Anatomy of an AI Agent - Building Blocks and Workflows

Moving beyond simple prompting techniques, it’s time to examine what actually makes an AI agent tick. In this post, I’ll break down the core components that transform a language model from a sophisticated autocomplete into an autonomous problem-solver, and explore how to model and implement agent workflows.

From Functions to Agents

Traditional software follows a simple pattern: input goes in, deterministic logic processes it, output comes out. The same input always produces the same output.

def calculate_discount(price, discount_percent):
    return price * (1 - discount_percent / 100)

# Always returns 80.0
calculate_discount(100, 20)

This predictability is often desirable, but it breaks down when facing tasks that require judgment, interpretation, or handling novel situations.

Consider a customer support scenario. A deterministic function might use keyword matching:

def handle_support(message):
    if "refund" in message.lower():
        return "Please visit our refund policy page..."
    elif "shipping" in message.lower():
        return "Shipping typically takes 3-5 days..."
    else:
        return "Please contact [email protected]"

This fails spectacularly when a customer writes: “I ordered a gift for my mother’s birthday but it arrived damaged. What can you do?” No keywords match, so they get a generic email response instead of the empathy and problem-solving they need.

An LLM-powered agent approaches this differently - understanding context, inferring intent, and generating appropriate responses dynamically.

The Modern AI Agent’s Core Capabilities

What distinguishes an AI agent from a simple LLM call? Four fundamental capabilities:

flowchart LR
    subgraph Agent["AI Agent"]
        P[Perceive] --> R[Reason]
        R --> PL[Plan]
        PL --> A[Act]
        A --> P
    end

    E[Environment] --> P
    A --> E

    style Agent fill:#e3f2fd

1. Perceive: Gather information from the environment (user input, tool outputs, external data)
2. Reason: Analyze information and draw conclusions
3. Plan: Determine the sequence of actions needed
4. Act: Execute actions, often using external tools

This creates a continuous loop where the agent observes results, reasons about them, and plans next steps.

Five Core Components of an Agent

Every well-designed AI agent needs five essential building blocks:

flowchart TD
    subgraph Components["Agent Architecture"]
        PS[Persona] --> K[Knowledge]
        K --> PR[Prompting Strategy]
        PR --> T[Tools/Execution]
        T --> I[Interaction Layer]
    end

    style PS fill:#fff3e0
    style K fill:#e8f5e9
    style PR fill:#e3f2fd
    style T fill:#fce4ec
    style I fill:#f3e5f5

1. Persona

The persona defines who the agent is - its role, expertise, tone, and behavioral boundaries. This shapes how the agent interprets tasks and formulates responses.

ANALYST_PERSONA = """
You are a senior financial analyst with 15 years of experience
in market research. You communicate insights clearly, always
support claims with data, and flag uncertainty when present.
You never provide specific investment advice.
"""

A well-crafted persona provides:

• Clear role definition
• Behavioral constraints
• Communication style guidance
• Ethical boundaries

2. Knowledge

Knowledge encompasses what the agent knows and can access:

• Parametric knowledge: Built into the model’s training
• Retrieved knowledge: Fetched at runtime via RAG (Retrieval-Augmented Generation)
• Contextual knowledge: Provided in the conversation or task

# Contextual knowledge injection
context = """
Company Policy Updates (Q4 2024):
- Return window extended to 60 days
- Free shipping on orders over $50
- New loyalty program launched
"""

prompt = f"Given these policies:\n{context}\n\nHelp the customer with: {query}"

3. Prompting Strategy

How you structure communication with the LLM dramatically impacts results. This includes:

• System prompts: Set behavior, constraints, and output format
• Few-shot examples: Demonstrate expected patterns
• Chain-of-thought triggers: Encourage step-by-step reasoning
• Output formatting: Specify JSON, markdown, or structured responses

4. Tools and Execution

Tools extend what an agent can do beyond text generation:

• Data retrieval: Search engines, databases, APIs
• Computation: Calculators, code execution
• External actions: Send emails, create tickets, update records
• Specialized models: Image generation, speech synthesis

5. Interaction Layer

How the agent communicates with users and other systems:

• Input parsing and validation
• Response formatting
• Multi-turn conversation management
• Handoff to humans when needed

Agent Sophistication Levels

Not every task needs a fully autonomous agent. Four levels of sophistication:

Level	Description	Example
Direct	Single LLM call, no tools	Text summarization
Augmented	LLM + external tools	Research with web search
RAG	LLM + knowledge retrieval	Customer support with docs
Autonomous	Full reasoning + planning loop	Complex research assistant

Choose the simplest level that solves your problem - unnecessary complexity adds latency, cost, and failure modes.

