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Rahul Agarwal
Founder | Agentic AI... • 1d
Everyone talks about AI agents. Very few people understand what’s actually happening under the hood. Here’s the vocabulary that shows up constantly when working with agent systems. First, the core ideas. An agent is software that observes information, decides what to do, and executes actions toward a defined objective. It operates inside an environment. That environment includes tools, APIs, databases, and everything the agent interacts with. Agents rely on perception. That’s the mechanism used to interpret inputs and understand surrounding context. Every step results in an action. Each action moves the system closer to completing the objective. The system also maintains a state. That simply means the current condition of everything happening right now. Now the intelligence layer. Large Language Models generate text, reason through prompts, and guide many decisions. More advanced reasoning models handle deeper logical problems and complex multi-step thinking. Agents also rely on memory. This stores previous interactions, context, and past execution history. A knowledge base supplies structured information the agent can reference while working. Next comes the system design. Agents call tools. These are APIs, services, or applications that actually perform tasks. Orchestration coordinates how inputs move through the system and become outputs. Agents often plan. They break a goal into smaller steps before execution begins. Some systems follow structured reasoning approaches like step-by-step problem solving. Others use loops that alternate between thinking and acting until the task completes. Finally, the more advanced patterns. Multiple agents can collaborate inside a multi-agent system. Groups may operate using swarm-style coordination driven by simple rules. Tasks sometimes move between agents through structured handoffs. Some architectures even run agent debates to compare multiple generated answers. Then comes evaluation. This measures whether the agent actually performed the job correctly. Understanding these terms makes modern AI systems much easier to reason about. Save and share this. You’ll run into these concepts everywhere as agent systems become more common.
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