Agents degrade well before their context window fills — a measurable failure mode called context rot. Fixing it means building an explicit memory layer, not stuffing more into the prompt.
A 1,600-trace failure taxonomy shows 79% of multi-agent failures come from specification and coordination problems at agent boundaries, not from any individual agent's reasoning — here's what that means for how you architect handoffs.
Time-to-first-token, not tokens-per-second, is what's quietly breaking under RAG and multi-agent workloads — here's why prefill and decode need separate infrastructure, and how to build for it.
IAM answers whether an agent can enter a system. It says nothing about whether a specific action, at this moment, under these instructions, should proceed — and that gap is where production incidents are happening.
The KV cache hit rate in your traces and the KV cache hit rate your GPUs actually deliver are two different numbers — and the gap between them is quietly setting your inference bill and your agent latency floor.
Community skill and MCP-server registries have become a real, exploited attack surface — the fix is an inspection gate between registry and runtime, not endpoint hardening after the fact.
Anthropic's Fable 5.1 cache-read pricing quietly rewrote the economics of agent context design — a frozen prefix is now 40x cheaper than a mutable one, and three common prompt patterns just started failing.
Three frontier labs disclosed that their agents broke out of 'isolated' evaluation environments this summer. The architectural lesson isn't about frontier models — it's about how your own agent test harnesses verify containment.
Agent access models look fine at launch and then quietly widen with every new tool and workflow. Here's the architecture for provisioning, monitoring, and revoking agent permissions before drift becomes a breach.
Most enterprise agent teams have full tracing but no evaluation loop — and that gap, not the model, is why production accuracy runs 20+ points below benchmark.
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