An AI agent uses a language model, context and available tools to work toward a goal through one or more steps. The useful distinction is not whether a product calls itself an “agent,” but how it decides what to do, what it can access, and when it must stop or ask a person. The 20 terms below map those choices; this is a practical selection, not a canonical industry list.
How does an AI agent work?
Microsoft Visual Studio Code defines an agent as “an AI system that uses a language model and tools to complete a goal on your behalf.” The model alone is not necessarily an agent: the surrounding application supplies context, exposes capabilities, executes requested actions and returns results.
A useful mental model is a loop: the system examines what it knows, chooses an action, observes the result and decides whether to continue or stop. Google for Developers describes the typical stages as “Observe,” “Reason,” “Act” and “Feedback.” Actual implementations vary; those labels describe a helpful pattern, not a requirement every product follows.
1. Agent
Software that uses a language model and available capabilities to pursue a goal through context gathering, actions and evaluation. For example, a coding agent might inspect a project, edit a file, run a test and use the test result to decide what to change next.
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2. Agentic
A quality of a system or workflow that involves some autonomy or adaptive decision-making. It is a matter of degree, not a binary product category: one system may choose among a few allowed actions, while another may dynamically plan several steps.
3. Agentic workflow
A process in which an agent plans or takes actions toward a goal and may adjust based on feedback. Unlike a purely fixed sequence, at least some next steps can depend on what the agent learns or what happens. The label does not guarantee that every step is dynamically chosen.
4. Agent loop
The repeated cycle of examining context, deciding, acting and evaluating. The loop may call a tool, inspect its result, and then make another decision. A useful implementation defines clear stopping rules so that iteration does not continue indefinitely.
How do agents choose and use actions?
Tool use is a collaboration between the model and the application: the model can request a capability, but the surrounding runtime is responsible for carrying it out and returning the result. The available capabilities and permissions strongly shape what the system can actually do.
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5. Tool
A capability an agent can invoke to gather information or take an action, such as reading a file, searching a knowledge base or calling an API. The application or runtime executes the request; the model typically receives the result as new context.
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6. Tool calling or function calling
A structured request from a model to invoke a named capability with parameters. For example, a model might request a weather lookup with a city argument. The application validates and runs the request, then returns the result. Tool calling is the invocation pattern; it is not itself the tool or the protocol used to connect to it.
7. Action space
The tools, resources and permissions available to an agent. A broad action space can make a system more capable but also harder to constrain and more error-prone; a narrow one can prevent it from completing a task. Google for Developers cautions that both extremes can cause problems, so expose only the capabilities appropriate to the task.
8. Planning
Selecting or laying out steps to reach a goal. A plan-and-solve approach drafts multiple steps before acting, but a plan should not be confused with a guarantee: results may require the agent to revise what it does next.
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The degree to which a system plans, acts and adapts without continuous human intervention. Autonomy depends on both the workflow and its permissions. A system can make decisions independently within a narrow scope while still requiring approval for consequential actions.
How are agent workflows coordinated?
Coordination can be explicit and predictable or involve runtime decisions. Multiple agents are one possible design, not a prerequisite for agentic behavior.
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10. Orchestration
Coordination and routing across model calls, tools, agents or workflow steps. An orchestrator may follow a fixed sequence or choose a path at runtime. Orchestration does not by itself mean that multiple autonomous agents are involved.
11. Subagent
A narrower specialist agent assigned part of a larger task, often by a manager or orchestrator. A coding assistant, for example, could delegate test analysis to a subagent and use its findings when deciding what to do next.
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12. Multi-agent system
A system in which multiple specialized agents collaborate or pass work among themselves. This can divide a complex task by role, but adds coordination overhead and more interactions to manage. A single agent with several tools is a different, often simpler, architecture.
