
Graph Engineering vs. Loop Engineering in AI Agents
Explore the architectural trade-offs between graph and loop engineering when building AI Agents, including production realities and state management.
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Explore the architectural trade-offs between graph and loop engineering when building AI Agents, including production realities and state management.

From Shadow AI and prompt injection to a $25.6M deepfake heist: a sober map of the risks, the frameworks (OWASP LLM Top 10, EU AI Act, ISO 42001) and a workable defensive architecture for 2026.

The Financial Times just exposed how Amazon engineers are gaming internal AI leaderboards by automating pointless tasks. As the creators of OpenClaw—the platform that inspired Amazon's tool—we have some thoughts on why measuring token consumption is the fastest way to ruin your AI strategy.

A hundred coding agents in parallel, fifty PRs a day, a billion tokens a day. What Gas City shows about multi-agent engineering — and the three ideas worth internalising regardless of which toolkit eventually wins.

Salesforce $300M on Anthropic, Uber out of annual AI budget by April, a 4-person startup burning $125k per month. Why token costs are exploding and how to set up your AI architecture so your CFO doesn't panic.

Microsoft has 100,000 engineers. Meta tracks mouse and keyboard. xAI offers $420 for your tax return. The next batch of AI training data comes from in-house staff — and it has consequences for how you set up your own AI stack.

Closing your laptop should not kill your AI Agents. On a Hostinger KVM2 VPS at around €9 per month you can run OpenClaw, Hermes AI and Paperclip AI 24/7 in an EU data centre — no Mac Mini, no networking pain, no hardware to babysit.

Cortecs.ai positions itself as Europe's LLM router: 150+ model endpoints, EU-sovereign hosting, pass-through pricing and GDPR compliance as primary Data Processor. What does this mean for Dutch engineering teams currently on OpenRouter?

Anthropic's Project Glasswing and the 'Mythos' capability are permanently changing the economics of zero-days. For CTOs and engineering managers, this means the focus must shift from detection to relentless response speed. A blueprint for the AI-driven SDLC.