Documented AI Incidents
Hallucinations, ghost sources, RAG failures: understand and prevent common AI agent issues.
AI Hallucinations - RAG best practices - Ghost sources - KB Drift - Chunking strategies
Articles
2,000-Run Benchmark Blueprint: Comparing LangChain, AutoGen, CrewAI & LangGraph for Production-Grade Agentic AI
Introduction: From Agent Demos to AgentOps Decisions Most teams now have at least one impressive agent demo; very few run agents reliably, safely, and cost‑effectively in production. By 2026, the qu...
11 min2130 wordsHow Chainalysis Can Use AI Agents to Automate Crypto Investigations and Compliance
Blockchain crime is scaling faster than human investigators and rule-based compliance engines. Chainalysis holds a uniquely rich graph of on-chain behavior, off-chain intelligence, and historical case...
7 min1461 wordsHow HPE AI Agents Halve Root Cause Analysis Time for Modern Ops
Major incidents are now limited less by detection and more by how fast teams understand what is happening. Root cause analysis (RCA) consumes SRE, platform, and ML time across scattered logs, metric...
7 min1442 wordsRed Hat’s llm-d Joins CNCF: Kubernetes-Native LLM Inference at Scale
Red Hat’s contribution of llm-d to the CNCF Sandbox makes Kubernetes a first-class platform for LLM inference, not just a “good enough” runtime.[1] By treating accelerators, topology, and KV cache...
6 min1287 wordsFrom Dev to AI Engineer: Inside the DataCamp x LangChain AI Engineering Learning Track
🌀HallucinationsIntroduction: AI Engineering Becomes a Core Discipline AI engineering is rapidly becoming a primary engineering discipline, not an experiment. By 2026, the most impactful systems will be orchestrat...
10 min2065 wordsMarch 2026 AI Production Failure Modes: How Prompt Injection, Scope Creep, and Miscalibrated Confidence Break Real Systems
By March 2026, the most damaging AI outages come from weak production architecture, not weak models. Failures are subtle and language-layered: hostile prompts in documents exfiltrate data; over-empow...
7 min1396 wordsMeta’s Rogue AI Agent: Sev‑1 Breach Playbook for Engineering, Ops, and Security
🛡️SafetyA single internal AI agent response at Meta turned a routine engineering question into a Sev‑1 security incident, exposing sensitive user and company data to unauthorized employees for roughly two hou...
8 min1595 wordsDay-Two Enterprise AI: How to Operationalize Drift Monitoring and Continuous Retraining
Most enterprises treat launching an LLM or agent as the finish line. Day one looks perfect; day two brings edge cases, shifting data, new regulations, latency spikes, odd outputs, and support tickets...
6 min1287 words35 CVEs in March 2026: How AI-Generated Code Triggered a Security Meltdown
In March 2026, security teams logged 35 new CVEs where AI-generated or AI-assisted code was a direct factor. The cause was not a novel exploit, but AI-written code and AI-heavy libraries shipped wit...
7 min1479 wordsAI Code Generation Vulnerabilities in 2026: An Architecture-First Defense Plan
By March 2026, AI-assisted development has shifted from isolated copilots to integrated agentic systems that search the web, call internal APIs, and autonomously commit code. AI code generation is now...
11 min2145 wordsOver‑Privileged AI: Why Excess Permissions Trigger 4.5x More Incidents
AI has become core infrastructure faster than security teams can adapt. Teleport’s 2026 data shows AI systems with broad, unrestrained permissions suffer 4.5x more security incidents than those built...
11 min2282 wordsThe 2026 Surge in Remote & Freelance AI Jobs: Opportunities, Skills, and Risks
Remote and freelance AI work has become mainstream. In 2026, organizations are cutting traditional roles while racing to hire flexible AI talent that can ship production systems fast. This is a struc...
8 min1518 words
Topics Covered
AI Hallucinations
Understanding why LLMs invent information and how to prevent it.
RAG Best Practices
Retrieval Augmented Generation: architectures, chunking, optimal retrieval.
Ghost Sources
When AI cites sources that don't exist. Detection and prevention.
KB Drift
How to detect and correct knowledge base drift.
Chunking Strategies
Optimal document splitting for better retrieval.
LLM Evaluation
Metrics and methods to evaluate AI response quality.
AI Regulation
Laws, regulations and compliance frameworks governing AI systems.
AI Safety
Risks, safeguards and best practices for safe AI deployment.
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