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+# AI Agent Workflow Pattern
+
+**Use Case**: Build AI agents with tool access, memory, and reasoning capabilities.
+
+---
+
+## Pattern Structure
+
+```
+Trigger → AI Agent (Model + Tools + Memory) → [Process Response] → Output
+```
+
+**Key Characteristic**: AI-powered decision making with tool use
+
+---
+
+## Core AI Connection Types
+
+n8n supports **8 AI connection types** for building agent workflows:
+
+1. **ai_languageModel** - The LLM (OpenAI, Anthropic, etc.)
+2. **ai_tool** - Functions the agent can call
+3. **ai_memory** - Conversation context
+4. **ai_outputParser** - Parse structured outputs
+5. **ai_embedding** - Vector embeddings
+6. **ai_vectorStore** - Vector database
+7. **ai_document** - Document loaders
+8. **ai_textSplitter** - Text chunking
+
+---
+
+## Core Components
+
+### 1. Trigger
+**Options**:
+- **Webhook** - Chat interfaces, API calls (most common)
+- **Manual** - Testing and development
+- **Schedule** - Periodic AI tasks
+
+### 2. AI Agent Node
+**Purpose**: Orchestrate LLM with tools and memory
+
+**Configuration**:
+```javascript
+{
+  agent: "conversationalAgent",  // or "openAIFunctionsAgent"
+  promptType: "define",
+  text: "You are a helpful assistant that can search docs, query databases, and send emails."
+}
+```
+
+**Connections**:
+- **ai_languageModel input** - Connected to LLM node
+- **ai_tool inputs** - Connected to tool nodes
+- **ai_memory input** - Connected to memory node (optional)
+
+### 3. Language Model
+**Available providers**:
+- OpenAI (GPT-4, GPT-3.5)
+- Anthropic (Claude)
+- Google (Gemini)
+- Local models (Ollama, LM Studio)
+
+**Example** (OpenAI Chat Model):
+```javascript
+{
+  model: "gpt-4",
+  temperature: 0.7,
+  maxTokens: 1000
+}
+```
+
+### 4. Tools (ANY Node Can Be a Tool!)
+**Critical insight**: Connect ANY n8n node to agent via `ai_tool` port
+
+**Common tool types**:
+- HTTP Request - Call APIs
+- Database nodes - Query data
+- Code - Custom functions
+- Search nodes - Web/document search
+- Pre-built tool nodes (Calculator, Wikipedia, etc.)
+
+### 5. Memory (Optional but Recommended)
+**Purpose**: Maintain conversation context
+
+**Types**:
+- **Buffer Memory** - Store recent messages
+- **Window Buffer Memory** - Store last N messages
+- **Summary Memory** - Summarize conversation
+
+### 6. Output Processing
+**Purpose**: Format AI response for delivery
+
+**Common patterns**:
+- Return directly (chat response)
+- Store in database (conversation history)
+- Send to communication channel (Slack, email)
+
+---
+
+## Common Use Cases
+
+### 1. Conversational Chatbot
+**Flow**: Webhook (chat message) → AI Agent → Webhook Response
+
+**Example** (Customer support bot):
+```
+1. Webhook (path: "chat", POST)
+   - Receives: {user_id, message, session_id}
+
+2. Window Buffer Memory (load context by session_id)
+
+3. AI Agent
+   ├─ OpenAI Chat Model (gpt-4)
+   ├─ HTTP Request Tool (search knowledge base)
+   ├─ Database Tool (query customer orders)
+   └─ Window Buffer Memory (conversation context)
+
+4. Code (format response)
+
+5. Webhook Response (send reply)
+```
+
+**AI Agent prompt**:
+```
+You are a customer support assistant.
+You can:
+1. Search the knowledge base for answers
+2. Look up customer orders
+3. Provide shipping information
+
+Be helpful and professional.
