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There is no single required blueprint. Platform diagrams group these functions differently, and some implementations bundle several into one component. The useful question is what each function does—not whether the architecture gives it a separate box.
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What makes software an AI agent?
An agent uses a model to interpret context and help pursue a defined goal, while surrounding software enables it to plan, act, and respond to results. The model may generate language and help select actions, but it does not by itself provide the application’s tools, permissions, state management, or execution environment.
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AWS’s Well-Architected Agentic AI Lens defines an agent as “an autonomous software system that uses a large language model (LLM) as its reasoning engine to perceive context, plan actions, execute tasks, and adapt its behavior in pursuit of a defined goal.” In practice, autonomy is bounded by the system’s instructions, available tools, configured limits, and any human approvals.
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The core building blocks
Model
The model interprets the user’s input and supplied context, generates responses, and may help reason through a task or choose a next action. The application around it determines what information it receives and what it can do. Without that surrounding software, a model’s response does not automatically query a database, update an order, or persist information between sessions.
Instructions and goals
Instructions establish the agent’s role, task, operating rules, and conditions for using tools. A goal gives the system a target for deciding which steps to take and whether the task is complete. Goals can be explicit in a user request or implicit in a workflow; making important completion conditions clear helps the system avoid treating an intermediate result as success.
Orchestration and planning
Orchestration coordinates the model, tools, context, and execution flow. It decides what happens next, routes tool calls, handles returned results, and manages the run. Planning is the process of breaking a goal into steps; a plan may be revised when new information changes what is useful.
Orchestration can be model-guided, deterministic code, or a hybrid. A model-guided flow can accommodate varied inputs, while code-driven steps can enforce precise and repeatable sequences. Microsoft’s agent architecture guidance emphasizes choosing the approach to fit the task rather than making every workflow flexible by default.
Tools and connections
Tools are callable capabilities the agent can use to obtain information or take action outside the model. They may include search, calculation, document retrieval, databases, APIs, or functions in other software. The tool set defines the agent’s practical reach: an agent without an order-lookup connection cannot retrieve an order merely because it understands the request.
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Protocols such as MCP can help standardize how tools are exposed and discovered, but they do not replace access controls. Each connection still needs permissions appropriate to the task. A tool’s availability is not permission to use it indiscriminately.
Context, retrieval, and memory
Context is the information available to the model for the current decision. It may include the user’s request, earlier conversation, documents, operational constraints, or data retrieved from another system. Retrieval-augmented generation (RAG) supplies external information to the model; in agentic retrieval, the agent can decide when to retrieve information and what to look for.
Memory can refer to temporary state used during a task or information retained across tasks. AWS describes categories including episodic, semantic, and procedural memory. These are design choices, not evidence that an agent automatically learns from every conversation. Persistent memory requires deliberate decisions about what to store, how long to keep it, and who can access it.
Runtime, interface, and storage
The interface is how a person or another system communicates with the agent, such as through a chat client or an application. Runtime infrastructure receives messages, runs the workflow, manages state and storage, and executes tool calls. Depending on the platform, these functions may appear as distinct components or be bundled into a framework.
Safety, permissions, and human oversight
Access controls and safeguards constrain which actions an agent can take. Permissions should be scoped to the task, especially when tools can change data or trigger external effects. A human approval step can be placed where a consequential action needs judgment, authorization, or confirmation.
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Oversight is not a substitute for reliable execution: the system should still have clear completion conditions, error handling, and limits. The appropriate boundary depends on the consequences of a mistaken action and the degree of autonomy the workflow needs.
How the agent loop works
A common pattern is a perceive–reason–act loop. The exact internals vary, but the sequence helps explain how the building blocks cooperate.
- Receive and interpret: The interface passes in a request and any available conversation, documents, or other context.
- Relate it to the goal: Instructions and goals frame what outcome is wanted and which boundaries apply.
- Choose a plan or next action: The model and orchestration decide whether to answer directly, gather more information, or call a tool.
- Execute: Orchestration invokes the selected tool or other interface, subject to its permissions.
- Inspect the result: The returned information becomes context for the next decision. The agent may continue, revise its plan, or prepare a response.
- Stop or hand off: The run ends when a defined completion condition is met, a final response is returned, an error occurs, or a configured limit is reached. A workflow can also pause for human review.
This is a useful architectural model, not a claim that every agent uses identical modules or follows the same internal sequence.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing an architecture
Architecture is a trade-off between flexibility and control. A structured, predictable task may benefit from deterministic steps; varied requests may need model-guided decisions; many systems combine the two. Consider the following dimensions before selecting a design.
- Predictability: Are inputs and required outputs consistent, or do they vary substantially?
- Planning flexibility: Must the system adapt its steps to new information, or can the sequence be specified in advance?
- Latency and cost: How many model decisions and tool calls does a successful run require?
- Reliability: What should happen when a tool fails, returns incomplete data, or produces an unexpected result?
- Human involvement: Which actions require review or approval before proceeding?
- Operational complexity: Can the team observe, secure, test, and maintain the chosen workflow?
Single agent or multiple agents?
A single agent with a defined tool set is often the simplest starting point. OpenAI’s practical guide to building agents recommends starting with one agent and expanding its tools where feasible before adding coordination between agents.
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Multiple agents can help when responsibilities are genuinely distinct or when one agent’s instructions and tool choices become difficult to manage. They also add coordination overhead and make cost, latency, security, reliability, and evaluation more demanding. Google Cloud describes sequential, parallel, and hierarchical patterns for different task shapes; the pattern should follow the work rather than serve as an end in itself.
| Design choice | Useful when | Main trade-off |
|---|---|---|
| Deterministic workflow | Steps and outputs need to be precise and repeatable. | Less adaptable to unexpected inputs or changing circumstances. |
| Model-guided workflow | Inputs vary and the next step depends on interpretation or context. | Requires careful limits, evaluation, and handling of unexpected decisions. |
| Hybrid workflow | Some decisions need flexibility while important steps must remain controlled. | Requires clear boundaries between model decisions and fixed logic. |
| Multi-agent system | Distinct responsibilities or task decomposition justify coordination among agents. | More coordination, operational complexity, and evaluation work. |
What the parts look like in practice
Research assistant
A research assistant might receive a question, retrieve relevant documents, summarize them, and return a synthesis. The model interprets the question and prepares the response; instructions define the task; retrieval supplies material; orchestration controls the sequence; and the runtime manages the session and results. If the answer must be grounded in a particular collection, the retrieval scope and source permissions matter as much as the model.
Order-support workflow
A support workflow might look up an order through an API. The agent needs instructions defining what it may disclose or change, a connection with appropriately limited access, and a way to interpret the API’s response. Looking up an order is different from canceling it: a consequential change may warrant stricter permission or a human approval step.
Designing for dependable behavior
Before adding more tools or agents, define what success means and what the system is allowed to do. Then make its execution observable enough to identify where a run went wrong: model decision, tool call, returned data, state handling, or completion logic.
The Tool Desk
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- Expose only the tools needed for the task and scope their permissions.
- Specify how the run should respond to errors, missing information, and configured limits.
- Use human review at decision points where approval or judgment is important.
- Test the complete workflow, including tool responses and stop conditions—not only the model’s standalone answers.
- Add multi-agent coordination only when separate responsibilities solve a real design problem.
A well-designed agent is therefore not simply a capable model with a prompt. It is a controlled system in which goals, decisions, actions, context, and execution fit together—and whose autonomy is limited to what its tools and safeguards can safely support.
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