12 Foundational Patterns

Overview

The 12 foundational patterns represent the complete taxonomy of proven agentic system design patterns currently used in production (2025-2026).

This map consolidates:

• Andrew Ng's 4 patterns
• Anthropic's 5 workflow patterns
• 3 additional emergent patterns from production systems

Together, these 12 patterns cover virtually all production agentic systems.

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The 12 Patterns at a Glance

graph TD
 A["12 FOUNDATIONAL AGENTIC PATTERNS<br/>(2025-2026)"] --> B["CORE PATTERNS - 4"]
 B --> B1["1. Reflection"]
 B --> B2["2. Tool Use<br/>Function Calling"]
 B --> B3["3. Planning & Decomposition"]
 B --> B4["4. Routing & Selection"]

 A --> C["WORKFLOW PATTERNS - 5"]
 C --> C1["5. Agentic Loop"]
 C --> C2["6. Retrieval Augmented<br/>Generation RAG"]
 C --> C3["7. Chain-of-Thought<br/>Reasoning"]
 C --> C4["8. Parallelization"]
 C --> C5["9. Multi-Agent<br/>Coordination"]

 A --> D["EMERGENT PATTERNS - 3"]
 D --> D1["10. Human-in-the-Loop"]
 D --> D2["11. Memory & Context<br/>Management"]
 D --> D3["12. Error Recovery &<br/>Resilience"]
```

---

## Core Patterns (1-4)

These are the fundamental building blocks all agents use.

### Pattern 1: Reflection

**What**: Agent generates output, critiques it, then improves

**Why**: Higher quality outputs through self-critique

**Example Use**: Writing, code generation, analysis

**Implementation Cost**: 3x inference (generate, critique, improve)

**Quality Gain**: +20-40% typical

```python
def reflection_loop(task):
 # Generate
 output = llm.generate(task)

 # Critique
 critique = llm.critique(output, task)

 # Improve
 if critique.issues:
 output = llm.improve(output, critique)

 return output
```

**Production Use**: 60% of agents use this

-

### Pattern 2: Tool Use / Function Calling

**What**: Agent calls external tools/APIs based on task needs

**Why**: Agents affect world, not just talk

**Example Use**: Database queries, web search, API calls

**Implementation Cost**: Tool definition and error handling

**Capability Gain**: From chatbot to autonomous agent

```python
def tool_use(goal):
 while not done:
 # Decide which tool
 tool = llm.choose_tool(goal, available_tools)

 # Call it
 result = tools[tool.name](**tool.args)

 # Integrate result
 context += result
```

**Production Use**: 80% of agents use this

---

### Pattern 3: Planning & Decomposition

**What**: Break complex goal into sub-goals before execution

**Why**: Complex tasks fail without planning

**Example Use**: Research tasks, multi-step workflows

**Implementation Cost**: Additional LLM call for planning

**Quality Gain**: +30-50% on complex tasks

```python
def planning_pattern(goal):
 # Decompose
 plan = llm.create_plan(goal)
 # steps = [step1, step2, step3,...]

 # Execute
 results = []
 for step in plan.steps:
 result = execute_step(step)
 results.append(result)

 return aggregate(results)
```

**Production Use**: 70% of agents use this

-

### Pattern 4: Routing & Selection

**What**: Classify request and route to specialist handler

**Why**: Specialists more accurate than generalists

**Example Use**: Customer support, API gateways

**Implementation Cost**: Classification model/logic

**Quality Gain**: +10-30% accuracy

```python
def routing_pattern(request):
 # Classify
 category = classifier.predict(request)

 # Select specialist
 specialist = specialists[category]

 # Handle
 return specialist.handle(request)
```

**Production Use**: 50% of agents use this

---

## Workflow Patterns (5-9)

These patterns describe how work flows through the system.

### Pattern 5: Agentic Loop

**What**: Iterative perceive→reason→act→reflect cycle

**Why**: Handles complex, multi-step tasks

**Characteristics**:

- Autonomous decision-making
- Iteration until goal met
- Reactive to environment

**When**: Multi-step problem-solving

---

### Pattern 6: Retrieval Augmented Generation (RAG)

**What**: Retrieve context before generating response

**Why**: Grounded, hallucination-reduced answers

**Characteristics**:

- Fast (no iteration)
- Document-grounded
- Deterministic

**When**: Knowledge-based Q&A

---

### Pattern 7: Chain-of-Thought Reasoning

**What**: Make reasoning steps explicit

**Why**: Better quality through visible reasoning

**Characteristics**:

- Transparent process
- Better on complex tasks
- Uses more tokens

**When**: Complex reasoning needed

---

### Pattern 8: Parallelization

**What**: Break task into parallel subtasks

**Why**: Speed up execution, gather diverse inputs

**Characteristics**:

- Concurrent execution
- Merge results
- Faster overall

**When**: Independent subtasks available