+```
+
+### 2. Document Q&A
+**Flow**: Upload docs → Embed → Store → Query with AI
+
+**Example** (Internal documentation assistant):
+```
+Setup Phase (run once):
+1. Read Files (load documentation)
+2. Text Splitter (chunk into paragraphs)
+3. Embeddings (OpenAI Embeddings)
+4. Vector Store (Pinecone/Qdrant) (store vectors)
+
+Query Phase (recurring):
+1. Webhook (receive question)
+2. AI Agent
+   ├─ OpenAI Chat Model (gpt-4)
+   ├─ Vector Store Tool (search similar docs)
+   └─ Buffer Memory (context)
+3. Webhook Response (answer with citations)
+```
+
+### 3. Data Analysis Assistant
+**Flow**: Request → AI Agent (with data tools) → Analysis → Visualization
+
+**Example** (SQL analyst agent):
+```
+1. Webhook (data question: "What were sales last month?")
+
+2. AI Agent
+   ├─ OpenAI Chat Model (gpt-4)
+   ├─ Postgres Tool (execute queries)
+   └─ Code Tool (data analysis)
+
+3. Code (generate visualization data)
+
+4. Webhook Response (answer + chart data)
+```
+
+**Postgres Tool Configuration**:
+```javascript
+{
+  name: "query_database",
+  description: "Execute SQL queries to analyze sales data. Use SELECT queries only.",
+  // Node executes AI-generated SQL
+}
+```
+
+### 4. Workflow Automation Agent
+**Flow**: Command → AI Agent → Execute actions → Report
+
+**Example** (DevOps assistant):
+```
+1. Slack (slash command: /deploy production)
+
+2. AI Agent
+   ├─ OpenAI Chat Model (gpt-4)
+   ├─ HTTP Request Tool (GitHub API)
+   ├─ HTTP Request Tool (Deploy API)
+   └─ Postgres Tool (deployment logs)
+
+3. Agent actions:
+   - Check if tests passed
+   - Create deployment
+   - Log deployment
+   - Notify team
+
+4. Slack (deployment status)
+```
+
+### 5. Email Processing Agent
+**Flow**: Email received → AI Agent → Categorize → Route → Respond
+
+**Example** (Support ticket router):
+```
+1. Email Trigger (new support email)
+
+2. AI Agent
+   ├─ OpenAI Chat Model (gpt-4)
+   ├─ Vector Store Tool (search similar tickets)
+   └─ HTTP Request Tool (create Jira ticket)
+
+3. Agent actions:
+   - Categorize urgency (low/medium/high)
+   - Find similar past tickets
+   - Create ticket in appropriate project
+   - Draft response
+
+4. Email (send auto-response)
+5. Slack (notify assigned team)
+```
+
+---
+
+## Tool Configuration
+
+### Making ANY Node an AI Tool
+
+**Critical concept**: Any n8n node can become an AI tool!
+
+**Requirements**:
+1. Connect node to AI Agent via `ai_tool` port (NOT main port)
+2. Configure tool name and description
+3. Define input schema (optional)
+
+**Example** (HTTP Request as tool):
+```javascript
+{
+  // Tool metadata (for AI)
+  name: "search_github_issues",
+  description: "Search GitHub issues by keyword. Returns issue titles and URLs.",
+
+  // HTTP Request configuration
+  method: "GET",
+  url: "https://api.github.com/search/issues",
+  sendQuery: true,
+  queryParameters: {
+    "q": "={{$json.query}} repo:{{$json.repo}}",
+    "per_page": "5"
+  }
+}
+```
+
+**How it works**:
+1. AI Agent sees tool: `search_github_issues(query, repo)`
+2. AI decides to use it: `search_github_issues("bug", "n8n-io/n8n")`
+3. n8n executes HTTP Request with parameters
+4. Result returned to AI Agent
+5. AI Agent processes result and responds
+
+### Pre-built Tool Nodes
+
+**Available in @n8n/n8n-nodes-langchain**:
+
+- **Calculator Tool** - Math operations
+- **Wikipedia Tool** - Wikipedia search
+- **Serper Tool** - Google search
+- **Wolfram Alpha Tool** - Computational knowledge
+- **Custom Tool** - Define with Code node
+
+**Example** (Calculator Tool):
+```
+AI Agent
+  ├─ OpenAI Chat Model
+  └─ Calculator Tool (ai_tool connection)
+
+User: "What's 15% of 2,847?"
