AI Agent Builder
Design and build AI agents with tools, memory, and multi-step reasoning capabilities. Covers ChatGPT, Claude, Gemini integration patterns based on n8n's 5,000+ AI workflow templates.
Overview
This skill covers:
- AI agent architecture design
- Tool/function calling patterns
- Memory and context management
- Multi-step reasoning workflows
- Platform integrations (Slack, Telegram, Web)
AI Agent Architecture
Core Components
┌─────────────────────────────────────────────────────────────────┐│ AI AGENT ARCHITECTURE │├─────────────────────────────────────────────────────────────────┤│ ││ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ││ │ Input │────▶│ Agent │────▶│ Output │ ││ │ (Query) │ │ (LLM) │ │ (Response) │ ││ └─────────────┘ └──────┬──────┘ └─────────────┘ ││ │ ││ ┌───────────────────┼───────────────────┐ ││ │ │ │ ││ ▼ ▼ ▼ ││ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ││ │ Tools │ │ Memory │ │ Knowledge │ ││ │ (Functions) │ │ (Context) │ │ (RAG) │ ││ └─────────────┘ └─────────────┘ └─────────────┘ ││ │└─────────────────────────────────────────────────────────────────┘Agent Types
yaml
agent_types: reactive_agent: description: "Single-turn response, no memory" use_case: simple_qa, classification complexity: low conversational_agent: description: "Multi-turn with conversation memory" use_case: chatbots, support complexity: medium tool_using_agent: description: "Can call external tools/APIs" use_case: data_lookup, actions complexity: medium reasoning_agent: description: "Multi-step planning and execution" use_case: complex_tasks, research complexity: high multi_agent: description: "Multiple specialized agents collaborating" use_case: complex_workflows complexity: very_highTool Calling Pattern
Tool Definition
yaml
tool_definition: name: "get_weather" description: "Get current weather for a location" parameters: type: object properties: location: type: string description: "City name or coordinates" units: type: string enum: ["celsius", "fahrenheit"] default: "celsius" required: ["location"] implementation: type: api_call endpoint: "https://api.weather.com/v1/current" method: GET params: q: "{location}" units: "{units}"Common Tool Categories
yaml
tool_categories: data_retrieval: - web_search: search the internet - database_query: query SQL/NoSQL - api_lookup: call external APIs - file_read: read documents actions: - send_email: send emails - create_calendar: schedule events - update_crm: modify CRM records - post_slack: send Slack messages computation: - calculator: math operations - code_interpreter: run Python - data_analysis: analyze datasets generation: - image_generation: create images - document_creation: generate docs - chart_creation: create visualizationsn8n Tool Integration
yaml
n8n_agent_workflow: nodes: - trigger: type: webhook path: "/ai-agent" - ai_agent: type: "@n8n/n8n-nodes-langchain.agent" model: openai_gpt4 system_prompt: | You are a helpful assistant that can: 1. Search the web for information 2. Query our customer database 3. Send emails on behalf of the user tools: - web_search - database_query - send_email - respond: type: respond_to_webhook data: "{{ $json.output }}"Memory Patterns
Memory Types
yaml
memory_types: buffer_memory: description: "Store last N messages" implementation: | messages = [] def add_message(role, content): messages.append({"role": role, "content": content}) if len(messages) > MAX_MESSAGES: messages.pop(0) use_case: simple_chatbots summary_memory: description: "Summarize conversation periodically" implementation: | When messages > threshold: summary = llm.summarize(messages[:-5]) messages = [summary_message] + messages[-5:] use_case: long_conversations vector_memory: description: "Store in vector DB for semantic retrieval" implementation: | # Store embedding = embed(message) vector_db.insert(embedding, message) # Retrieve relevant = vector_db.search(query_embedding, k=5) use_case: knowledge_retrieval entity_memory: description: "Track entities mentioned in conversation" implementation: | entities = {} def update_entities(message): extracted = llm.extract_entities(message) entities.update(extracted) use_case: personalized_assistantsContext Window Management
yaml
context_management: strategies: sliding_window: keep: last_n_messages n: 10 relevance_based: method: embed_and_rank keep: top_k_relevant k: 5 hierarchical: levels: - immediate: last_3_messages - recent: summary_of_last_10 - long_term: key_facts_from_all token_budget: total: 8000 system_prompt: 1000 tools: 1000 memory: 4000 current_query: 1000 response: 1000Multi-Step Reasoning
ReAct Pattern