```python
def parallelization_pattern(task):
 subtasks = decompose_into_independent(task)

 # Run in parallel
 with concurrent.futures.ThreadPoolExecutor() as executor:
 futures = [
 executor.submit(process, subtask)
 for subtask in subtasks
]

 results = [f.result() for f in futures]
 return merge_results(results)
```

**When**:

- Search multiple sources
- Parallel analysis
- Independent sub-goals

-

### Pattern 9: Multi-Agent Coordination

**What**: Multiple agents collaborate on task

**Why**: Specialize work, scale capacity

**Characteristics**:

- Agent specialization
- Communication/coordination
- Scalable

**When**: Large/complex projects

---

## Emergent Patterns (10-12)

These patterns emerged from production systems (2024-2025).

### Pattern 10: Human-in-the-Loop

**What**: Humans retained authority over high-impact decisions

**Why**: Safety, oversight, trust

**Characteristics**:

- Risk-based escalation
- Approval workflows
- Audit trails

**Implementation**:

```python
def hitl_pattern(decision):
 risk = assess_risk(decision)

 if risk < LOW_THRESHOLD:
 execute_autonomous(decision)
 elif risk < HIGH_THRESHOLD:
 approval = wait_for_human_approval(decision)
 if approval:
 execute(decision)
 else:
 escalate_to_human(decision)
```

**Production Adoption**: 85%+ of enterprise systems

---

### Pattern 11: Memory & Context Management

**What**: Maintain and retrieve information across interactions

**Why**: Agents can't fit full history in context window

**Characteristics**:

- Short-term (current task)
- Long-term (vector DB)
- Episodic (what happened)
- Semantic (what to know)

**Implementation**:

```python
class MemoryPattern:
 def __init__(self):
 self.short_term = [] # Current task
 self.episodic = VectorDB() # Past events
 self.semantic = KnowledgeBase() # Facts

 def add_memory(self, event):
 self.episodic.add(event)

 def recall(self, query):
 return self.episodic.search(query)
```

**Production Adoption**: 75%+ of sophisticated agents

-

### Pattern 12: Error Recovery & Resilience

**What**: Graceful handling of failures, recovery strategies

**Why**: Production systems fail; design for it

**Characteristics**:

- Error detection
- Retry strategies
- Fallback paths
- Logging/audit

**Implementation**:

```python
def resilience_pattern(task):
 max_retries = 3
 for attempt in range(max_retries):
 try:
 return execute(task)
 except RecoverableError as e:
 if attempt < max_retries - 1:
 task = adjust_strategy(task)
 continue
 except UnrecoverableError as e:
 return handle_graceful_failure(e)

 return fallback_solution(task)
```

**Production Adoption**: 95%+ of production systems

---

## Pattern Interaction Matrix

Which patterns work together?

```
 1 2 3 4 5 6 7 8 9 10 11 12
 Refl Tool Plan Rout Loop RAG CoT Para Multi HITL Mem Err

1. Reflection -
2. Tool Use -
3. Planning -
4. Routing -
5. Loop -
6. RAG -
7. CoT -
8. Parallel -
9. Multi -
10. HITL -
11. Memory -
12. Error -

Legend: = Works together, = Strong synergy
```

**Key Synergies**:

- Tool Use + Planning = Powerful agents
- Agentic Loop + Memory = Stateful agents
- Multi-Agent + HITL = Enterprise systems
- RAG + Error Recovery = Robust systems

---

## Common Pattern Combinations

### 1. Simple Agent (Most Common 2025)

```
Tool Use + Planning + Error Recovery
```

- 60% of production agents
- Autonomous with guardrails
- Example: Customer service bot

### 2. Advanced Agent

