#reliability
13 articles
Computer Use and GUI Agents: Screenshot Loops, Grounding, and Why It Is Hard
How GUI agents see screens, decide actions, and click their way through UIs — the grounding problem, safety architecture, and why computer use agents fail in production.
Concurrency in Agent Systems: Parallel Tools, Fan-Out, and Rate Limits
How to run agent tool calls in parallel, design fan-out/join patterns, schedule around provider rate limits, and cancel in-flight work without corrupting state.
Limits and failure modes: overthinking, underthinking, and when reasoning breaks
The empirically-documented ways reasoning models fail in production — overthinking, underthinking, sycophantic traces, benchmark gap — and how to detect and route around them.
Why Models Hallucinate: A Mechanistic View
Why LLMs hallucinate is not a mystery — it is a structural consequence of next-token prediction, probabilistic decoding, and a missing verification loop.
Hallucination mitigation and output validation for production systems
Factuality vs. faithfulness, grounding techniques, structured outputs, LLM-as-judge, and conformal prediction — practical controls that reduce hallucination in deployed LLMs.
The LLM Gateway Pattern: Rate Limiting, Cost Attribution, and Observability
How a centralized AI gateway gives you virtual keys, dual TPM/RPM rate limiting, per-request cost tagging, and provider fallback — and when to build vs buy.
Agent Evaluation and Why Agents Fail in Production
Why 85% of agent pilots never reach production scale — compounding error rates, LLM-judge bias, and the six evaluation layers that catch what offline tests miss.
Capability limits and failure modes every AI engineer must know
Hallucination, lost-in-the-middle, injection, and benchmark overfitting are structural properties of LLMs — and they shape how you design systems.
Fallbacks, Retries, and Circuit Breakers: Building Reliable LLM Pipelines
How to handle 429s, provider outages, and transient failures in production LLM systems: exponential backoff, fallback chains, and circuit breakers explained with real code.
Building Reliable Tool-Using Systems: Patterns From Production
Production patterns for reliable LLM tool use: loop budgeting, validation layers, idempotency, fallbacks, and observability that prevent runaway agents.
Input and Output Guardrails in Production: NeMo Guardrails, Llama Guard, and Beyond
How to layer NeMo Guardrails, Llama Guard 3, and lightweight classifiers to defend LLM apps — latency costs, bypass rates, and what still fails.
The AI Gateway Pattern
How a thin proxy between your app and every LLM provider gives you routing, fallback, cost tracking, and semantic caching without touching application code.
Cost Control and Reliability Engineering
Cut your LLM API bill ~4x and eliminate 429 cascades with prompt caching, model routing, semantic caching, and circuit breakers. Worked math from $60k to $14k/month.