Artificial Intelligence

A junior engineer's customer-support agent got stuck in infinite loops on 3% of production conversations, made 11,000 tool calls overnight, and burned the entire monthly API budget before anyone noticed. The seven causes are no termination condition, unclear completion criteria, tool errors that look like "try again," oscillation between two states, dependency on a tool that never returns the expected result, plans that can't replan, and prompts that reward thoroughness over efficiency. The LoopGuard class implements both defenses: a max_total_steps=15 budget and an exact-repeat detector that hashes tool_name + sorted args and flags >=3 identical calls, plus a 4-step oscillation detector catching A→B→A→B patterns. The seven common pitfalls include no step budget, relying on the model's self-control (just trust the model = 11,000 tool calls), and setting alerts only on agent loop rather than system-level call-pair frequency. Every loop incident is diagnostic gold—it pinpoints which prompt or design assumption broke.

78 agent frameworks crossed the radar since 2023; most will be dead by EOY 2026. Seven criteria for a serious framework: persistence (checkpointing for crash-resumable workflows), composability (nesting/sub-graphs), observability (first-class tracing), tool integration (sane patterns), multi-model support (not locked to one provider), eval hooks, and active maintenance (releases in last 60 days). The default 2026 recommendation for new teams is LangGraph with CrewAI as a strong alternative for role-based workflows and LlamaIndex when the workload is document-heavy. Pick based on workload, team expertise, operational requirements — not Twitter hype. Pick the framework whose documentation you'd be happy to give a new hire on day one.

Anti-Spoofing: Detecting Masks and Deepfakes. In 2024, a financial news outlet reported that an HR onboarding call had been bypassed using a synthetic face rendered on a small display. By the time anyone noticed, two fraudulent accounts had been opened, and the incident response team was busy reviewing logs of

Positions BERTopic as the modern topic-modeling stack combining transformer embeddings, UMAP, HDBSCAN, and class-based TF-IDF to produce human-readable topics with representative documents. Warns that unsupervised output is always a hypothesis to be validated, not a discovered truth, and lists representation, objective, evaluation, and deployment gaps. Builds a scikit-learn KMeans baseline on a synthetic subscription customer dataframe, contrasting Euclidean and cosine distance, scaling effects, and the curse of dimensionality that drives feature selection and embeddings.

Frames function calling (OpenAI's 2023 release) as the primitive underlying every serious AI agent from Copilot Workspace to customer-support bots. Notes that typical agents make 5-15 tool calls per task, each a potential failure point (malformed arguments, timeouts, rate limits, permission errors). Cites 2024 Anthropic and OpenAI papers showing tool design accounts for more agent-performance variance than prompt engineering. Recommends treating tool calls like API contracts: typed JSON Schema, documented, versioned, tested, observable, and retried.

Why K-Means Is Still the Default (And When to Skip It). At 9:17 on a Monday morning, a dashboard can show five tidy groups and still tell a completely false story. The interesting part of unsupervised learning isn't making colored dots; it's discovering useful structure, then proving that result survives

Unsupervised Learning in 2026: When It Still Beats Supervised At 9:17 on a Monday morning, a dashboard can show five tidy groups and still tell a completely false story. The Core Idea Unsupervised Learning learns structure without receiving the final answer for every row. When to Use This (And When Not To) Use unsupervised learning when labels are unavailable, stale, expensive, or unable to express the question.

Unsupervised feature learning addresses labeled data scarcity by learning representations from unlabeled data. Approaches include autoencoders (compress and reconstruct to extract bottleneck features — also doubles as anomaly detection via reconstruction error), variational autoencoders (Gaussian prior on latent space for smooth generation), and contrastive learning methods like SimCLR (identify similar vs different samples). The healthcare deterioration detector story: 8,000 labeled events weren't enough, but pre-training an encoder via contrastive predictive coding on millions of unlabeled vital-sign windows beat every supervised baseline. Try a denoising autoencoder on your largest unlabeled pool first.