The Silent CPU Crunch, Shadow Agent Sprawl, and Why LeetCode Is Dead
Today we are looking at the heavy operational realities of scaling agentic AI. From AWS rationing basic CPU capacity to enterprises bracing for a wave of untracked internal bots, the bottleneck is shifting fast from model capability to execution engineering.
Tools & Products
Plan-and-Act Agent Design Patterns
I am telling everyone building agents to look at the Plan-and-Act pattern right now. Standard ReAct loops accumulate failed attempts directly in the prompt context, which completely tanks model attention over long runs. Splitting your system into a dedicated planner and a stripped-down executor is how you build reliable web and UI agents.
Agentic Code Quality and Constraints
We need to stop worrying about the LLM's raw generation and start focusing on the sandbox boundaries we build around them. The goal isn't to get the agent to write perfect code on the first try; it's to build deterministic constraints that reject bad proposals automatically. If your CI/CD isn't set up to self-remediate agent output yet, you're doing it wrong.
Data Validation with Pointblank in Python
Data quality is the silent killer of RAG and LLM applications. I like Pointblank because it moves away from rigid schema validation towards dynamic quality gates and row-level quarantines. If you aren't treating data validation as an in-stream process, your agents are going to hallucinate on stale, dirty data.
Research
Revision Prompting for Industrial LLM Workflows
Standard LLM rewriting is incredibly wasteful and expensive. Revision Prompting—where you send the original text, the desired changes, and ask the model for a patch—is a massive win for speed and token cost. It's a simple workflow change that delivers instant latency improvements for any production text-editing pipeline.
LinkedIn's Shard-Level Cache Speeds Up Model Distillation by 8x
LinkedIn cutting their search model training time from 45 hours to under 5 hours by caching teacher outputs is a prime example of engineering pragmatism. You don't always need a bigger cluster to iterate faster; sometimes you just need to avoid recalculating the same embeddings. Same-day iteration cycles are what separate winning AI teams from the rest.
Tytan: Neurosymbolic Schema Construction
Merging deterministic validation with probabilistic LLMs is the holy grail for automated data engineering. Tytan shows how you can automatically map messy relational schemas by letting an LLM make educated guesses, then validating those guesses against actual database keys and joins. It is a great blueprint for how we should be building automated pipeline tools.
Industry
AWS Cracks Down on CPU Waste Amid Agentic Boom
Everyone is obsessed with GPU shortages, but the real crunch right now might be basic CPU capacity. AWS is literally telling their own engineers to clean up idle EC2 instances because AI agent orchestration is devouring classic compute. If you're building multi-agent systems, don't ignore your underlying CPU unit economics.
AI Agents Are Becoming Your Next Access Database Problem
We are heading straight toward a massive wave of shadow AI where business units build undocumented, unmonitored agents on their own. It's the Excel or Access database nightmare of the 2000s all over again. Instead of locking down access, you need to embed identity, logging, and permissions directly into your developer platforms today.
Coinbase Rebuilds Engineering Interviews for the AI Era
Coinbase rebuilding their hiring loop to test how candidates guide and evaluate AI is the smartest thing I've read all week. LeetCode is officially dead; we need engineers who know how to debug AI output and exercise system-level judgment. If your hiring process still bans LLM usage, you're testing for skills that are already obsolete.
Physical Intelligence Splits Robotics Stack with Postgres and ClickHouse
I love seeing actual architectural blueprints from the field, and Physical Intelligence's Postgres and ClickHouse split is a masterclass in scaling AI metadata. Attempting to run high-cardinality robotics logs and transactional agent state in a single relational database is a recipe for disaster. Keep your transactional state simple, and offload the search and analytical heavy lifting to ClickHouse.
Stop building brittle AI wrappers and start building robust, scalable workflows. Book a free AI audit today at consult.kylemzhang.com