Deep dive into the core components of AI agents — the LLM brain, memory systems, tool interfaces, planning modules, and observation loops that make autonomous AI systems work.

The Anatomy of an AI Agent: Understanding Agent Architecture and Components

Every AI agent, regardless of the framework it's built with or the task it performs, shares a common set of architectural components. Understanding these components — how they work individually and how they interact — is essential for anyone building, deploying, or evaluating agentic AI systems.

At PADISO, Sydney's leading AI automation agency founded by Kevin Kasaei, we've architected dozens of production agent systems. This guide breaks down the anatomy of an AI agent into its core components, explains how they work together, and provides practical guidance for building effective agent architectures.

The Agent Architecture Overview

An AI agent can be understood as a system with five core components operating in a continuous loop:

┌─────────────────────────────────────────────┐
│                  AI AGENT                    │
│                                             │
│  ┌─────────┐  ┌──────────┐  ┌───────────┐ │
│  │   LLM   │  │ Planning │  │  Memory   │ │
│  │  Core   │◄─┤  Module  │◄─┤  System   │ │
│  └────┬────┘  └──────────┘  └───────────┘ │
│       │                                     │
│  ┌────▼────┐  ┌──────────┐                 │
│  │  Tool   │  │Observa-  │                 │
│  │Interface│──┤tion Loop │                 │
│  └─────────┘  └──────────┘                 │
│                                             │
└─────────────────────────────────────────────┘
        │              ▲
        ▼              │
   ┌─────────┐    ┌─────────┐
   │External │    │ Results │
   │ Tools   │───►│& Feedback│
   └─────────┘    └─────────┘

Each component plays a distinct role, and the interaction between them creates the emergent behaviour we recognise as "agency."

Component 1: The LLM Core

The large language model is the cognitive engine of the agent. It processes natural language inputs, reasons about problems, generates plans, decides which tools to use, and produces outputs. Every other component exists to support and extend the LLM's capabilities.

Model Selection

The choice of LLM significantly impacts agent performance across several dimensions:

Reasoning capability: More capable models like Claude Opus and GPT-4 produce better plans and handle complex reasoning tasks more reliably. For mission-critical agents, investing in a stronger model pays dividends in reduced error rates.

Speed: Smaller models like Claude Haiku or GPT-4o-mini respond faster, which matters for agents that make many sequential LLM calls. Some architectures use fast models for routine decisions and powerful models for complex reasoning.

Cost: Token pricing varies dramatically between models. An agent processing thousands of tasks daily needs cost-efficient model selection. Model routing — using different models for different sub-tasks — is a key optimisation strategy.

Context window: Agents often need to process large amounts of information simultaneously. Models with larger context windows can handle more complex tasks without information loss.

The System Prompt

The system prompt defines the agent's identity, capabilities, constraints, and behavioural guidelines. It's the most important configuration point for any agent.

system_prompt = """
You are a financial analysis agent for ACME Corp.

Your role is to:
1. Analyse financial data from our PostgreSQL database
2. Generate insights about revenue trends and anomalies
3. Produce weekly summary reports

Constraints:
- Never modify data in the database (read-only access)
- Flag any transaction over $100,000 for human review
- Use Australian dollar formatting
- Always cite the specific data points supporting your conclusions

When uncertain, ask for clarification rather than making assumptions.
"""

A well-crafted system prompt is the difference between an agent that reliably accomplishes its goals and one that goes off the rails.

Component 2: The Planning Module

The planning module determines how the agent approaches a given task. Rather than diving straight into action, effective agents develop a strategy first.

Planning Patterns

ReAct (Reasoning + Acting): The agent alternates between reasoning steps (thinking about what to do) and action steps (executing tools). This interleaved approach allows the agent to adapt its plan based on intermediate results.

Thought: I need to find Q4 revenue data. Let me query the database.
Action: query_database("SELECT SUM(revenue) FROM sales WHERE quarter = 'Q4'")
Observation: Total Q4 revenue: $2,450,000
Thought: Now I need to compare this with Q3 to identify the trend.
Action: query_database("SELECT SUM(revenue) FROM sales WHERE quarter = 'Q3'")
Observation: Total Q3 revenue: $2,180,000
Thought: Q4 shows 12.4% growth over Q3. Let me investigate which product lines drove this.

