AI Pirates
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AI Pirates
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// AI Agents Protocol

AI Agents.
Autonomous systems that work for you.

Not a chatbot. Not a copilot. An AI agent plans autonomously, uses tools, makes decisions, and executes multi-step workflows on its own. We build these systems — production-ready, GDPR-compliant, on your servers.

Agentic AI ReAct Pattern Tool Use Multi-Agent GDPR-compliant

// Evolution

Chatbot vs. Copilot
vs. AI Agent.

Chatbot / Copilot

Only responds to direct inputs
Cannot use tools or APIs
No memory across sessions
Cannot plan or prioritize
Requires permanent human supervision

AI Agent

Autonomously plans multi-step workflows
Uses APIs, databases, web, email
Long-term memory via vector DBs
Decides and prioritizes autonomously
Works 24/7 without human oversight

The core: An AI agent uses the ReAct pattern — Reason (think), Act (execute), Observe (evaluate), Loop (repeat). Orchestrated by LangChain or LangGraph, an agent can decompose complex tasks into subtasks and process them sequentially.

// Use Cases

AI Agents
for every department.

🎧

Customer Service Agent

24/7 ticket handling, intelligent routing, knowledge base access. Resolves level-1 requests autonomously and escalates only when needed. Integration with Zendesk, Freshdesk, Intercom.

📣

Marketing Agent

Content creation, campaign management, A/B test evaluation, performance reporting. Learn more on our Agentic Marketing page.

📈

Sales Agent

Lead qualification, CRM maintenance, follow-up sequences, meeting scheduling. The agent scores leads, enriches data, and prioritizes the sales funnel.

⚙️

Operations Agent

Process automation, system monitoring, anomaly detection, automated reporting. Runs via n8n workflows on your servers.

🔍

Research Agent

Market analysis, competitive intelligence, data extraction, trend monitoring. Searches sources, aggregates data, and delivers structured insights.

💻

Code Agent

Code review, automated testing, deployment pipelines, documentation. Supports development teams with repetitive engineering tasks.

// Architecture

How we build
AI Agents.

LLM LAYER

GPT-4, Claude, Llama, Mistral

ORCHESTRATION

LangChain, LangGraph, n8n

TOOLS

APIs, DBs, Web, Mail, Slack

MEMORY

Vector DBs, RAG

DEPLOYMENT

Docker, EU Servers, GDPR

01

Use Case & Scope

What tasks should the agent handle? What tools does it need? What does the ideal workflow look like? We define the scope together — more about our consulting approach.

02

Architecture & Tech Stack

LLM selection, orchestration framework, tool integration, memory strategy. Open source where possible, commercial APIs where necessary.

03

Build & Test

Iterative build: agent logic, prompt engineering, tool connections, guardrails. Testing with real data and edge cases.

04

Deploy & Monitor

Deployment on your infrastructure. Monitoring, logging, alerting. The agent works autonomously — you maintain full control.

05

Scale & Optimize

Connect new tools, launch additional agents, build multi-agent systems. Continuous optimization based on real performance data.

// FAQ

Frequently Asked Questions
About AI Agents.

What is the difference between a chatbot and an AI agent?

A chatbot responds to inputs with predefined answers. An AI agent plans autonomously, uses tools (APIs, databases, web), makes decisions, and executes multi-step workflows — e.g., analyzing a customer inquiry, looking up data in the CRM, developing a solution, and sending a personalized response.

What tasks can an AI agent handle?

Repetitive, rule-based, or data-intensive tasks: customer service (24/7 tickets), marketing (content, campaigns), sales (lead scoring, CRM), operations (monitoring, reporting), and research (market analysis, data extraction).

How long does AI agent development take?

Simple agents (FAQ bot with knowledge base): 1-2 weeks. Complex multi-agent systems: 4-8 weeks. We work iteratively — the first functioning agent often stands after a few days.

Are AI agents GDPR-compliant?

Yes — with the right architecture. We use EU hosting, self-hosted LLMs (Mistral, Llama via Ollama), data processing agreements, and data minimization. More about our GDPR-compliant AI automation.

Can an AI agent work with our existing systems?

Yes. AI agents integrate via APIs: CRM (HubSpot, Salesforce), ERP, databases, email, Slack, Google Workspace. Orchestration runs via n8n or LangChain.

What does an AI agent cost?

Simple agents from 5,000 EUR, complex multi-agent systems 15,000-40,000 EUR. Ongoing token costs typically 50-500 EUR/month depending on volume. We optimize for token efficiency and use open source where possible.

// Start Building

Ready for
autonomous AI?

30-minute Discovery Call — free of charge. We identify the ideal use case for your first AI agent and sketch the architecture.

Book Discovery Call