#llmops
25 posts tagged with #llmops
Every article below is hand-written, technically reviewed, and focused on llmops. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
AI and Machine Learning How to Ship OpenAI Agents API Guardrails in 1 Day [2026]
Production tool-use fails on governance, not prompts. Here’s a practical setup for OpenAI Agents API guardrails: allowlists, layered rate limits, and audit logs you can actually use in incidents.
AI and Machine Learning Execution Trace Tree for AI Agents: Build One in 60 Minutes
Stop drowning in agent logs. Instrument a deterministic execution trace tree (spans, tool calls, checkpoints) so you can replay failures and diff runs like real engineering.
Cloud and DevOps How to Build Vendor-Neutral LLM Observability Monitoring [2026]
A practical blueprint for llm observability monitoring vendor neutral: an OpenTelemetry pipeline, an open trace schema, redaction boundaries, tail sampling, and cost controls that survive vendor swaps.
AI and Machine Learning Agent Evaluation Roadmap for Small Teams [2026]: The 30-Min/Week Plan
A pragmatic map of offline vs online vs HITL evals, what to measure beyond “response quality,” and the smallest program that actually prevents agent regressions.
AI and Machine Learning How to Start an AI Agent Evaluation Program (5-Task Scorecard)
Ship agents with a regression safety net: pick 5 real tasks, define pass/fail, run evals weekly in CI, and publish a scorecard that forces better decisions.
AI and Machine Learning How to Do Agent Tool Call Failure Testing [2026 CI Harness]
Build a deterministic CI harness that breaks your agent’s tools on purpose: timeouts, 429s, partial writes, retries, and stuck loops. Then regress it with golden traces.
Cybersecurity AI Security Leader Playbook [2026]: 10 Controls That Ship
A practical AI security leader playbook you can implement this quarter: inventory, approval gates, agent threat modeling, OWASP LLM Top 10 controls, vendor review, and incident response.
Cloud and DevOps ChatGPT Down? 8 Fallback Patterns for API Outages [2026]
When ChatGPT goes down, your app shouldn’t. A production playbook for detection, retries with jitter, circuit breakers, bulkheads, throttling, and multi-provider failover.
AI and Machine Learning RAG Data Leakage Test Suite [2026]: CI Red-Team Setup
Build an automated red-team suite for RAG apps: canary tokens, regex + similarity detectors, multi-step prompt-injection attacks, and a CI risk score that blocks risky merges.
Cloud and DevOps How to Pick LLM Application Observability Metrics [2026]
Token logs are table stakes. Here’s the minimum set of LLM application observability metrics for tools, RAG, caching, refusals, and privacy-safe logging that actually debugs production incidents.
AI and Machine Learning Data Privacy in RAG Redaction and Retention [2026 Playbook]
A practical engineering playbook for data privacy in RAG: what to redact, where privacy leaks happen, default retention periods, and audit trails you can defend in an audit without wrecking debugging.
AI and Machine Learning AI Engineering Evals: Regression Gates for Prompts, Tools, RAG [2026]
Stop letting prompt tweaks and model upgrades silently break production. Here’s a CI-style regression gate system for prompts, tool calling, and RAG with golden sets, schemas, shadow evals, and failure budgets.
AI and Machine Learning AI Agent Observability Logging Schema [2026]: OTel + Redaction
A copy‑paste JSON logging contract for agent runs (spans, tool I/O, cost, retries) plus a minimal OpenTelemetry mapping you can implement in a weekend—without leaking secrets.
AI and Machine Learning LLM Latency Benchmark Methodology: Streaming UX Metrics [2026]
A UX-first LLM latency benchmark methodology for streaming chat and agent apps: measure chunk cadence, jitter, tool-call stall time, and end-to-end time-to-usable—not just TTFT.
AI and Machine Learning AI Agent Evaluation Framework 2026: 8 Metrics Beyond Task Success
If your agent eval is just “did it finish the task?”, you’re flying blind. Here’s a 2026-ready scorecard for tool correctness, recovery, safety, and cost-per-success—plus a regression suite blueprint you can actually run in CI.
Cybersecurity Prevent Sensitive Data Leakage in RAG: The 2026 Playbook
RAG leaks rarely happen in the model. They happen in logs, traces, and vector stores. Here’s a practical 2026 playbook to ship redaction, least-context retrieval, and auditable controls end-to-end.
AI and Machine Learning Agent Evaluation Harness [2026]: Replay, Rubrics, CI Gates
Most agent failures aren’t “bad prompts”. They’re multi-step tool cascades. Here’s how I build an agent evaluation harness that actually prevents regressions.
Developer Tools AI Coding Assistant Reviews 2026: The Only Buyer Framework That Holds Up
In 2026, the best AI coding assistant isn’t the smartest model. It’s the one that hits latency SLOs, finds the right code, and ships with governance that won’t get you fired.
![developer monitor terminal logs tracing — illustration for article on OpenTelemetry Instrumentation for AI Agents [2026]:](https://img.kunalganglani.com/images/vzekdneq/production/f126a9eb93656ea60adeae440cf4b74ed0b5fb93-1200x675.webp?auto=format&fit=max&q=75&w=600)
AI and Machine Learning OpenTelemetry Instrumentation for AI Agents [2026]: Ship It
A vendor-neutral tracing schema for AI agents: model LLM calls, retrieval, tool runs, retries, and token cost as spans. Then dashboard latency, error tax, and cost per successful task.
AI and Machine Learning Reduce LLM API Costs 60%: 6 Techniques [2026]
A technique-by-technique playbook with real cost math for cutting LLM API bills in production — covering semantic caching, prompt compression, model routing, batch APIs, and context tiering with 2026 pricing.
AI and Machine Learning Local LLM Cost vs Cloud API: 2026 Break-Even Math [Calculator]
A workload-specific break-even framework with real per-token math — hardware amortization vs. API spend — for coding, RAG, and batch workloads in 2026.
AI and Machine Learning AI Agent Latency Budgets: Performance Guide [2026]
Single-model TTFT benchmarks lie to agent builders. Here's the 6-tier latency budget framework for production AI agents in 2026, with real math for multi-hop tool calls.
AI and Machine Learning LLM Latency Benchmarks 2026: 6 Levers to Hit Sub-500ms TTFT
Real TTFT and throughput data across 10+ models, where latency breaks user experience, and 6 architectural levers to hit sub-500ms budgets in production without sacrificing quality.
AI and Machine Learning Netflix Headroom: How to Cut AI Agent Costs 10x in Production [2026]
Netflix open-sourced Headroom — a context optimization layer that slashes LLM inference costs by up to 10x. Here's how the architecture works and how any team can apply the same patterns.
AI and Machine Learning AI Tech Debt: The 3 Types Silently Killing Your LLM App in Production [2026 Framework]
Prompt decay, model drift, and hallucination tax are the three distinct types of AI tech debt accumulating in every LLM-powered production system. Here's a framework to identify, measure, and pay them down.