AI Agents & Automation

From Idea to Agent

AI Agents are the next evolutionary step after chatbots. Instead of just answering, they can act autonomously - send emails, call APIs, update databases, make decisions.

What are AI Agents?

An AI Agent is a system that:

1. Understands Goals - What should be achieved?
2. Plans Autonomously - Which steps are necessary?
3. Uses Tools - Email, APIs, databases, code
4. Iterates - Learns from feedback and adapts
5. Works Autonomously - Without constant human supervision

Our Agent Stack

No-Code / Low-Code

Fast implementation for business users:

• N8N - Self-hosted workflow automation with AI integration
• Make (Integromat) - Cloud-based automations
• Power Automate - For Microsoft 365 environments

Code-based

Maximum flexibility for complex use cases:

• LangChain / LangGraph - Framework for AI agents
• AutoGen - Multi-agent framework (Microsoft)
• CrewAI - Collaborative multi-agent systems
• Custom Python/TypeScript - Full control

Agent Patterns

1. Task Automation Agent

Use Case: Automate repetitive tasks Example: “Categorize every customer inquiry, enter in CRM and automatically respond”

Components:

• Email trigger
• LLM for classification
• CRM integration (Salesforce, HubSpot)
• Template-based response

2. Research Agent

Use Case: Collect and summarize information Example: “Analyze competitor news daily and create summary”

Components:

• Web scraping
• LLM-based summarization
• Relevance filtering
• Slack/Email notification

3. Data Processing Agent

Use Case: Process and structure documents Example: “Extract invoices, validate and transfer to accounting system”

Components:

• OCR / Document AI
• LLM for extraction
• Validation logic
• ERP integration

4. Agentic RAG

Use Case: Intelligent knowledge retrieval with reasoning Example: “Analyze support request, find relevant docs, create structured response”

Components:

• Query understanding
• Multi-step retrieval
• Context ranking
• Answer generation with sources

5. Multi-Agent System

Use Case: Solve complex tasks through collaboration Example: “Create marketing campaign - one agent researches, one writes, one designs”

Components:

• Orchestrator agent
• Specialized sub-agents
• Shared memory
• Coordination logic

Workflow Automation with N8N

N8N is our preferred tool for quick agent prototypes:

• Self-Hosted - Full control over your data
• 300+ Integrations - Slack, CRM, email, databases
• AI-Native - OpenAI, Anthropic, Ollama directly integrated
• Visual Builder - Workflows via drag & drop
• Code-Extensible - Python/JavaScript for custom logic

Our Approach

1. Discovery Workshop

Together we identify:

• Which tasks are time-consuming and repetitive?
• Where are manual processes error-prone?
• Which systems need to be integrated?

2. Agent Design

We design the optimal agent:

• Define triggers & events
• Model decision logic
• Plan tool integration
• Define human-in-the-loop

3. Implementation

Pragmatic build:

• Prototype in N8N/Make (1-2 weeks)
• Feedback & iteration
• Production hardening (error handling, monitoring)

4. Enablement

Empower your team:

• Documentation
• Training
• Handover to your team

Typical Results

• 70-90% time savings on automated tasks
• Fewer errors through consistent processes
• 24/7 availability - Agents never sleep
• Scalability - From 10 to 10,000 requests without additional effort

Why alfatier for AI Agents?

• Pragmatic - We start with quick wins, not moonshots
• Technology-agnostic - No-code or custom code - whatever fits
• Self-Hosted First - Data sovereignty where possible
• Real Experience - We use agents internally every day