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AI agents use a model to decide what to do next, tools to access information or take permitted actions, and a runtime to execute those tool requests. Memory and context help the agent carry useful information through a task or into a later run. MCP, the Model Context Protocol, provides a shared way for clients to discover and call capabilities exposed by servers; it does not itself make an agent, grant permissions, or guarantee security.
What makes an AI system an agent?
There is no single definition used by every vendor. A practical way to understand an agent is as a model-driven system that can use context and capabilities across multiple steps to pursue a task. The model receives the task, instructions, relevant context, and descriptions of available tools. It then decides whether to answer, request a tool, or take another step.
That flexibility is not always necessary. Some applications follow a predefined workflow in code, while an agent lets the model choose among steps. For a task that only needs one response or a simple retrieval, a conventional model call may be enough. Adding autonomy is useful when the system needs to adapt its next step to information it has just received, but it also adds decisions that the application must control.
How an agent uses tools
A tool is a defined capability that connects the model to information or an action outside its own response. The model does not independently operate an external service: it requests a call, and the application or agent runtime validates and executes it. The result is returned to the model, which can use it to decide what to do next or to prepare a final answer.
#1 Best Overall
- Receive the task and tool definitions. The model sees the user’s request, relevant context, and descriptions of available capabilities.
- Request a specific tool call. The model produces a structured request using the tool’s defined inputs.
- Execute and return the result. The runtime or server handles the request and sends the result back to the model.
- Continue or respond. The model can make another request based on the result, or provide an answer to the user.
For example, a customer-support workflow might look up an order, check a refund policy, issue an approved refund, and summarize what happened. The first two calls retrieve information; issuing the refund changes a system. This sequence illustrates a possible workflow, not a claim that a model can issue a real refund without application permissions and authorization.
Three useful tool categories
- Data tools retrieve information, such as an order record or a document.
- Action tools change or communicate with a system, such as updating a record or sending a message.
- Orchestration tools hand work to another process or specialist agent.
Keeping these roles clear helps an agent select an appropriate capability and helps the application apply controls suited to the operation. In particular, a read-only lookup and a consequential write should not be treated as interchangeable just because both are exposed as tools.
What MCP does—and what it does not do
The Model Context Protocol (MCP) is an open specification for connecting AI clients to external tools and data. An MCP server can publish capabilities for a client to discover and use. The protocol standardizes a connection pattern; it does not define an agent’s overall reasoning, ensure that an integration is trustworthy, or grant access to systems on its own.
- Tools are callable functions with structured inputs.
- Resources are data or content a client can read.
- Prompts are reusable prompt templates.
- Instructions provide server-wide guidance for using its capabilities.
A typical tool definition includes a name, description, and input schema, and may include an output schema. The server publishes definitions and handles calls; the client or runtime discovers capabilities, sends requests, and returns results to the model. The exact connection location depends on where the server is reachable and how the application is deployed.
MCP is not a model, database, or automatic security layer. The host application and connected server determine which data and actions are exposed and under what controls. Authentication, authorization, network access, and safe execution remain deployment responsibilities. Keep credentials out of reusable tool definitions and logs; where agent-generated code is involved, use credential handling that does not expose secrets to that code.
How context, compaction, and memory differ
These terms describe different ways information is available to an agent. A model’s current context is not the same thing as durable memory, and reducing the context for a continuing task is not the same as saving information for a future run.
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- Current task context is the conversation, instructions, tool descriptions, and other information available during the present run.
- Compacted state is a reduced representation used to continue the current run when its context needs to be made smaller. It should preserve the state needed to proceed, rather than replaying every earlier turn.
- Persisted memory is selected information made available to a later run, so the system can reuse useful workflow lessons without loading the entire earlier interaction.
Implementations vary: there is no universal memory architecture shared by all agent frameworks. In an OpenAI cookbook example, a generated memo is treated as a human-reviewed source of truth. More generally, durable evidence and important outputs belong in explicit artifacts people can inspect; a model’s recalled or summarized memory should not be treated as an authoritative record by itself.
Choosing where the agent loop runs
A central design decision is who owns orchestration and state: a managed platform, the application, or a lower-level API loop. OpenAI documents three starting points. They are vendor-specific product options, not universal categories for all agent systems.
Best Value
| OpenAI option | Where the loop and control sit | When it can fit |
|---|---|---|
| Agents API | Managed runtime | When using a platform-managed agent flow is appropriate. |
| Agents SDK | Application-controlled orchestration | When the application should own more of the orchestration. |
| Responses API | Direct model interaction, or a loop built by the application | When a lower-level interaction or custom agent loop is needed. |
Product behavior can change, so consult the current documentation before choosing an implementation. Compare options by the responsibilities that matter to your application:
- Orchestration: Who decides when a tool runs, retries, or hands off work?
- State and continuity: What session state is saved, what can be compacted, and what information is separately persisted for future runs?
- Connectivity: Are capabilities application tools, remote MCP servers, or a combination? Where must a server be reachable?
- Security and control: Where do credentials live, what permissions are granted, and how are external actions authorized?
- Tool quality: Are names and descriptions clear, schemas well-defined, and tool selection tested?
Designing tools that agents can use reliably
A strong prompt cannot compensate for a confusing or poorly bounded tool. Treat each capability as an interface that both the model and the application need to understand.
Quick Recap
- Give each tool a narrow purpose, accurate name, and description that explains when to use it.
- Define structured inputs and, where useful, expected outputs. Document behavior and boundaries rather than relying on the model to infer them.
- Separate information retrieval from consequential actions, and make application authorization explicit for writes or communications.
- Test whether the agent selects the right tool for representative requests, and evaluate results rather than assuming a valid call is a correct call.
- Start with the smallest workflow that solves the task. Add model-driven flexibility only where the next step genuinely depends on what the system learns.
- Decide in advance who owns the agent loop, persistent state, credentials, approvals, and execution environment.
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