Modeling Agent Workflows

Before writing code, it helps to visualize the workflow. I’ve found thinking in terms of building blocks useful:

Common Building Blocks

flowchart LR
    subgraph Blocks["Workflow Building Blocks"]
        D[Direct Call]
        A[Augmented]
        R[RAG]
        E[Evaluation]
        RT[Routing]
        P[Parallel]
        O[Orchestrator]
    end

• Direct: Simple LLM call
• Augmented: LLM with tool access
• RAG: Retrieval-augmented generation
• Evaluation: Quality checking step
• Routing: Decision point that directs flow
• Parallel: Concurrent processing
• Orchestrator: Central coordinator managing workers

Example: Customer Data Workflow

Let’s model a workflow that fetches customer data and generates insights:

flowchart TD
    Start([Start]) --> Fetch[Data Fetching Agent]
    Fetch --> Process[Data Processing Agent]
    Process --> Report[Report Generator]
    Report --> End([End])

    Fetch -.-> DB[(Customer DB)]
    Process -.-> Analytics[Analytics Engine]

    style Fetch fill:#e3f2fd
    style Process fill:#fff3e0
    style Report fill:#e8f5e9

Each node represents a specialized agent with a focused responsibility.

Implementing a Simple Agent System

Here’s a basic implementation pattern separating concerns:

from openai import OpenAI

client = OpenAI()

class BaseAgent:
    """Base class for all agents"""

    def __init__(self, name: str, persona: str):
        self.name = name
        self.persona = persona

    def run(self, task: str, context: dict = None) -> str:
        messages = [
            {"role": "system", "content": self.persona},
            {"role": "user", "content": self._build_prompt(task, context)}
        ]

        response = client.chat.completions.create(
            model="gpt-4o-mini",
            messages=messages,
            temperature=0.7
        )

        return response.choices[0].message.content

    def _build_prompt(self, task: str, context: dict) -> str:
        if context:
            context_str = "\n".join(f"{k}: {v}" for k, v in context.items())
            return f"Context:\n{context_str}\n\nTask: {task}"
        return task


class DataFetchingAgent(BaseAgent):
    """Simulates fetching customer data"""

    def __init__(self):
        super().__init__(
            name="DataFetcher",
            persona="You retrieve and structure customer data. Return data in a clean format."
        )


class DataProcessingAgent(BaseAgent):
    """Analyzes data and generates insights"""

    def __init__(self):
        super().__init__(
            name="DataProcessor",
            persona="You are a data analyst. Analyze provided data and generate actionable insights."
        )

Orchestrating the Workflow

def run_customer_analysis(customer_id: str) -> str:
    # Initialize agents
    fetcher = DataFetchingAgent()
    processor = DataProcessingAgent()

    # Step 1: Fetch data
    raw_data = fetcher.run(
        f"Fetch all relevant data for customer {customer_id}"
    )

    # Step 2: Process and analyze
    insights = processor.run(
        "Analyze this customer data and identify key patterns",
        context={"customer_data": raw_data}
    )

    return insights

Key Principles for Agent Design

From building these systems, I’ve learned a few principles:

1. Separation of Concerns
Each agent should have a single, clear responsibility. This makes debugging easier and allows independent improvement.

2. Clear Interfaces
Define what each agent expects as input and produces as output. Ambiguity leads to brittle integrations.

3. Graceful Degradation
When an agent fails or produces low-quality output, the system should handle it gracefully - retry, fallback, or escalate.

4. Observable Execution
Log what each agent receives, decides, and produces. You’ll thank yourself when debugging production issues.

5. Start Simple
Begin with the simplest architecture that works, then add complexity only when needed.

Key Takeaways

1. Four capabilities define agents: Perceive, Reason, Plan, Act
2. Five components to design: Persona, Knowledge, Prompting Strategy, Tools, Interaction
3. Match sophistication to need: Don’t over-engineer simple problems
4. Model before implementing: Visualize workflows to catch design issues early
5. Separate concerns: Single-responsibility agents are easier to build and maintain

In the next post, I’ll dive deeper into one of the most common workflow patterns - chaining prompts together to accomplish complex, multi-step tasks.

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This is Part 4 of my series on building intelligent AI systems. Next: implementing prompt chaining workflows.