Plan-and-Execute: The agent generates a complete plan upfront, then executes each step sequentially. This approach is more efficient for well-understood tasks but less adaptive to unexpected results.

# Plan phase
plan = agent.create_plan("Analyse Q4 performance")
# Returns: [
#   "1. Query Q4 revenue by product line",
#   "2. Compare with Q3 and Q4 last year",
#   "3. Identify top-performing and underperforming lines",
#   "4. Analyse contributing factors",
#   "5. Generate summary report with charts"
# ]

# Execute phase
for step in plan:
    result = agent.execute_step(step)
    plan.update_context(result)

Tree-of-Thought: For complex problems, the agent explores multiple reasoning paths simultaneously, evaluating each before committing to a direction. This is computationally expensive but produces better results for ambiguous problems.

Dynamic Replanning

Real-world tasks rarely go exactly as planned. Effective agents detect when their plan is no longer viable and replan dynamically. This might happen when:

A tool returns an unexpected error
Intermediate results reveal the problem is different than assumed
New information changes the optimal approach
External constraints change during execution

Component 3: The Memory System

Memory gives agents the ability to maintain context, learn from experience, and build knowledge over time. Without memory, every agent interaction starts from zero.

Working Memory

Working memory is the agent's current context — the conversation history, task state, and intermediate results. It's analogous to a human's short-term memory. Most frameworks implement this as the LLM's conversation context.

The challenge with working memory is capacity. LLM context windows are finite, and as conversations grow, older information falls out. Effective agents manage working memory by summarising past interactions and keeping only the most relevant context active.

Episodic Memory

Episodic memory records specific past interactions — what tasks the agent performed, what approaches worked, what errors occurred. This allows agents to learn from experience within a session or across sessions.

# Storing an episodic memory
memory.store_episode({
    "task": "Generate monthly revenue report",
    "approach": "Used SQL aggregation with date filtering",
    "outcome": "success",
    "notes": "Client prefers bar charts over line charts for monthly comparisons",
    "timestamp": "2026-03-15"
})

# Retrieving relevant episodes for a new task
relevant_episodes = memory.recall("Generate quarterly revenue report")

Semantic Memory

Semantic memory stores factual knowledge — company documentation, product specifications, policy documents, FAQs. This is typically implemented using vector databases like Pinecone, Weaviate, or Chroma.

When an agent needs to answer a question or make a decision requiring domain knowledge, it queries the vector store to retrieve relevant information:

# Semantic memory retrieval
relevant_docs = vector_store.similarity_search(
    query="What is our refund policy for enterprise customers?",
    top_k=5,
    filter={"category": "policies"}
)

Procedural Memory

Procedural memory captures how to do things — learned procedures, optimised workflows, and task-specific strategies. This is the least mature memory type in current agent frameworks but represents an active area of development.

Component 4: The Tool Interface

Tools are what transform an LLM from a text generator into an agent that can take action in the world. The tool interface defines how the agent discovers, selects, and invokes external capabilities.

Tool Definition

Each tool is defined with a clear specification that the LLM can understand:

from openclaw import Tool

@Tool(
    name="query_database",
    description="Execute a read-only SQL query against the company database. Use this to retrieve financial data, customer records, and operational metrics.",
    parameters={
        "query": {
            "type": "string",
            "description": "The SQL query to execute. Must be a SELECT statement."
        },
        "database": {
            "type": "string",
            "enum": ["finance", "customers", "operations"],
            "description": "Which database to query"
        }
    }
)
def query_database(query: str, database: str) -> dict:
    # Validate query is read-only
    if not query.strip().upper().startswith("SELECT"):
        raise ValueError("Only SELECT queries are permitted")
    # Execute and return results
    return db_connections[database].execute(query)

Tool Selection

When the agent needs to take action, the LLM evaluates the available tools and selects the most appropriate one based on the current context and goal. The quality of tool descriptions directly impacts selection accuracy.

