In Part 1 (Understanding AI Agents, Agentic AI, and Multi-Agent Systems), we introduced what AI agents are and why they matter for small and medium-sized enterprises (SMEs). In this post, we go one level deeper and explore how AI agents actually work — technically and practically — using three fundamental components: memory, instructions, and tools.
Memory: Short-Term and Long-Term Context
Memory helps agents stay aware of what's happening and what happened before. There are two key types:
- Short-term memory (STM): Keeps track of the current interaction, like remembering the last few messages in a conversation.
- Long-term memory (LTM): Stores information across sessions, like remembering customer preferences or past actions.
What memory enables:
- Personalization
- Coherent conversation flow
- Awareness of historical interactions
With STM, your AI agent doesn't act like a goldfish. With LTM, it remembers your business data and learns over time.
Instructions: What the Agent Is Meant to Do
Instructions shape the behavior of the agent. They include:
- System prompts: Define the agent's role, personality, tone, and boundaries.
- Goals: Define what the agent should achieve (e.g., "book meetings" or "answer product questions").
- Rules: Guardrails and constraints to follow (e.g., "be polite," "don't disclose confidential info").
But here's the catch: instructions alone are not enough to make an agent truly autonomous.
Tools: The Agent's Hands
Tools allow the agent to act in your systems:
- Look up information (via APIs, databases, knowledge bases)
- Modify systems (like CRM updates or order entries)
- Trigger notifications or automations
Examples:
- Use a calendar API to book meetings
- Search a FAQ database to answer questions
- Use email/SMS tools to send updates
Without tools, agents can only talk. With tools, they can do.
What Makes an Agent Truly Agentic?
Great question. Many agents today are not actually agentic — they follow scripts and respond reactively.
Agentic AI means:
- Autonomy: Can act without constant human input
- Reasoning: Can plan multi-step actions based on a goal
- Goal-orientation: Operates with a clear purpose in mind
What your system needs to support this:
- ✅ Modular structure (like LangChain): Supports chaining memory + tools + logic
- ✅ Goal formulation: Let the agent work toward objectives Use LangChain Planners, AgentExecutors, etc.
- ✅ Reasoning loops: ReAct-style chains (Reasoning + Acting) Multi-step planning enabled via prompts or tools
- ✅ Autonomy: Agents can take initiative and execute actions across systems Not waiting for the next prompt — but driving the task forward
What Temperature Actually Does
Temperature controls creativity and variation in output — not autonomy. A higher temperature makes the model more exploratory or verbose, but it doesn't give it goals or planning abilities.
That comes from structure.
TL;DR — Building Smart, Agentic AI
You need more than prompts. To build agents that act like smart employees, you need:
- Clear goals
- A reasoning planner
- Integrated memory
- Actionable tools
This combination turns a passive chatbot into an active digital teammate.
Want to see what that looks like in your business? Start building AI agents that truly work for you.