An analysis of 150 production incidents finds that compound AI systems break at component boundaries, and half of those failures are silent. Here is how to place circuit breakers, quality gates, and typed interfaces where the damage actually starts.
Most teams treat a green eval run as permission to ship an agent change. The data says that's the wrong contract. Here is how to build risk-tiered release gates where evals narrow the review queue instead of replacing it.
A production agent that crashes loudly is a solved problem. One that converts an error into a fluent, believable answer is not. Here is the failure class, why tests miss it, and the architecture that contains it.
A max_budget setting on your LLM gateway is a promise, not a control. Here is why budget enforcement silently fails in multi-replica agent deployments, and the reserve-then-settle architecture that actually holds.
Full-pass agent benchmarks now cost hundreds to thousands of dollars per run. Early outcome prediction halts runs whose result is already evident, and the architecture around it decides whether your rankings survive.
A new benchmark shows models follow individual RAG instructions 84% of the time but satisfy all of them in only 27% of responses. Here is the architecture that handles noisy, missing, and conflicting evidence outside the prompt.
Most enterprises believe their AI agent inventory is complete. The data says otherwise. Here's how to build agent inventory as a continuously reconciled discovery pipeline, with a canonical agent record, risk tiering, and evidence that is ready before an examiner asks.
Kill switches stop an agent from doing more damage; they do nothing about the damage already done. Here's how to design agent actions around reversibility, with compensating transactions, state checkpoints, and autonomy tiers set by blast radius.
Three enterprise vendors shipped policy enforcement inside the MCP protocol layer in the same week, converging on an answer to a question most agent teams haven't actually resolved: where should governance live?
A University of Wyoming production deployment shows RAG accuracy collapsing from 75% to under 40% as the corpus scaled past 1,000 documents — and the fix isn't a better embedding model, it's domain-scoped retrieval architecture.
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