Agent Architecture: Perception, Reasoning, and Action Loops

To build an agent with the Gemini ADK, you must stop thinking about "code that follows commands" and start thinking about "architectures that manage intelligence." An agent is not a single script; it is a collection of subsystems working in concert.

In this lesson, we will deconstruct the four fundamental pillars of agent architecture: Perception, Reasoning, Action, and the Feedback Loop. We will explore how these components interact, the data structures that flow between them, and how Gemini acts as the central orchestrator of this complex machine.

1. The Architectural Blueprint

At its most abstract, an agentic system follows the Sense-Think-Act paradigm. While this originated in robotics, it has been adapted for digital agents that navigate software ecosystems.

graph TD
    subgraph "External World"
    A[Environment / APIs / User]
    end
    
    subgraph "Agent Architecture"
    B[Perception Layer]
    C[Reasoning Engine - Gemini]
    D[Action Interface]
    E[State & Memory]
    end
    
    A -->|Sensory Input/Data| B
    B -->|Contextualized State| C
    C -->|Internal Plan| D
    E <-->|Context/History| C
    D -->|Tool Execution| A
    A -->|Observation| B
    
    style C fill:#4285F4,color:#fff
    style E fill:#F4B400,color:#fff

2. Pillar 1: Perception (The Input Processor)

Perception is the agent's ability to ingest and structure information from the outside world. In the Gemini era, perception is no longer limited to text.

Native Multimodal Perception

Unlike previous generations of agents that required separate models for "seeing" and "hearing," Gemini provides native multimodality.

Text Perception: Standard JSON, log files, or user messages.
Visual Perception: Ingesting screenshots of a UI to determine where a button is.
Audio Perception: Listening to a recorded meeting to identify action items.

The Problem of "Signal vs. Noise"

A major architectural challenge in the Perception layer is filtering. If an agent perceives everything (e.g., a 2GB log file), it will quickly exceed its context window or become confused.

Solution: Perception components often include Pre-processing or Summarization steps before the data reaches the Reasoning engine.

3. Pillar 2: Reasoning (The Engine of Decision)

Reasoning is the "Brain" of the agent. This is where Gemini 1.5 Pro or Flash resides. The role of the reasoning engine is to take the perceived state and answer the question: "Based on my goal and what I see, what is the best thing to do next?"

Chain of Thought (CoT) and Self-Correction

Modern architecture encourages the model to "think step-by-step." This is not just for show; it forces the model to allocate more compute to the planning phase.

Planning: Decomposing a complex goal into smaller sub-tasks.
Selection: Picking the right tool for the sub-task.
Validation: Assessing if the previous action was successful before moving to the next.

Reasoning as a "Black Box"

It is important to remember that reasoning is probabilistic. You cannot "debug" the reasoning step like you debug a Python function. Instead, you influence it through System Instructions and Few-Shot Examples.

4. Pillar 3: Action (The Tool Interface)

An agent that stays in its head isn't an agent; it's a philosopher. To be an agent, it must have Effectors—interfaces that let it change the world.

Functional Mapping

In the Gemini ADK, actions are mapped to Tools. A tool is a standard interface (usually a JSON schema) that describes:

What the tool does (The Docstring).
What inputs it needs (The Arguments).
What it returns (The Output).

The Tool Execution Layer

When Gemini "decides" to act, it doesn't actually run the code. It produces a Tool Call request. The ADK runtime then:

Intercepts the request.
Executes the actual Python/API code.
Injects the result back into the agent's context.

5. Pillar 4: The Feedback Loop (The Engine of Progress)

The feedback loop is what separates an "agentic workflow" from a "static script." It is the process of observing the result of an action and using it to inform the next reasoning step.

The Anatomy of a Single Turn

sequenceDiagram
    participant R as Reasoning (Gemini)
    participant T as Tool Interface
    participant E as Environment
    
    R->>T: Action: Search("Apple Stock")
    T->>E: API Request to Yahoo Finance
    E-->>T: Returns "$220.00"
    T->>R: Observation: "$220.00"
    Note over R: New Decision based on result
    R->>T: Action: SendEmail("Price is $220.00")

Why Feedback is Difficult

Infinite Loops: An agent might keep trying the same failing tool.
Drift: After 10 turns, the original goal might be forgotten.
Latency: Each loop turn takes time (LLM inference + Tool execution).

6. Deep Dive: Building the "Reasoning-Action-Observation" Loop in Python

To understand how the Gemini ADK works under the hood, let's build a manual version of this loop. This will demystify the "magic" of the ADK runtime.

The "Mental Model" Implementation

import google.generativeai as genai

# 1. Define our world (The Environment)
def get_stock_price(ticker: str):
    # Mock data
    prices = {"AAPL": 220, "GOOGL": 180, "TSLA": 250}
    return prices.get(ticker, "Unknown ticker")

# 2. Setup the Brain
model = genai.GenerativeModel('gemini-1.5-flash')

# 3. The "Manual" Loop (What the ADK does for you)
def run_agentic_loop(user_goal: str):
    history = [
        {"role": "user", "parts": [user_goal]},
        {"role": "model", "parts": ["I will help you. First, I need to check the prices."]}
    ]
    
    for turn in range(5): # Limit to 5 turns to prevent infinite loops
        # A. Thinking Step
        response = model.generate_content(history)
        print(f"\n[Turn {turn}] Agent thinks: {response.text}")
        
        # B. Check if agent wants to take action
        # (In a real ADK app, this is handled via function_calling objects)
        if "BUY" in response.text:
            print("--- GOAL REACHED ---")
            break
            
        # C. Simulating Observation
        # If the agent asked for a price, we "observe" it and add to history
        if "price" in response.text:
            observation = get_stock_price("AAPL")
            history.append({"role": "user", "parts": [f"Observation: Price is {observation}"]})
        
    return "Agent sequence complete."

# run_agentic_loop("Buy AAPL if the price is under 230")

What ADK Improves Here:

Type Safety: No more parsing BUY or price from strings.
Context Management: ADK handles the history list for you.
Native Tool Use: Gemini actually emits a function_call structured object, not just text.

7. Architectural Considerations for Production

When designing your agent's architecture, keep these three factors in mind:

1. Granularity of Reasoning

Should one agent handle everything, or should you have a Supervisor and multiple Sub-agents?

Small Task: One agent (e.g., summarizing an email).
Big Task: Multi-agent (e.g., writing and testing a new feature).

2. Sandbox vs. Open World

What is the "blast radius" of your architecture?

Read-only tools: Low risk.
Write/Delete tools: High risk (Needs strict Human-in-the-Loop checkpoints).

3. Latency vs. Thoroughness

Gemini 1.5 Flash is fast (great for reactive loops). Gemini 1.5 Pro is thorough (great for complex planning). Often, the best architecture uses both: Flash for the tools and Pro for the high-level plan.

8. Summary and Exercises

Architecting an agent is about Balancing Autonomy with Control.

Perception feeds the loop.
Reasoning (Gemini) processes the state and plans.
Action interfaces with the external world.
Feedback ensures the agent is on the right path.

Exercises

Diagramming: Draw the architecture for an agent that manages a shared calendar for a family. What are the "observations"? What are the "actions"?
Logic Trace: If an agent is told to "Organize my inbox," but the Gmail API returns a "Rate Limit Exceeded" error, how should the Reasoning step handle that Observation?
Prompt Design: Write a system instruction for an agent that restricts it to only using specific tools. How do you prevent "jailbreaking" through tool misuse?

In the next lesson, we will explore Stateless vs. Stateful Agents, a critical distinction that determines how your agent handles long-running tasks and multi-turn conversations.