Agent Architecture

Overview

An AI agent’s architecture defines how it perceives its environment, reasons about tasks, takes actions, and learns from experience. The architecture determines the agent’s capabilities, reliability, and scalability. This page covers the core components and design patterns for building effective agents.

Core Components

graph TD
    subgraph "Agent Architecture"
        PERCEPTION[Perception Layer]
        REASONING[Reasoning Engine]
        ACTION[Action Layer]
        MEMORY[Memory System]
        PLANNING[Planning Module]
    end

    ENV[Environment] --> PERCEPTION
    PERCEPTION --> REASONING
    REASONING --> PLANNING
    PLANNING --> ACTION
    ACTION --> ENV
    MEMORY <--> REASONING
    MEMORY <--> PLANNING

Perception Layer

The perception layer processes inputs from the environment:

class PerceptionLayer:
    def process(self, raw_input):
        # Parse input
        parsed = self.parser.parse(raw_input)
        
        # Extract relevant information
        entities = self.entity_extractor.extract(parsed)
        intent = self.intent_classifier.classify(parsed)
        
        # Update context
        context = Context(entities=entities, intent=intent)
        return context

Reasoning Engine

The LLM serves as the reasoning engine. Different strategies are used depending on the task:

graph TD
    REASONING[Reasoning Strategies]
    REASONING --> DIRECT[Direct Response]
    REASONING --> COT_R[Chain-of-Thought]
    REASONING --> REACT_R[ReAct]
    REASONING --> REFLECT[Reflection]
    REASONING --> PLAN_R[Planning]

Action Layer

The action layer executes decisions from the reasoning engine:

class ActionLayer:
    def __init__(self, tools):
        self.tools = {tool.name: tool for tool in tools}
    
    def execute(self, action_plan):
        tool = self.tools[action_plan.tool_name]
        
        try:
            result = tool.execute(action_plan.parameters)
            return ActionResult(success=True, result=result)
        except Exception as e:
            return ActionResult(success=False, error=str(e))

Tool Registry

graph LR
    TOOLS[Tool Registry]
    TOOLS --> SEARCH[Search Tools]
    TOOLS --> CODE[Code Tools]
    TOOLS --> DATA[Data Tools]
    TOOLS --> COMM[Communication Tools]

    SEARCH --> WEB[Web Search]
    SEARCH --> KB[Knowledge Base]

    CODE --> PY[Python Executor]
    CODE --> SHELL[Shell]

    DATA --> SQL[SQL Query]
    DATA --> API[API Calls]

Memory System

graph TD
    MEMORY[Memory System]
    MEMORY --> SHORT[Short-term Memory]
    MEMORY --> LONG[Long-term Memory]
    MEMORY --> EPISODIC[Episodic Memory]

    SHORT --> CONTEXT[Current conversation context]
    SHORT --> WORKING[Working memory for current task]

    LONG --> FACTS[Facts and knowledge]
    LONG --> PREFS[User preferences]
    LONG --> PROCEDURES[Learned procedures]

    EPISODIC --> PAST[Past interactions]
    EPISODIC --> SUCCESSES[Successful strategies]
    EPISODIC --> FAILURES[Failed approaches]

Planning Module

class PlanningModule:
    def create_plan(self, goal, context):
        # Decompose goal into sub-tasks
        sub_tasks = self.decompose(goal)
        
        # Order sub-tasks (dependencies)
        ordered = self.topological_sort(sub_tasks)
        
        # Create execution plan
        plan = Plan(tasks=ordered, context=context)
        return plan
    
    def replan(self, plan, results, failures):
        # Analyze what went wrong
        analysis = self.analyze_failures(failures)
        
        # Adjust plan
        new_plan = self.adjust(plan, analysis)
        return new_plan

Agent Design Patterns

Single Agent

graph LR
    USER[User] --> AGENT[Single Agent]
    AGENT --> TOOLS[All Tools]
    TOOLS --> AGENT
    AGENT --> OUTPUT[Output]

Simple, good for focused tasks. Doesn’t scale well for complex systems.

Hierarchical Agent

graph TD
    USER[User] --> MANAGER[Manager Agent]
    MANAGER --> WORKER1[Worker Agent 1]
    MANAGER --> WORKER2[Worker Agent 2]
    MANAGER --> WORKER3[Worker Agent 3]
    WORKER1 --> TOOLS1[Specialized Tools]
    WORKER2 --> TOOLS2[Specialized Tools]
    WORKER3 --> TOOLS3[Specialized Tools]

Manager delegates tasks to specialized workers. Better for complex tasks with distinct sub-problems.

Pipeline Agent

graph LR
    INPUT[Input] --> A1[Agent 1: Research]
    A1 --> A2[Agent 2: Analysis]
    A2 --> A3[Agent 3: Writing]
    A3 --> OUTPUT[Output]

Sequential processing. Each agent specializes in one stage.

Error Handling

graph TD
    ACTION[Execute Action] --> SUCCESS{Success?}
    SUCCESS -->|Yes| CONTINUE[Continue to next step]
    SUCCESS -->|No| ERROR[Error Handler]
    ERROR --> RETRY{Retry?}
    RETRY -->|Yes| ACTION
    RETRY -->|No| ALTERNATIVE{Try alternative?}
    ALTERNATIVE -->|Yes| NEW_ACTION[Execute alternative action]
    ALTERNATIVE -->|No| ESCALATE[Escalate to user]

Interview Questions

Q1: What are the core components of an agent architecture?

Answer: The five core components are:

1. Perception: Processes inputs (user messages, tool outputs, environment state)
2. Reasoning: The LLM that makes decisions (direct, CoT, ReAct, reflection)
3. Action: Executes decisions via tools (search, code, APIs)
4. Memory: Short-term (context), long-term (vector DB), episodic (past interactions)
5. Planning: Decomposes goals into sub-tasks, handles dependencies, replans on failure

Q2: How do you handle errors in an agent system?

Answer: A robust error handling strategy includes:

1. Retry with backoff: For transient failures (API timeouts)
2. Alternative tools: If one tool fails, try another
3. Graceful degradation: Return partial results if possible
4. User escalation: When the agent can’t resolve the issue
5. Reflection: Analyze the error and adjust approach
6. Maximum iterations: Prevent infinite retry loops

Q3: Compare single-agent vs multi-agent architectures.

Answer:

• Single agent: Simpler, easier to debug, good for focused tasks. All tools and context in one place.
• Multi-agent: Better for complex tasks with distinct sub-problems. Each agent specializes (research, coding, writing). Enables parallel execution. But harder to debug, coordinate, and may have communication overhead.
• Rule of thumb: Start with single agent. Move to multi-agent when the single agent’s prompt becomes too long or the task has clearly separable sub-tasks.

Common Mistakes

• ❌ No maximum iteration limit (agent runs forever)
• ❌ Poor error handling (agent crashes on tool failures)
• ❌ Making agents too complex when simple solutions work
• ❌ Not validating tool outputs before using them
• ❌ Ignoring memory (agent forgets important context)

Summary

Agent architecture consists of perception, reasoning, action, memory, and planning components. Design patterns include single-agent, hierarchical, and pipeline architectures. Robust error handling and iteration limits are essential for production agents.

Cross-References

• ReAct → Reasoning + Acting pattern
• Memory → Memory systems in detail
• Planning → Task decomposition
• Tool Calling → Tool integration
• Multi-Agent → Multi-agent systems
• LLM Architecture
• Distributed Messaging