Three AI Agent Architecture Patterns: When to Use Each One

Building AI agents requires choosing the right architectural foundation. The wrong choice leads to systems that are either too rigid for complex tasks or too complex for simple ones.

Here are three proven patterns, when to use them, and how they work in practice.

Reactive Agents: Fast and Simple

Reactive agents respond directly to inputs without maintaining internal state or complex reasoning chains. They follow condition-action rules: when X happens, do Y.

When to Use Reactive Agents

Simple, well-defined tasks: Customer service chatbots answering FAQ questions
Real-time requirements: Trading systems that need millisecond responses
Predictable environments: Content moderation with clear policy rules
Limited compute resources: Edge devices or mobile applications

Architecture Components

Input → Rule Engine → Action

Sensors: Capture environmental input (user messages, API calls, events)
Rule Engine: Maps inputs to actions using if-then logic
Actuators: Execute responses (send messages, make API calls, trigger workflows)

Example: Content Moderation Bot

A reactive content moderation agent processes social media posts:

def moderate_content(post):
    if contains_profanity(post.text):
        return "BLOCK"
    elif contains_spam_keywords(post.text):
        return "FLAG_FOR_REVIEW"
    elif post.engagement_rate > 0.9:
        return "PROMOTE"
    else:
        return "ALLOW"

The agent examines each post independently, applies rules, and takes action. No memory of previous posts affects current decisions.

Strengths: Fast execution, predictable behavior, easy to debug and modify Weaknesses: Cannot handle complex reasoning, no learning from experience, limited context awareness

Deliberative Agents: Planning and Reasoning

Deliberative agents maintain internal models of their environment and plan sequences of actions to achieve goals. They think before they act.

When to Use Deliberative Agents

Multi-step tasks: Research assistants gathering information from multiple sources
Complex goal achievement: Project management agents coordinating team workflows
Uncertain environments: Investment advisors analyzing market conditions
Long-term optimization: Supply chain agents balancing cost, speed, and quality

Architecture Components

Input → World Model → Planner → Executor → Output
              ↑           ↓
          Knowledge Base ← Monitor

World Model: Internal representation of the current state
Knowledge Base: Facts, rules, and learned information
Planner: Generates action sequences to achieve goals
Monitor: Tracks plan execution and environmental changes
Executor: Carries out planned actions

Example: Research Assistant Agent

A deliberative research agent helps users find and synthesize information:

Goal Formation: "Find recent studies on renewable energy efficiency"
Planning:
- Search academic databases
- Filter by publication date (last 2 years)
- Extract key findings
- Synthesize common themes
Execution: Execute search queries, process results, generate summary
Monitoring: Check if results meet quality thresholds, adjust search if needed

The agent maintains context throughout the process, remembers what it has already searched, and can revise its plan based on intermediate results.

Strengths: Handles complex tasks, adapts to changing conditions, optimizes for long-term goals Weaknesses: Slower response times, higher computational requirements, can over-plan simple tasks

Hybrid Agents: Best of Both Worlds

Hybrid agents combine reactive and deliberative components, using fast reactive responses for routine situations and deliberative planning for complex scenarios.

When to Use Hybrid Agents

Mixed task complexity: Customer support handling both simple FAQs and complex technical issues
Performance requirements: Systems needing both speed and sophistication
Evolving environments: Agents that encounter both routine and novel situations
Resource constraints: Applications that must balance response time with capability

Architecture Components

Input → Classifier → Reactive Engine → Fast Response
          ↓
    Deliberative Engine → Planned Response

Input Classifier: Determines whether to use reactive or deliberative processing
Reactive Layer: Handles simple, frequent requests
Deliberative Layer: Processes complex requests requiring planning
Coordination Layer: Manages handoffs between layers

Example: Technical Support Agent

A hybrid technical support agent triages and handles customer requests:

Reactive Layer (handles 70% of requests):

Password resets: "Click 'Forgot Password' and check your email"
Account balance: Query database and return formatted response
Common errors: Lookup error code and return standard solution

Deliberative Layer (handles 30% of requests):

Complex technical issues requiring multi-step diagnosis
Account problems needing investigation across multiple systems
Feature requests requiring analysis and routing to development teams

Classification Logic:

def classify_request(user_input):
    confidence = intent_classifier.predict(user_input)
    if confidence > 0.8 and intent in REACTIVE_INTENTS:
        return "reactive"
    else:
        return "deliberative"

The agent provides immediate responses for routine requests while still handling complex cases that require reasoning and planning.

Strengths: Balances speed and capability, adapts to request complexity, efficient resource usage Weaknesses: More complex to build and maintain, requires good classification logic

Choosing the Right Pattern

Consider these factors when selecting an architecture:

Task Complexity

Simple, rule-based tasks: Reactive
Multi-step, goal-oriented tasks: Deliberative
Mixed complexity: Hybrid

Performance Requirements

Sub-second response times: Reactive or Hybrid with good classification
Complex reasoning acceptable: Deliberative
Variable response times: Hybrid

Environmental Predictability

Stable, well-defined rules: Reactive
Changing conditions requiring adaptation: Deliberative
Mix of routine and novel situations: Hybrid

Resource Constraints

Limited compute/memory: Reactive
Ample resources for complex processing: Deliberative
Need to optimize resource usage: Hybrid

Implementation Tips

Start simple: Begin with reactive agents and add complexity only when needed
Measure performance: Track response times, accuracy, and resource usage
Plan for evolution: Design systems that can incorporate additional patterns over time
Test edge cases: Ensure agents handle unexpected inputs gracefully
Monitor in production: Watch for patterns that might indicate the need for architectural changes

The right architecture depends on your specific requirements. Most successful AI agent systems evolve from simple reactive implementations toward hybrid architectures as complexity and requirements grow.