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An AI agent is more than a language model: it is a goal-directed software system that combines a model with instructions, orchestration, tools, context and state, and runtime infrastructure. Together, these parts let it interpret a request, choose and carry out actions, inspect results, and decide whether to continue or stop. Implementations organize these functions in different ways; not every system has a separately named module for each one.
What are the core building blocks of an AI agent?
The model is the reasoning engine, but the surrounding software determines what task it performs, what information it can use, which actions it can take, and how the run is managed. Microsoft’s overview of agent architecture components and AWS’s guide to the core building blocks of software agents describe overlapping functions, though their diagrams divide them differently.
| Building block | What it contributes |
|---|---|
| Model | Interprets input and context, generates responses, and helps select actions. |
| Instructions and goals | Define the task, operating boundaries, and what counts as progress or completion. |
| Orchestration and planning | Coordinate steps, decide what happens next, and manage the run. |
| Tools and connections | Expose capabilities such as searches, calculations, APIs, databases, and other software functions. |
| Context, retrieval, and memory | Supply relevant conversation details, documents, operational constraints, or retained information. |
| Runtime, interface, and storage | Handle user interaction, execution, messages, and state. |
| Safety and human oversight | Limit access and actions, and add review or approval where needed. |
Model
The model processes the request and available information, generates language, and may help reason about a next step. It does not by itself provide the complete application: the orchestration, tools, state, interface, and policies around it shape how it is used.
Instructions and goals
Instructions describe the agent’s role, task, rules, and conditions for using tools. A goal gives the system a target for choosing and evaluating steps. Goals can be stated explicitly or inferred from the task, but boundaries should make clear what the agent may and may not do.
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Orchestration and planning
Orchestration coordinates the parts and manages task progression. Planning breaks a goal into steps and can be revised when results or circumstances change. The flow may be guided by the model, controlled by deterministic code, or combine both. Microsoft recommends matching the approach to the task: flexible intent assessment favors model-guided handling, while precise, repeatable execution favors a defined workflow.
Tools and connections
Tools are callable capabilities that let an agent query or affect systems beyond the model. They may include search, calculation, summarization, APIs, databases, or other software functions. Connectors and protocols such as MCP can standardize how capabilities are exposed or discovered; they do not replace permission checks or other controls.
Context, retrieval, and memory
Context can include the current request, conversation history, documents, operational constraints, and retrieved data. Retrieval-augmented generation supplies external information to the model. In agentic retrieval, the agent can decide whether to retrieve information and what to seek.
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Memory may be temporary session context or information retained for later use. AWS describes categories including episodic, semantic, and procedural memory. These are design capabilities—not a promise that an agent automatically learns from every interaction or updates its behavior without an intentional process.
Runtime, interface, and storage
A user may interact through a chat interface or another application. Runtime infrastructure receives messages, executes the system, and manages state and storage. A platform diagram may show these as separate components, while a framework may bundle several together.
Safety, permissions, and human oversight
Access controls and responsible-AI safeguards constrain what the agent can do. Scope tool permissions to the task rather than treating a tool’s availability as authorization to use it freely. A human review or approval step can be placed at consequential decisions where judgment or accountability matters.
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How does the agent loop work?
A common pattern is a perceive-reason-act loop. It describes how components cooperate; it is not a claim that every agent has identical internal steps.
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- Receive input: The system takes in a user request or other event.
- Interpret goal and context: The model and orchestration relate the input to instructions, available context, and the target outcome.
- Choose a plan or next action: The agent selects a step, such as answering directly, retrieving information, or calling a tool.
- Execute: Orchestration carries out the step, often by invoking a permitted tool or interface.
- Inspect the result: The returned information or action result becomes part of the context for the next decision.
- Continue or stop: The system repeats as needed, then ends when it meets a completion condition, returns a final response, encounters an error, or reaches a configured limit.
For example, a research assistant could retrieve documents, summarize them, and return a synthesis. A support workflow could use an API to look up an order. These illustrate possible designs, not guarantees of accuracy or performance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you choose an agent architecture?
Choose a design based on the shape and risk of the task, rather than assuming that more autonomy or more components are inherently better. A deterministic workflow suits structured processes where steps and outputs must be precise. A model-led workflow can handle more varied inputs and decisions. A hybrid design can use a model to interpret a request and code to enforce repeatable execution.
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- Predictability: How much can the input and required sequence vary?
- Planning flexibility: Must the system adapt its steps as it learns more, or follow a known path?
- Reliability: What happens if a tool fails, the model makes a poor choice, or information is incomplete?
- Latency and cost: How many model turns and tool calls can the task tolerate?
- Human involvement: Which decisions need approval, review, or an escalation route?
- Operational complexity: Can the team monitor, secure, evaluate, and maintain the design?
Start with one agent when its responsibilities are manageable
A single agent with a model, clear instructions, and a defined tool set is often the simplest starting point. OpenAI’s practical guide to building agents recommends beginning with one agent and expanding its tools before adding multi-agent coordination when feasible.
Add multiple agents for distinct responsibilities
Multiple agents can help when work divides into distinct responsibilities or when one agent’s instructions and tool choices become difficult to manage. Google Cloud describes sequential, parallel, and hierarchical patterns for different task shapes in its guide to choosing a design pattern for an agentic AI system. The added coordination also brings more operational overhead, including evaluation, security, reliability, and cost demands. Use multiple agents because the division of work helps—not simply because the system can support them.
Quick Recap
What should a reliable agent design make explicit?
- Goal and stopping condition: Define the intended outcome and what ends the run.
- Tool boundaries: Specify which functions are available and limit their permissions to the task.
- Decision ownership: Identify actions that require human review or approval.
- Failure handling: Decide how the system responds to errors, missing information, or unsuccessful tool calls.
- State and memory: Choose what information persists and for how long, rather than assuming all context should be retained.
- Evaluation: Check whether the system completes the task safely and reliably across relevant cases, including failures and edge cases.
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