With all the news about AI agents, it’s easy to get overwhelmed. If you’re building with tools like Microsoft Copilot or exploring how AI agents can help your business, here are 10 terms you should know.
🔁 Autonomy Loop
The full cycle an AI agent follows: it observes the environment, decides what to do, takes action, and learns from the outcome. For example, a customer support agent might recognize a complaint, decide to issue a refund, process it, and then adjust future actions based on the resolution success
🧠 Belief State
This is the agent’s understanding of what’s currently true. If an onboarding agent thinks an employee hasn't completed training even though they have, the belief state needs to be corrected. The accuracy of this internal state impacts how well the agent functions.
📍 Goal-Oriented Behavior
AI agents don’t just follow instructions step-by-step. They pursue outcomes. A sales assistant agent might work toward closing a deal, using emails, calendar invites, and CRM updates based on how the conversation progresses.
🗺️ World Model
The structured knowledge an agent uses to make decisions. This might include users, business rules, current tasks, or external data. A scheduling agent might model things like working hours, room availability, and meeting priorities to make the right recommendation.
🛠 Tool Use
Agents often rely on external software, services, or APIs to complete tasks. For instance, an AI agent for social media could use a publishing API to schedule posts after creating the content.
🧩 Decomposition
The agent breaks a high-level task into smaller pieces that it can act on. An agent assigned to “plan an event” might break it into finding a venue, booking catering, and sending invites, each as its own step.
🧮 Reasoning Engine
This is the logic core of an AI agent. It decides what to do based on goals, current state, and constraints. A financial agent might decide whether to escalate a budget overage based on thresholds and team history.
⚙️ Orchestration
The agent coordinates multiple tools, services, or even other agents to reach a goal. For example, a hiring agent might source candidates, schedule interviews, and generate offer letters, pulling data from HR systems along the way.
🌐 Multi-Agent Collaboration
Sometimes agents work in teams. One might draft a report, another fact-checks it, and a third formats it and sends it to stakeholders. Each has a focused role but contributes to a shared outcome.
🔒 Guardrails
These are the rules, filters, or policies that keep agents operating safely. A compliance-focused agent might restrict language, avoid sharing sensitive data, or pause for approval before sending messages outside the organization.
Understanding these terms is just the beginning. If you're exploring how AI agents can streamline your workflows or boost team performance visit our AI Agents Studio page to see how we can help you turn these concepts into real solutions.