+AI: *uses calculator tool* → "426.05"
+```
+
+### Database as Tool
+
+**Pattern**: Postgres/MySQL node connected as ai_tool
+
+**Configuration**:
+```javascript
+{
+  // Tool metadata
+  name: "query_customers",
+  description: "Query customer database. Use SELECT queries to find customer information by email, name, or ID.",
+
+  // Postgres config
+  operation: "executeQuery",
+  query: "={{$json.sql}}",  // AI provides SQL
+  // Security: Use read-only database user!
+}
+```
+
+**Safety**: Create read-only DB user for AI tools!
+
+```sql
+CREATE USER ai_readonly WITH PASSWORD 'secure_password';
+GRANT SELECT ON customers, orders TO ai_readonly;
+-- NO INSERT, UPDATE, DELETE access
+```
+
+### Code Node as Tool
+
+**Pattern**: Custom Python/JavaScript function
+
+**Example** (Data processor):
+```javascript
+// Tool metadata
+{
+  name: "process_csv",
+  description: "Process CSV data and return statistics. Input: csv_string"
+}
+
+// Code node
+const csv = $input.first().json.csv_string;
+const lines = csv.split('\n');
+const data = lines.slice(1).map(line => line.split(','));
+
+return [{
+  json: {
+    row_count: data.length,
+    columns: lines[0].split(','),
+    summary: {
+      // Calculate statistics
+    }
+  }
+}];
+```
+
+---
+
+## Memory Configuration
+
+### Buffer Memory
+**Stores all messages** (until cleared)
+
+```javascript
+{
+  memoryType: "bufferMemory",
+  sessionKey: "={{$json.body.user_id}}"  // Per-user memory
+}
+```
+
+### Window Buffer Memory
+**Stores last N messages** (recommended)
+
+```javascript
+{
+  memoryType: "windowBufferMemory",
+  sessionKey: "={{$json.body.session_id}}",
+  contextWindowLength: 10  // Last 10 messages
+}
+```
+
+### Summary Memory
+**Summarizes old messages** (for long conversations)
+
+```javascript
+{
+  memoryType: "summaryMemory",
+  sessionKey: "={{$json.body.session_id}}",
+  maxTokenLimit: 2000
+}
+```
+
+**How it works**:
+1. Conversation grows beyond limit
+2. AI summarizes old messages
+3. Summary stored, old messages discarded
+4. Saves tokens while maintaining context
+
+---
+
+## Agent Types
+
+### 1. Conversational Agent
+**Best for**: General chat, customer support
+
+**Features**:
+- Natural conversation flow
+- Memory integration
+- Tool use with reasoning
+
+**When to use**: Most common use case
+
+### 2. OpenAI Functions Agent
+**Best for**: Tool-heavy workflows, structured outputs
+
+**Features**:
+- Optimized for function calling
+- Better tool selection
+- Structured responses
+
+**When to use**: Multiple tools, need reliable tool calling
+
+### 3. ReAct Agent
+**Best for**: Step-by-step reasoning
+
+**Features**:
+- Think → Act → Observe loop
+- Visible reasoning process
+- Good for debugging
+
+**When to use**: Complex multi-step tasks
+
+---
+
+## Prompt Engineering for Agents
+
+### System Prompt Structure
+```
+You are a [ROLE].
+
+You can:
+- [CAPABILITY 1]
+- [CAPABILITY 2]
+- [CAPABILITY 3]
+
+Guidelines:
+- [GUIDELINE 1]
+- [GUIDELINE 2]
+
+Format:
+- [OUTPUT FORMAT]
+```
+
+### Example (Customer Support)
+```
+You are a customer support assistant for Acme Corp.
+
+You can:
+- Search the knowledge base for answers
+- Look up customer orders and shipping status
+- Create support tickets for complex issues
+
+Guidelines:
+- Be friendly and professional
+- If you don't know something, say so and offer to create a ticket
+- Always verify customer identity before sharing order details
+
+Format:
+- Keep responses concise
+- Use bullet points for multiple items
+- Include relevant links when available
+```
+
+### Example (Data Analyst)
+```
+You are a data analyst assistant with access to the company database.