Thought: I need to find information about XAction: web_search("X")Observation: [search results]Thought: Based on the results, I should also check YAction: database_query("SELECT * FROM Y")Observation: [database results]Thought: Now I have enough information to answerAction: respond("Final answer based on X and Y")Planning Agent
yaml
planning_workflow: step_1_plan: prompt: | Task: {user_request} Create a step-by-step plan to complete this task. Each step should be specific and actionable. output: numbered_steps step_2_execute: for_each: step actions: - execute_step - validate_result - adjust_if_needed step_3_synthesize: prompt: | Steps completed: {executed_steps} Results: {results} Synthesize a final response for the user.Platform Integrations
Slack Bot Agent
yaml
slack_agent: trigger: slack_message workflow: 1. receive_message: extract: [user, channel, text, thread_ts] 2. get_context: if: thread_ts action: fetch_thread_history 3. process_with_agent: model: gpt-4 system: "You are a helpful Slack assistant" tools: [web_search, jira_lookup, calendar_check] 4. respond: action: post_to_slack channel: "{channel}" thread_ts: "{thread_ts}" text: "{agent_response}"Telegram Bot Agent
yaml
telegram_agent: trigger: telegram_message handlers: text_message: - extract_text - process_with_ai - send_response voice_message: - transcribe_with_whisper - process_with_ai - send_text_or_voice_response image: - analyze_with_vision - process_with_ai - send_response document: - extract_content - process_with_ai - send_responseWeb Chat Interface
yaml
web_chat_agent: frontend: type: react_component features: - message_input - message_history - typing_indicator - file_upload backend: endpoint: "/api/chat" method: POST streaming: true session_management: method: jwt_token storage: redis ttl: 24_hoursAgent Templates
Customer Support Agent
yaml
support_agent: name: "Customer Support AI" model: gpt-4 system_prompt: | You are a customer support agent for {company}. Guidelines: - Be helpful, professional, and empathetic - Use the knowledge base to answer questions - Escalate to human if you cannot help - Never make up information Available actions: - Search knowledge base - Look up customer account - Create support ticket - Escalate to human agent tools: - knowledge_search: description: "Search help articles" - customer_lookup: description: "Get customer account info" - create_ticket: description: "Create support ticket" - escalate: description: "Transfer to human agent" escalation_triggers: - sentiment: very_negative - keywords: ["speak to human", "manager", "complaint"] - failed_resolution: after_3_attemptsResearch Agent
yaml
research_agent: name: "Research Assistant" model: claude-opus system_prompt: | You are a research assistant that helps users find and synthesize information on any topic. Process: 1. Understand the research question 2. Plan search strategy 3. Execute searches across multiple sources 4. Synthesize findings 5. Present with citations tools: - web_search - academic_search - news_search - wikipedia_lookup output_format: - executive_summary - key_findings - detailed_analysis - sources_citedOutput Example
Request: "Design an AI agent for appointment scheduling"
Output:
markdown
# Appointment Scheduling Agent Design
## Architecture
```yamlagent: name: "Scheduling Assistant" model: gpt-4 capabilities: - Check calendar availability - Book appointments - Send confirmations - Handle rescheduling - Answer FAQsTools Required
-
calendar_check
- Input: date_range, duration
- Output: available_slots[]
-
book_appointment
- Input: datetime, customer_info, service_type
- Output: confirmation_id
-
send_notification
- Input: type, recipient, details
- Output: success/failure
-
customer_lookup
- Input: email or phone
- Output: customer_record
Conversation Flow
User: "I'd like to book an appointment"Agent: [Thought: Need to know service type and preferred time]Agent: "I'd be happy to help! What service are you looking for, and do you have a preferred date/time?"
User: "Haircut, next Tuesday afternoon"Agent: [Action: calendar_check(date="next Tuesday", time="afternoon")]Agent: "I have availability at 2pm, 3pm, and 4:30pm next Tuesday. Which works best for you?"
User: "3pm please"Agent: [Action: book_appointment(datetime="Tuesday 3pm", service="haircut")]Agent: [Action: send_notification(type="confirmation", ...)]Agent: "You're all set for a haircut at 3pm next Tuesday. I've sent a confirmation to your email. See you then!"n8n Implementation
yaml
workflow: - webhook_trigger: /schedule-chat - ai_agent: tools: [calendar, booking, notification] - respond_to_user
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*AI Agent Builder Skill - Part of Claude Office Skills*
