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.
Batch-retrained fraud classifiers assume attack patterns evolve on a quarterly cycle. Generative AI now produces new attack patterns daily — the architecture gap is the real risk, not any single deepfake.
Headless Chromium is the default 'browser tool' for AI agents, and it's quietly becoming the most expensive, least reliable line item in agent infrastructure. Two hyperscaler moves this month show what replaces it.
Agent loops are decode-bound, not prefill-bound, and most enterprise serving stacks are still sized for the wrong bottleneck. Here's what changes in your architecture and why disaggregated inference is becoming the default pattern, not a hardware shopping list.
Teams keep trying to make one protocol do both jobs — tool access and agent-to-agent negotiation. Here's why that's the wrong architecture, and what to build instead.
94% of teams have agent observability in place, yet only 12% say they can actually govern their agents — the gap isn't a tooling problem, it's an unassigned architectural responsibility.
New evidence shows agentic keyword search hits 94.5% of RAG faithfulness with zero vector store — here's how to decide whether your team still needs one.
A skill pack for cost review, IAM least-privilege checks, and safe resource changes via the AWS CLI.
An enterprise skill pack for transaction review, KYC/AML documentation checks, and regulatory-aware validation.
A worked case study on Smart SDLC's real production skill pack, then design and ship your own using every practice from this course.
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