+
+You can:
+- Query sales, customer, and product data
+- Perform data analysis and calculations
+- Generate summary statistics
+
+Guidelines:
+- Write efficient SQL queries (always use LIMIT)
+- Explain your analysis methodology
+- Highlight important trends or anomalies
+- Use read-only queries (SELECT only)
+
+Format:
+- Provide numerical answers with context
+- Include query used (for transparency)
+- Suggest follow-up analyses when relevant
+```
+
+---
+
+## Error Handling
+
+### Pattern 1: Tool Execution Errors
+```
+AI Agent (continueOnFail on tool nodes)
+  → IF (tool error occurred)
+    └─ Code (log error)
+    └─ Webhook Response (user-friendly error)
+```
+
+### Pattern 2: LLM API Errors
+```
+Main Workflow:
+  AI Agent → Process Response
+
+Error Workflow:
+  Error Trigger
+    → IF (rate limit error)
+      └─ Wait → Retry
+    → ELSE
+      └─ Notify Admin
+```
+
+### Pattern 3: Invalid Tool Outputs
+```javascript
+// Code node - validate tool output
+const result = $input.first().json;
+
+if (!result || !result.data) {
+  throw new Error('Tool returned invalid data');
+}
+
+return [{ json: result }];
+```
+
+---
+
+## Performance Optimization
+
+### 1. Choose Right Model
+```
+Fast & cheap: GPT-3.5-turbo, Claude 3 Haiku
+Balanced: GPT-4, Claude 3 Sonnet
+Powerful: GPT-4-turbo, Claude 3 Opus
+```
+
+### 2. Limit Context Window
+```javascript
+{
+  memoryType: "windowBufferMemory",
+  contextWindowLength: 5  // Only last 5 messages
+}
+```
+
+### 3. Optimize Tool Descriptions
+```javascript
+// ❌ Vague
+description: "Search for things"
+
+// ✅ Clear and concise
+description: "Search GitHub issues by keyword and repository. Returns top 5 matching issues with titles and URLs."
+```
+
+### 4. Cache Embeddings
+For document Q&A, embed documents once:
+
+```
+Setup (run once):
+  Documents → Embed → Store in Vector DB
+
+Query (fast):
+  Question → Search Vector DB → AI Agent
+```
+
+### 5. Async Tools for Slow Operations
+```
+AI Agent → [Queue slow tool request]
+       → Return immediate response
+       → [Background: Execute tool + notify when done]
+```
+
+---
+
+## Security Considerations
+
+### 1. Read-Only Database Tools
+```sql
+-- Create limited user for AI tools
+CREATE USER ai_agent_ro WITH PASSWORD 'secure';
+GRANT SELECT ON public.* TO ai_agent_ro;
+-- NO write access!
+```
+
+### 2. Validate Tool Inputs
+```javascript
+// Code node - validate before execution
+const query = $json.query;
+
+if (query.toLowerCase().includes('drop ') ||
+    query.toLowerCase().includes('delete ') ||
+    query.toLowerCase().includes('update ')) {
+  throw new Error('Invalid query - write operations not allowed');
+}
+```
+
+### 3. Rate Limiting
+```
+Webhook → IF (check user rate limit)
+        ├─ [Within limit] → AI Agent
+        └─ [Exceeded] → Error (429 Too Many Requests)
+```
+
+### 4. Sanitize User Input
+```javascript
+// Code node
+const userInput = $json.body.message
+  .trim()
+  .substring(0, 1000);  // Max 1000 chars
+
+return [{ json: { sanitized: userInput } }];
+```
+
+### 5. Monitor Tool Usage
+```
+AI Agent → Log Tool Calls
+        → IF (suspicious pattern)
+          └─ Alert Admin + Pause Agent
+```
+
+---
+
+## Testing AI Agents
+
+### 1. Start with Manual Trigger
+Replace webhook with manual trigger:
+```
+Manual Trigger
+  → Set (mock user input)
+  → AI Agent
+  → Code (log output)
+```
+
+### 2. Test Tools Independently
+Before connecting to agent:
+```
+Manual Trigger → Tool Node → Verify output format
+```
+
+### 3. Test with Standard Questions
+Create test suite:
+```
+1. "Hello" - Test basic response
+2. "Search for bug reports" - Test tool calling
+3. "What did I ask before?" - Test memory
+4. Invalid input - Test error handling
+```
+
+### 4. Monitor Token Usage
+```javascript
+// Code node - log token usage
+console.log('Input tokens:', $node['AI Agent'].json.usage.input_tokens);
+console.log('Output tokens:', $node['AI Agent'].json.usage.output_tokens);
+```
+
+### 5. Test Edge Cases
+- Empty input
+- Very long input
+- Tool returns no results
+- Tool returns error
+- Multiple tool calls in sequence
+
+---
+
+## Common Gotchas
+
+### 1. ❌ Wrong: Connecting tools to main port
+```
+HTTP Request → AI Agent  // Won't work as tool!
