Setup and context
As AI agent technology rapidly evolves, the question "Which design pattern fits my use case?" haunts engineering teams everywhere. There isn't a single optimal agent design—effectiveness depends on task complexity, reliability requirements, and latency constraints.
This guide explores four major design patterns used in production environments, examining their strengths, weaknesses, and implementation details. Rather than academic theory, this is practical wisdom from enterprise deployments.
The AI Agent Design Landscape
Before exploring patterns, let's define what makes an agent distinct from related technologies.
Agents vs Chatbots vs LLMs
| Characteristic | LLM | Chatbot | Agent |
|---|---|---|---|
| Autonomy | None | Low (script-dependent) | High (self-directed) |
| Tool Integration | Impossible | Limited | Full support |
| State Management | None | Basic | Complex state machine |
| Reasoning Loop | Single-turn | Sequential | Iterative & adaptive |
| Production Readiness | Low | Medium | High |
An agent's essence is the ability to interact with external systems (APIs, databases, user input) while autonomously selecting actions toward a goal.
Pattern 1: Reactive Pattern
The simplest design: Input → Analyze → Act → Return Result in a single loop.
Architecture
User Input
↓
LLM Analyzes Situation
↓
Selects Optimal Tool
↓
Executes Tool
↓
Returns Result
The LLM immediately selects and executes an appropriate tool without planning ahead. Simple, fast, but inflexible for multi-step tasks.
Implementation Example
class ReactiveAgent:
def __init__(self, llm, tools):
self.llm = llm
self.tools = {t.name: t for t in tools}
def run(self, user_input):
# Request tool selection
prompt = f"""
Available tools: {list(self.tools.keys())}
User request: {user_input}
Select the single best tool and explain why.
Format: TOOL: [name], REASON: [reason]
"""
response = self.llm.generate(prompt)
tool_name = self._extract_tool(response)
# Execute
if tool_name in self.tools:
return self.tools[tool_name].execute(user_input)
return "Tool not available"Strengths
- Minimal implementation complexity
- Lowest latency (single LLM call)
- Easy to debug
Limitations
- Cannot handle multi-step workflows
- No error recovery mechanisms
- Poor context retention
Best For
✓ Simple information retrieval ("What's the weather?") ✓ Quick calculations or data transformations ✗ Customer support ticket resolution ✗ Complex report generation
Pattern 2: Planning Pattern
Decompose complex tasks into multiple steps, then execute sequentially with explicit planning.
Architecture
Input → Decompose into Steps → Create Plan → Execute Each Step → Aggregate Results
The LLM creates a "to-do list" first, then works through it systematically. Slower than Reactive but handles complexity better.
Implementation Example
class PlanningAgent:
def __init__(self, llm, tools):
self.llm = llm
self.tools = {t.name: t for t in tools}
def run(self, user_input):
# Step 1: Decompose
decompose_prompt = f"""
User task: {user_input}
List the minimum steps needed.
Format: 1. [Step description], 2. [Step description], ...
"""
decomposed = self.llm.generate(decompose_prompt)
steps = self._parse_steps(decomposed)
# Step 2: Execute each step
context = {}
for i, step in enumerate(steps):
plan_prompt = f"""
Current step: {step}
Prior results: {context}
Available tools: {list(self.tools.keys())}
Select a tool and parameters.
Format: TOOL: [name], PARAMS: [params]
"""
response = self.llm.generate(plan_prompt)
tool_name, params = self._parse_tool_call(response)
if tool_name in self.tools:
result = self.tools[tool_name].execute(**params)
context[f"step_{i}"] = result
return contextStrengths
- Handles multi-step workflows
- Execution plan is verifiable
- Error location is obvious
Limitations
- Extra LLM calls increase cost & latency
- Inflexible when circumstances change
- Plan correction is complex
Best For
✓ Report generation from multiple sources ✓ Complex information retrieval and aggregation ✗ Real-time conversational responses ✗ Dynamic environments
Pattern 3: Learning Pattern
Agents learn from past successes and failures, improving decision quality over time.
Architecture
Execute → Evaluate Results → Record Feedback → Apply to Next Decision
Each action result is scored and stored, allowing the agent to improve through experience.
Implementation Example
class LearningAgent:
def __init__(self, llm, tools, memory_store):
self.llm = llm
self.tools = {t.name: t for t in tools}
self.memory = memory_store
self.feedback_history = []
def run(self, user_input, learn=True):
# Find similar past successes
similar_cases = self.memory.find_similar(user_input)
few_shot_examples = self._format_examples(similar_cases)
prompt = f"""
Successful precedents:
{few_shot_examples}
New task: {user_input}
Based on success patterns, recommend an action.