Best practices for tool design:

Write clear, specific descriptions that help the LLM understand when to use each tool
Define input parameters precisely with types, constraints, and examples
Return structured outputs that the LLM can easily parse
Include error information in outputs to help the agent recover from failures

Tool Chains

Complex tasks often require multiple tools used in sequence. The agent orchestrates these tool chains dynamically:

Query the database for raw data
Pass results to a calculation tool for analysis
Send analysis results to a chart generator
Include the chart in a report generated by a document tool
Email the report to stakeholders

Each step's output becomes the next step's input, with the agent making decisions about how to transform and route data between tools.

Component 5: The Observation Loop

The observation loop is what makes agents truly autonomous. After each action, the agent observes the result, evaluates whether it's making progress toward its goal, and decides what to do next.

The Loop Structure

def agent_loop(goal, max_iterations=50):
    plan = create_initial_plan(goal)

    for i in range(max_iterations):
        # Decide next action
        action = decide_next_action(plan, memory, context)

        # Check for completion
        if action.type == "COMPLETE":
            return action.result

        # Execute action
        observation = execute_action(action)

        # Update context with observation
        context.add_observation(observation)

        # Evaluate progress
        progress = evaluate_progress(goal, context)

        # Replan if needed
        if progress.requires_replanning:
            plan = create_revised_plan(goal, context)

    return "Max iterations reached without completion"

Stopping Conditions

Knowing when to stop is as important as knowing what to do. Agents need clear stopping conditions:

Goal achieved: The agent has accomplished its objective
Max iterations: Safety limit to prevent infinite loops
Budget exhausted: Token or cost limit reached
Confidence threshold: Agent is too uncertain to continue safely
Human escalation: The task requires human judgement

Error Handling

The observation loop is also responsible for error handling. When a tool call fails or returns unexpected results, the agent needs to:

Recognise the error
Determine if it's recoverable
Try an alternative approach or escalate
Update its plan to account for the failure

How Components Interact

The power of agent architecture comes from how these components interact in real time:

A goal enters the system
The planning module breaks it into steps
The LLM core selects the first action
The tool interface executes the action
The observation loop processes the result
The memory system stores relevant information
The LLM core reasons about the next step
Steps 3-7 repeat until completion

This cycle creates a feedback loop where each action informs the next, allowing agents to handle complex, multi-step tasks that would be impossible with single-shot LLM calls.

Architecture Variations

Different use cases call for different architectural emphases:

Customer support agents prioritise memory (for customer context) and tool access (for account management), with relatively simple planning needs.

Research agents emphasise planning (for multi-source investigation) and memory (for synthesising findings across many documents).

DevOps agents prioritise tool access (for infrastructure management) and the observation loop (for monitoring and responding to system states).

Data analysis agents balance all components equally, needing strong planning for analysis workflows, extensive tool access for data sources, and robust memory for maintaining context across complex analyses.

Building with OpenClaw.ai

OpenClaw.ai provides a clean implementation of this architecture, allowing developers to focus on their specific use case rather than building agent infrastructure from scratch.

The framework handles the observation loop, memory management, and tool execution, while giving developers full control over the planning strategy, tool definitions, and guardrails.

from openclaw import Agent, MemoryStore, Guardrails

agent = Agent(
    name="Data Analyst",
    model="claude-sonnet",
    memory=MemoryStore(type="vector", provider="chroma"),
    guardrails=Guardrails(
        max_iterations=30,
        max_cost_usd=5.00,
        require_approval=["delete_*", "send_*"]
    ),
    planning_strategy="react",
    tools=[query_db, create_chart, generate_report]
)

Practical Recommendations

Based on our experience at PADISO building production agents:

Start with the simplest architecture that works. Don't over-engineer early. Begin with a basic ReAct loop and add complexity only when needed.
Invest in tool quality. The most common agent failures stem from poorly defined tools — vague descriptions, missing error handling, or ambiguous parameter specifications.
Monitor the observation loop. Log every iteration — the reasoning, the action taken, the observation received, and the decision made. This is invaluable for debugging.
Set conservative guardrails initially. You can always loosen limits as you build confidence. Starting too permissive risks costly errors in production.
Test with realistic scenarios. Agent behaviour is emergent and can surprise you. Test with the actual complexity and variability of real-world inputs, not just happy-path scenarios.

Understanding agent anatomy isn't just academic — it's the foundation for building systems that actually work in production. Every architectural decision, from model selection to memory design, impacts the agent's reliability, cost, and capability. Get the architecture right, and everything else follows.