AI Agents on Pfisterer Consulting

My LinkedIn strategy as code: building a multi-agent content pipeline

Tue, 19 May 2026 15:00:00 +0200

Last Sunday, 22:00. On my desk are eleven Markdown files, next to them an open spreadsheet with 90 days of LinkedIn analytics. Cold mate in the glass, an honest question in the back of my head about why I am building a pipeline rather than writing the next post.

The spreadsheet gives a clear answer. My profile has 511 followers and delivered 44,483 impressions over the last 90 days at an engagement rate of 0.18 percent, a factor of 11 to 16 below the LinkedIn benchmark of two to three percent. On 52 of those 90 days the feed stayed entirely silent, without a single reaction to any of my posts.

Nine Seconds to Data Loss: What the PocketOS Crash Teaches German Mittelstand

Mon, 04 May 2026 10:00:00 +0200

Thursday, April 24, 2026, late evening in Utah. Jer Crane, founder of the U.S. SaaS company PocketOS, has his Cursor agent running. A routine pass through staging, powered by Claude Opus 4.6. What happens next has shown up in nearly every AI-risk talk this year: the agent deletes the entire production database in nine seconds. Backups included.

PocketOS makes software for car rental companies. Reservations, payments, customer records, vehicle tracking. Three months of data were gone. Customers showing up Friday morning to pick up a rental car found no booking in the system. Crane spent hours reconstructing reservations from Stripe payment histories, calendar integrations, and email confirmations.

The Burrito That Wrote Python: What Chipotle's Chatbot Taught German Mittelstand

Mon, 20 Apr 2026 11:15:00 +0200

Lunch break, 12:47. A developer is hungry. He opens the Chipotle app on his iPhone, taps into the support chat window, and asks the one question that should never belong there: “How do I reverse a linked list in Python?”

He expects what any support bot returns: “Sorry, I can only answer questions about your order.” Instead, Pepper, the bot behind that little chat window, comes back with a clean iterative solution, adds a runtime note, and then asks politely: “What would you like for lunch?”

Claude Opus 4.6 Performance Decline: What Businesses Must Learn

Mon, 13 Apr 2026 12:00:00 +0200

Your ERP vendor swaps out the database backend overnight. The interface looks the same, but queries take twice as long and reports come back with gaps. No changelog, no announcement. That is exactly what is happening right now with Anthropic’s flagship model Claude Opus 4.6 – and it affects everyone running AI-assisted development or automation in production. This topic falls squarely into my AI & Automation consulting for mid-sized companies.

Thinking Depth in Freefall: The Performance Data on Claude Opus 4.6

On April 2, 2026, Stella Laurenzo, Senior Director AI at AMD, published a detailed analysis on GitHub Issue #42796. Not opinion – data: 6,852 sessions, 234,760 tool calls, 17,871 thinking blocks. The results are clear.

Context Rot in AI Agents: Why Quality Degrades

Tue, 31 Mar 2026 14:30:00 +0200

Anyone who works with Claude Code, Cursor, or Copilot knows the pattern. The problem has a name: Context Rot. The first results are sharp and the architecture holds up. Then quality drops off. After 20, 30 minutes, the answers start repeating themselves. The AI agent loses context. It produces code that contradicts what it wrote five prompts ago.

Not a user error – an architecture problem. The solution lies in context engineering: the deliberate management of context windows through multi-agent orchestration. These are exactly the kinds of real-world problems I address in my AI & Automation consulting.