Agentic AI
In Agentic AI systems, tools are external assets or systems that agents can invoke to perform actions beyond their built-in capabilities. While large language models can reason over text, their ability to interact with the external world is limited unless they are equipped with tools. Tools effectively act as an extension of the agent’s brain, enabling it to fetch data, perform calculations, manipulate files, or interact with APIs. This turns passive reasoning into dynamic action.
What Are Tools?
In this context, tools are functions, services, or APIs that the agent can call based on its reasoning. The agent must know when to use which tool and how to interpret its output to progress the task at hand.
Examples of tools agents might use:
- APIs: Weather lookup, flight search, calendar access
- Functions: Calculations, data parsing, JSON formatting
- Systems: File reading/writing, sending emails, executing commands
- Search engines: Real-time web search or document retrieval
By combining natural language reasoning with real-world interactions, tools dramatically expand what an agent can accomplish.
When combined with reasoning, the agent decides when to use a tool, what for, and how to handle the result. Frameworks like ReAct (Reason + Act) and Toolformer pioneered these interactions.
Memory in Agentic AI
While tools help agents interact with the world, memory helps them interact over time. Unlike traditional models that treat each prompt in isolation, agentic systems need a way to remember past tasks, preferences, and events to behave coherently and contextually. Memory allows agents to personalize their behavior, reflect on prior results, and avoid repeating past mistakes.
Types of Agent Memory:
Short-Term Memory (STM) captures the current task’s context and is discarded after the session. Long-Term Memory (LTM) persists across sessions and is used to recall general knowledge or user preferences. Episodic Memory stores specific past experiences in a time-stamped format.
| Type | Purpose | Examples |
|---|---|---|
| Short-Term Memory (STM) | Stores temporary context relevant to the current task | A chat thread or loop in execution |
| Long-Term Memory (LTM) | Stores persistent knowledge across tasks | User preferences, task history |
| Episodic Memory | Stores time-stamped interactions or outcomes | Logs of past decisions, results |
This memory is often implemented via vector databases like Pinecone, Weaviate, or FAISS to semantically store and retrieve relevant chunks.
Why memory matters:
- Enables personalization (e.g., “You usually book evening flights”)
- Supports reasoning and reflection
- Reduces redundant tool use by remembering previous answers
- Allows task continuation and re-activation
How is memory stored?
Most implementations use Vector Databases (like Pinecone, Weaviate, FAISS) to store and retrieve semantic memory chunks. Agents embed previous interactions into vector space, then retrieve the most relevant context on demand.
How Tools and Memory Work Together
Let’s take an example: a travel planning agent.
- The user asks to “Plan a 5-day trip to Japan under ₹1,00,000.”
- The agent:
- Uses memory to recall the user prefers warm weather and vegetarian food.
- Uses tools to:
- Look up current weather
- Search for budget flights
- Recommend hotels
- Suggest places to eat
- Stores new preferences (e.g., likes onsens) in memory
- Summarizes and presents a final plan
The result is an adaptive, context-aware experience powered by real-world data and persistent knowledge.
How to Implement This?
Several frameworks support tool + memory integration:
- LangChain: Offers memory and tool abstraction via chains and agents
- AutoGen: Supports agent collaboration with tool calling and chat history
- LangGraph: Enables stateful agents with persistent memory and control flow
Behind the scenes, agents use retrieval (for memory) and function calling or APIs (for tools) to interact with the external world.
Challenges & Considerations
While tools and memory elevate the capabilities of agents, they also introduce new engineering and operational challenges. Tool usage may increase system latency and cost, especially when invoking external APIs. Memory storage can become noisy, outdated, or misleading if not managed carefully.
- Latency: Each tool/API call adds wait time
- Cost: Tool usage and vector storage can be expensive
- Security: Tool execution must be sandboxed and trusted
- Memory Management: Old, outdated, or irrelevant memories must be pruned
- Prompt Engineering: Proper orchestration is needed to decide when to use what
Being aware of these limitations helps in designing robust, scalable agent systems.
Conclusion
Tools and memory form the cognitive and operational core of agentic intelligence. Tools give agents the ability to take real-world actions, while memory ensures they act consistently over time. When used together, they allow agents to become reflective, contextual, and purpose-driven.
In the next post, we’ll explore how multiple agents can collaborate like teams to solve bigger, more complex problems. Stay tuned for AI Series Part 5: Multi-Agent Systems!
Stay tuned! and if you haven’t read
- AI Series: Part 1: Introduction to Agentic AI,
- AI Series: Part 2: Key Components of Agentic AI
- AI Series: Part 3: Agent Workflows and Execution Patterns
check that out too
Prabhu Vignesh Kumar is a seasoned software engineering leader with a strong passion for AI, particularly in simplifying engineering workflows and improving developer experience (DX) through AI-driven solutions. With over a decade of experience across companies like Elanco, IBM, KPMG and HCL, he is known for driving automation, optimizing IT workflows, and leading high-impact engineering initiatives.
