Agent Components

You already know what an agent does: it loops, observes, and acts. But if you sat down to build one from scratch, what would you actually need? Understanding the pieces of an agent makes it much easier to design, extend, and debug. When something goes wrong — and it will — you’ll know exactly which component to inspect. Every agent, from the simplest to the most advanced, is built from five core components. Below we walk through them using “Zippy” as an example agent and explain design trade-offs and implementation considerations.

The model — Zippy’s brain that does the thinking.
Tools — Zippy’s hands to interact with the world.
Memory — everything Zippy currently knows (context).
Orchestration — the loop that ties perception, reasoning, and action together.
The system prompt — instructions and personality for Zippy.

Read on to see each component in order, with practical notes and examples.

Quick reference: the five components

Component	Purpose	Example
Model	Performs reasoning and text generation	`gpt-4`, `claude`, `gemini`
Tools	External functions the agent can call	`search_web`, `send_email`, `run_shell`
Memory	Context the model can access (conversation, tool results, summaries)	session history + vector store retrieval
Orchestration	The perceive → reason → act loop	main event loop that runs model + tool execution
System prompt	Defines role, constraints, and output format	role + rules + tool usage guidelines

1) The model — the agent’s brain

The model (an LLM) reads the current conversation and context, decides what to do next, and generates either a final response or a tool call. The model choice affects capability, latency, cost, and safety characteristics. Most agents use a single model for simplicity, but some use multiple models (a fast/cheap model for routine tasks and a larger model for complex reasoning). That single vs. multi-model decision is a trade-off between cost, latency, and capability.

A stylized infographic titled "THE MODEL" showing a central model node that reads the conversation and outputs either a direct response or a tool call. Below are icons and labels comparing single vs multi-model approaches and highlighting trade-offs like capability, cost, and speed.

Best practices:

Benchmark common tasks on candidate models for latency/cost.
Use model ensembles or routing when different tasks have different needs.
Include model-specific guardrails (e.g., temperature, token limits).

2) Tools — the agent’s hands

Tools are the functions the agent can call to interact with the outside world. Without tools, an agent is effectively a chatbot; with tools, it can act: search the web, query databases, send email, run code, or call APIs. Think of tools as the bridge between reasoning and action. The set of tools you give Zippy directly determines what he can do.

A neon-style infographic titled "TOOLS" showing a central "AGENT" node linked to circular icons labeled Search, Calendar, Email, Code, and APIs. The diagram sits on a dark grid background with a capability bar and the subtitle "Functions the agent calls to act."

Design tips:

Keep tool interfaces small and predictable (name, args, return type).
Validate tool arguments before execution.
Sanitize and log tool outputs for auditability and debugging.

3) Memory — the agent’s workspace

Memory is everything available to the model when it makes a decision: conversation history, recent tool results, the system prompt, and any retrieved documents. All of this lives inside the context window, which is a hard token limit. Deciding what to keep, summarize, or evict is a core engineering challenge.

A retro-style infographic titled "MEMORY" showing a long horizontal "context window" bar divided into three colored segments labeled Conversation (yellow), Tool Results (cyan), and System Prompt (pink). The graphic visually represents how memory is partitioned within the context window.

Memory is finite. Common strategies to keep essential context within token limits include: summarization, retrieval-augmented storage (vector DBs), compression, and selective eviction.

Memory considerations:

Store long-term facts in a retrieval system (vector store) and fetch relevant snippets at run time.
Use incremental summarization for long conversations.
Track provenance for facts retrieved from tools or external sources.

4) Orchestration — the perceive → reason → act loop

Orchestration is the glue: the code that runs the agent loop. It typically sends the current conversation and relevant memory to the model, executes any requested tool calls, appends results to the conversation, and repeats until the model returns a final response. A minimal orchestration loop looks like this:

# agent_loop.py
agent_running = True
while agent_running:
    # Send conversation + selected memory to the model
    response = model.call(conversation)

    if response.tool_call:
        # Execute the requested tool and append its output to the conversation
        result = run_tool(response.tool_name, response.tool_args)
        conversation.add({"role": "tool", "name": response.tool_name, "content": result})
    else:
        # Model produced a final response — append and stop the loop
        conversation.add({"role": "assistant", "content": response.text})
        agent_running = False

Notes:

Most of the agent’s “intelligence” comes from the model’s reasoning, not the orchestration code.
Orchestration must handle errors, retries, timeouts, and security checks for tool calls.
Keep orchestration modular: separate model I/O, tool execution, memory management, and logging.

5) The system prompt — defining role & constraints

The system prompt (or “system message”) instructs the model about role, purpose, available tools and how to use them, constraints, safety rules, and the expected output format. A strong system prompt reduces unpredictability and can remove unnecessary complexity from orchestration.

A neon-styled "SYSTEM PROMPT" layout on a dark grid background showing sections like Role & Purpose, Tool Usage, Constraints, and Output Format. To the right is a red graphic labeled "WEAK PROMPT" with an arrow pointing down to the word "CHAOTIC."

Prompting tips:

Be explicit: list tools, expected inputs/outputs, and when tools should be called.
Define safety rules and refusal behavior.
Provide output templates to simplify downstream parsing.

Example: how OpenClaw composes the five components

OpenClaw implements the five components in production with practical choices for reliability and scalability:

Model: multi-provider support (Anthropic Claude, OpenAI GPT family, Google Gemini) with automatic failover.
Tools: shell/batch execution, web browsing, and channel messaging tools.
Memory: session history + compression and retrieval once the context window fills.
Orchestration: a central agent loop implementing perceive → reason → act with logging and retry logic.
System prompt: configurable instructions that define personality, constraints, and tool usage rules.

Final notes

Now that you know the five components, remember: memory management is where a lot of the engineering effort concentrates. Efficient summarization, retrieval, and token budgeting strategies are critical for reliable, capable agents. Future sections will explore memory techniques, tool design patterns, and orchestration best practices.

Links and references

OpenAI documentation
Anthropic developer docs
Google Generative AI overview
LangChain: agent patterns and tools
Retrieval-augmented generation (RAG) patterns and vector stores for memory management

LLM Fundamentals

Tools and Workflows

Building AI Agents

Introduction

Production & OpenClaw

Quick reference: the five components

1) The model — the agent’s brain

2) Tools — the agent’s hands

3) Memory — the agent’s workspace

4) Orchestration — the perceive → reason → act loop

5) The system prompt — defining role & constraints

Example: how OpenClaw composes the five components

Final notes

Links and references

Watch Video

Practice Lab

​Quick reference: the five components

​1) The model — the agent’s brain

​2) Tools — the agent’s hands

​3) Memory — the agent’s workspace

​4) Orchestration — the perceive → reason → act loop

​5) The system prompt — defining role & constraints

​Example: how OpenClaw composes the five components

​Final notes

​Links and references

Watch Video

Practice Lab

Quick reference: the five components

1) The model — the agent’s brain

2) Tools — the agent’s hands

3) Memory — the agent’s workspace

4) Orchestration — the perceive → reason → act loop

5) The system prompt — defining role & constraints

Example: how OpenClaw composes the five components

Final notes

Links and references