```
Agentic Loop + Tool Use + Planning + Memory + Error Recovery
```

- 25% of production agents
- Complex task handling
- Example: Research assistant

### 3. Team Agent

```
Multi-Agent + Routing + Coordination + HITL
```

- 10% of production agents
- Large projects
- Example: Enterprise research team

### 4. RAG-Based

```
RAG + Tool Use + Error Recovery
```

- 20% of production agents
- Knowledge-based systems
- Example: Documentation bot

### 5. Enterprise (Full Stack)

```
All 12 patterns combined
```

- <5% of production agents (most complex)
- Mission-critical systems
- Example: Full enterprise AI platform

---

## Pattern Adoption in 2025-2026

### Current Usage (Production Systems)

| Pattern| Usage| Trend|
|---------|-------|-------|
| 1. Reflection| 60%| Growing|
| 2. Tool Use| 80%| Growing|
| 3. Planning| 70%| Stable|
| 4. Routing| 50%| Growing|
| 5. Agentic Loop| 55%| Growing|
| 6. RAG| 65%| Stable|
| 7. CoT| 45%| Stable|
| 8. Parallelization| 40%| Growing|
| 9. Multi-Agent| 30%| Growing Fast|
| 10. HITL| 85%| Growing|
| 11. Memory| 75%| Growing|
| 12. Error Recovery| 95%| Stable|

---

## Evolution Over Time

### 2023: Foundation

```
Tool Use + Planning + Error Recovery
```

→ Simple working agents

### 2024: Sophistication

```
+ Reflection + Memory + Agentic Loop + HITL
```

→ Production-ready agents

### 2025-2026: Standardization

```
All 12 patterns in various combinations
```

→ Enterprises deploy sophisticated systems
→ Multi-agent systems emerge
→ Specialization increases

---

## Decision Framework: Which Patterns Do I Need?

### Step 1: Understand Your Task

- **Simple**: Classification, simple Q&A
- **Complex**: Multi-step, exploration needed
- **Collaborative**: Requires team coordination

### Step 2: Check Requirements

- Speed needed? → Use RAG, minimal loop
- Quality paramount? → Add Reflection, CoT
- Takes action? → Add Tool Use
- Needs learning? → Add Memory
- Team effort? → Add Multi-Agent + HITL

### Step 3: Start Minimal, Add As Needed

```
Start: Tool Use + Error Recovery

If quality issues → Add Reflection
If multi-step → Add Planning
If exploration → Add Agentic Loop
If specialization → Add Routing
If team → Add Multi-Agent + HITL
If grounding → Add RAG
If reasoning → Add CoT
If parallel → Add Parallelization
If memory → Add Memory pattern
```

---

## Pattern Anti-Patterns (What NOT to Do)

**Every pattern at once** - Complexity explosion
**No error handling** - Production will fail
**Reflection everywhere** - 3x cost, not always needed
**No memory** - Can't learn or maintain state
**Always agentic loop** - Sometimes RAG is faster
**No routing** - Generalist agents underperform
**No HITL** - Lost user trust

---

## Metrics by Pattern

### Reflection

- Cost multiplier: 3x
- Quality gain: +30%
- Use when: Quality > Speed

### Tool Use

- Capability gain: 10x (chatbot → agent)
- Error rate: -50% (with proper error handling)
- Use when: Need to take action

### Planning

- Quality gain: +50% (complex tasks)
- Speed: -20% (planning overhead)
- Use when: Multi-step, complex

### Routing

- Accuracy gain: +20-30%
- Latency: Minimal
- Use when: Multiple task types

### Agentic Loop

- Latency: Variable (multiple iterations)
- Quality: +40-60% (complex tasks)
- Use when: Exploration needed

### RAG

- Hallucination reduction: 80%+
- Latency: <1s
- Use when: Document-based

### Memory

- Context efficiency: 5-10x better
- Learning capability: +70%
- Use when: Multi-turn, learning needed

### HITL

- User trust: +90%
- Escalation rate: 5-15%
- Cost: Human time overhead
- Use when: High risk, compliance needed

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## Next: Read [Pattern Selection Framework](/01-agent-design/02-core-design-patterns/04-pattern-selection-framework/)

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**Last Updated**: August 9, 2026