Fixed workflow or adaptive agent?
| Design | How it behaves | Trade-off |
|---|---|---|
| Fixed or state-machine workflow | Moves through predefined steps or transitions. | Generally more constrained and predictable, but less able to adapt outside its rules. |
| Adaptive agent behavior | Chooses or revises actions based on context and results. | More flexible, but harder to constrain and validate. |
Google for Developers describes state-machine agents as generally making fewer mistakes while adapting less freely beyond their rules. Neither design is universally better: choose according to the task’s need for adaptability, predictability and oversight.
How do agents retain and find information?
Memory and retrieval both supply information, but they solve different problems. Memory concerns what a system retains across steps or sessions; retrieval finds relevant material that can be supplied as context.
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13. Agent memory
Mechanisms for retaining and retrieving information across steps or sessions. AWS distinguishes short-term session memory from persistent long-term memory, and describes episodic, semantic and procedural memory types. Those labels describe different kinds of retained information; implementations need not use every type.
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A pattern that retrieves relevant material and supplies it as context for generation. A basic RAG system may retrieve documents in a fixed preprocessing step before the model responds. Retrieval can also be dynamic, but that is not required for something to be RAG.
15. Agentic RAG
Retrieval directed by an agent’s reasoning loop. The agent can decide whether information is needed, choose what to retrieve, select a retrieval tool and assess whether the returned context is enough. That makes retrieval part of the agent’s decisions rather than only a fixed preliminary step.
16. Embedding
A numeric vector representation of text used to find semantically similar content. Embeddings are often used for semantic search in RAG systems; they represent one retrieval technique, not the whole RAG process.
Session context or persistent memory?
| Approach | What it retains | Useful distinction |
|---|---|---|
| Short-term session memory | Information associated with a current interaction or session. | Supports continuity during that session. |
| Persistent long-term memory | Information retained beyond a session. | Can support later interactions, subject to the system’s retention and access design. |
Retrieval is related but distinct: it can bring external or stored material into the current context without treating that material as memory the agent has retained.
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What does MCP do for developers?
17. MCP (Model Context Protocol)
MCP is an open protocol for standardizing connections between AI applications or agents and external tools, data and services. Google Cloud’s MCP servers documentation describes discovery of tools, prompts and resources, along with authorization controls. MCP is a way to connect to capabilities and information; it is not an agent, a reasoning method or a guarantee that a connected action is safe.
Protocol support changes over time. Google Cloud’s documentation reports support for MCP version 2026-07-28 for its remote servers. That version statement applies to those documented Google Cloud servers, not to every MCP implementation; check the provider’s current documentation when compatibility matters.
How the terms fit together
- Tool: the capability an application makes available.
- Tool calling: the structured way a model requests that capability.
- MCP: one standardized way for applications to connect with tools or data.
- Memory: information retained across steps or sessions.
- RAG: retrieved information supplied as context for generation.
- Orchestration: coordination across components or workflow steps.
How do people and checks keep agents bounded?
18. Human in the loop
A design in which a person approves, corrects or makes a decision at a defined point. It is especially useful before consequential or irreversible actions. Approval gates make the point of human control explicit rather than relying on a person to notice a problem after the fact.
19. Evaluator or critic
A component or agent that checks another output before it is finalized. It may identify issues or request revisions, but evaluation is not proof of correctness: the evaluator can miss mistakes or make its own.
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20. Termination condition
A predefined rule for ending the agent loop. Possible conditions include successful completion, exhausted resources or a person identifying a problem. Clear termination conditions bound iteration and make it easier to recognize when the system should stop rather than continue acting.
Choosing an architecture in practice
These terms describe design choices, not a ladder where “more agentic” is always better. A useful starting point is to decide how much adaptability the task needs, which actions the system may take, and where a person or evaluator should intervene.
Quick Recap
- Prefer a fixed workflow when the steps are known and predictability is more important than adapting to unexpected results.
- Use adaptive decisions where results can meaningfully change the next useful step.
- Start with one agent and a limited set of tools unless the task benefits from dividing work among specialists.
- Choose session context, persistent memory or retrieval according to what information must be available and when.
- Set permissions, review points and stopping rules around the potential impact of actions.
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