AI Engineering Blog

Agentic AI refers to autonomous systems that plan, decide and act to achieve goals without constant human intervention. For businesses, this means fully automated workflows, real-time document processing, intelligent orchestration of back-office operations and continuous threat monitoring — at a fraction of the cost of traditional approaches. Unlike simple chatbots, agentic systems hold state, use tools, call APIs and recover from errors autonomously.

When you use cloud AI APIs (OpenAI, Anthropic, Google), your data leaves your perimeter, is processed on third-party servers, and may be used to improve their models depending on your contract. Private LLMs run entirely on your own infrastructure — or on dedicated hardware under your control. Contracts, client data, financial records and internal processes never reach an external server. This is a technical guarantee, not a marketing claim, and it is required by GDPR Article 32 for most regulated industries.

Every workflow is built from scratch for the specific use case — no generic templates. The stack: n8n for orchestration, LangChain or LangGraph for agent logic, CrewAI for multi-agent coordination, and custom security hardening at every tool call boundary. Each pipeline includes prompt injection filtering, privilege isolation, cost circuit breakers, human-in-the-loop controls and full audit trails. Production deployment is on rootless Podman on Rocky Linux.

It depends on your AI risk tier. High-risk systems (HR decisions, financial scoring, critical infrastructure, biometrics) require conformity assessments, technical documentation, human oversight mechanisms and registration in the EU database. General-purpose AI models above 10^25 FLOPs have additional transparency and safety obligations. Limited-risk AI (chatbots, deepfakes) requires disclosure. Minimal-risk AI has no mandatory obligations. Full enforcement is August 2026 — classification assessments should start now.

MLSecOps applies DevSecOps principles to machine learning systems. In practice: continuous monitoring for prompt injection attacks, model poisoning and data drift; adversarial robustness testing; supply chain verification of model weights and training data; privilege isolation for agent tool calls; and incident response playbooks specific to AI failures. Without MLSecOps, a production LLM is an unmonitored attack surface.

A Starter LLM deployment (single model, security baseline, basic RAG) typically takes 2–3 weeks from kickoff to production. An Enterprise Stack with multi-model setup, load balancing, fine-tuning and full GDPR documentation is 6–10 weeks. Air-gapped deployments for regulated industries add 2–4 weeks for network isolation validation and compliance documentation.