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 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_high Tool Calling Pattern Tool Definition 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 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 visualizations n8n Tool Integration 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 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_assistants

Context Window Management

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

:

1000

Multi-Step Reasoning

ReAct Pattern

Thought: I need to find information about X

Action: web_search("X")

Observation: [search results]

Thought: Based on the results, I should also check Y

Action: database_query("SELECT * FROM Y")

Observation: [database results]

Thought: Now I have enough information to answer

Action: respond("Final answer based on X and Y")

Planning Agent

planning_workflow

:

step_1_plan

:

prompt

:

|

Task:

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:

Results:

Synthesize a final response for the user.

Platform Integrations

Slack Bot Agent

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

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_response

Web Chat Interface

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_hours

Agent Templates

Customer Support Agent

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_attempts

Research Agent

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_cited

Output Example

Request

"Design an AI agent for appointment scheduling" Output :

Appointment Scheduling Agent Design

Architecture ```yaml agent: name: "Scheduling Assistant" model: gpt-4 capabilities: - Check calendar availability - Book appointments - Send confirmations - Handle rescheduling - Answer FAQs Tools 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 workflow : - webhook_trigger : /schedule - chat - ai_agent : tools : [ calendar , booking , notification ] - respond_to_user

AI Agent Builder Skill - Part of Claude Office Skills