+```
+
+### ✅ Correct: Use ai_tool connection type
+```
+HTTP Request --[ai_tool]--> AI Agent
+```
+
+### 2. ❌ Wrong: Vague tool descriptions
+```
+description: "Get data"  // AI won't know when to use this
+```
+
+### ✅ Correct: Specific descriptions
+```
+description: "Query customer orders by email address. Returns order ID, status, and shipping info."
+```
+
+### 3. ❌ Wrong: No memory for conversations
+```
+Every message is standalone - no context!
+```
+
+### ✅ Correct: Add memory
+```
+Window Buffer Memory --[ai_memory]--> AI Agent
+```
+
+### 4. ❌ Wrong: Giving AI write access
+```
+Postgres (full access) as tool  // AI could DELETE data!
+```
+
+### ✅ Correct: Read-only access
+```
+Postgres (read-only user) as tool  // Safe
+```
+
+### 5. ❌ Wrong: Unbounded tool responses
+```
+Tool returns 10MB of data → exceeds token limit
+```
+
+### ✅ Correct: Limit tool output
+```javascript
+{
+  query: "SELECT * FROM table LIMIT 10"  // Only 10 rows
+}
+```
+
+---
+
+## Real Template Examples
+
+From n8n template library (234 AI templates):
+
+**Simple Chatbot**:
+```
+Webhook → AI Agent (GPT-4 + Memory) → Webhook Response
+```
+
+**Document Q&A**:
+```
+Setup: Files → Embed → Vector Store
+Query: Webhook → AI Agent (GPT-4 + Vector Store Tool) → Response
+```
+
+**SQL Analyst**:
+```
+Webhook → AI Agent (GPT-4 + Postgres Tool) → Format → Response
+```
+
+Use `search_templates({query: "ai agent"})` to find more!
+
+---
+
+## Checklist for AI Agent Workflows
+
+### Planning
+- [ ] Define agent purpose and capabilities
+- [ ] List required tools (APIs, databases, etc.)
+- [ ] Design conversation flow
+- [ ] Plan memory strategy (per-user, per-session)
+- [ ] Consider token costs
+
+### Implementation
+- [ ] Choose appropriate LLM model
+- [ ] Write clear system prompt
+- [ ] Connect tools via ai_tool ports (NOT main)
+- [ ] Add tool descriptions
+- [ ] Configure memory (Window Buffer recommended)
+- [ ] Test each tool independently
+
+### Security
+- [ ] Use read-only database access for tools
+- [ ] Validate tool inputs
+- [ ] Sanitize user inputs
+- [ ] Add rate limiting
+- [ ] Monitor for abuse
+
+### Testing
+- [ ] Test with diverse inputs
+- [ ] Verify tool calling works
+- [ ] Check memory persistence
+- [ ] Test error scenarios
+- [ ] Monitor token usage and costs
+
+### Deployment
+- [ ] Add error handling
+- [ ] Set up logging
+- [ ] Monitor performance
+- [ ] Set cost alerts
+- [ ] Document agent capabilities
+
+---
+
+## Summary
+
+**Key Points**:
+1. **8 AI connection types** - Use ai_tool for tools, ai_memory for context
+2. **ANY node can be a tool** - Connect to ai_tool port
+3. **Memory is essential** for conversations (Window Buffer recommended)
+4. **Tool descriptions matter** - AI uses them to decide when to call tools
+5. **Security first** - Read-only database access, validate inputs
+
+**Pattern**: Trigger → AI Agent (Model + Tools + Memory) → Output
+
+**Related**:
+- [webhook_processing.md](webhook_processing.md) - Receiving chat messages
+- [http_api_integration.md](http_api_integration.md) - Tools that call APIs
+- [database_operations.md](database_operations.md) - Database tools for agents