"""
response = self.llm.generate(prompt)
action = self._parse_action(response)
result = self._execute_action(action)
# Record for future learning
if learn:
feedback = self._evaluate_result(result)
self.feedback_history.append({
'task': user_input,
'action': action,
'result': result,
'feedback': feedback
})
if feedback['success']:
self.memory.add(user_input, action)
return resultStrengths
- Improves over time
- Learns domain-specific optimal solutions
- Automatic error recovery
Limitations
- Risk of learning bad patterns early
- Memory management becomes complex
- Past knowledge may not transfer to new situations
Best For
✓ Customer support chatbots ✓ Iterative decision-making tasks ✗ One-time tasks ✗ Rapidly changing domains
Pattern 4: Agentic Loop Pattern
The most sophisticated design: Think → Act → Observe → Think Again in a cycle until goal achievement.
Architecture
↓ Think
Observe ← Act
↓
(repeat until goal achieved)
The agent introspects, executes actions, observes outcomes, and adjusts continuously. This matches Claude's "extended thinking" and represents the state-of-the-art.
Implementation Example
class AgenticLoopAgent:
def __init__(self, llm, tools, max_iterations=10):
self.llm = llm
self.tools = {t.name: t for t in tools}
self.max_iterations = max_iterations
self.history = []
def run(self, user_input):
goal = user_input
iteration = 0
while iteration < self.max_iterations:
# Think about next action
thinking_prompt = f"""
Goal: {goal}
Prior observations:
{self._format_history()}
Current status: {self._assess_progress()}
Plan the next action in detail.
Format:
[Reasoning]
NEXT_ACTION: [action]
RATIONALE: [why]
EXPECTED_OUTCOME: [expected result]
"""
response = self.llm.generate(thinking_prompt)
action = self._parse_action(response)
# Act and observe
observation = self._execute_action(action)
self.history.append({
'iteration': iteration,
'thought': response,
'action': action,
'observation': observation
})
# Check goal achievement
if self._check_goal_achieved(goal, observation):
return {
'success': True,
'result': observation,
'iterations': iteration + 1
}
iteration += 1
return {
'success': False,
'reason': 'Max iterations reached',
'iterations': iteration
}Strengths
- Highest autonomy and adaptability
- Handles unpredictable situations
- Ideal for complex problem-solving
Limitations
- Many LLM calls (cost & latency)
- Debugging is difficult
- Goal verification can be tricky
Best For
✓ Complex research and analysis ✓ Decision-making in uncertain environments ✓ Creative problem-solving
Pattern Selection Matrix
| Situation | Recommended |
|---|---|
| Low complexity | Reactive |
| Multi-step & plannable | Planning |
| Iterative & learnable | Learning |
| Complex & unpredictable | Agentic Loop |
| Speed critical | Reactive / Planning |
| Reliability critical | Planning / Agentic Loop |
| Cost sensitive | Reactive |
Production Best Practices
Error Handling with Fallback
class RobustAgent:
def run_with_fallback(self, user_input):
try:
return self._agentic_loop(user_input)
except ToolError as e:
logger.warning(f"Agentic loop failed: {e}, trying planning")
return self._planning_agent(user_input)
except Exception as e:
logger.error(f"Planning failed: {e}, falling back to reactive")
return self._reactive_agent(user_input)Memory Management
class MemoryEfficientAgent:
def __init__(self, max_history=50):
self.history = []
self.max_history = max_history
def add_to_history(self, entry):
self.history.append(entry)
if len(self.history) > self.max_history:
# Preserve important entries
important = [e for e in self.history if e.get('importance', 0) > 0.5]
recent = self.history[-10:]
self.history = important + recentCost Optimization
class CostOptimizedAgent:
def select_model(self, complexity):
if complexity < 3:
return "claude-3-haiku" # Cheapest
elif complexity < 7:
return "claude-3-sonnet" # Balanced
else:
return "claude-3-opus" # Best qualitySummary
Agent design success depends on choosing the right pattern for your constraints.
Key Takeaways
- Reactive: Simple & fast, limited functionality
- Planning: Multi-step support with explicit planning
- Learning: Continuous improvement through experience
- Agentic Loop: Maximum autonomy, highest complexity
No pattern is universally superior—match your pattern to your requirements. Most real-world systems benefit from hybrid approaches (e.g., Planning + Learning).
In production, never forget error handling, memory management, and cost optimization. Continuous refinement is essential.