Most engineers expect AI-assisted automation to be the easy part. You describe a test, the model writes it, you move on. The first week will prove you wrong. Not because the code is bad. Because the code is almost right. And almost-right code is harder to debug than wrong code. Wrong code fails loudly. Almost-right code passes on Monday, fails on Tuesday, passes again on Wednesday, and by Thursday you are questioning whether you understand your own application. I have watched teams adopt AI copilots into their Playwright suites and spend the first five days doing nothing but untangling false passes. If you are about to start this journey, here is what that week actually looks like. The Problem: The Model Does Not Know What "Stable" Means A language model has never waited for a network response. It has never watched a flaky selector survive three CI runs and then collapse on the fourth. It writes tests from a static understanding of your page, not from the dynamic reality of your application. You will ask it to write a test that clicks a button and waits for a confirmation toast. The model will produce something like this: await page . click ( ' button:has-text("Submit") ' ); await page . waitForSelector ( ' .toast-success ' ); Looks fine. Runs fine. Then your team deploys a new build where the toast takes 400ms longer to appear because of an analytics call. The test fails. Not because the feature broke. Because the model assumed a timing that was never guaranteed. This is the core problem. The model writes tests that match the page as it was when the model saw it . It does not write tests that match the page as it will be . The Solution: Treat AI-Generated Tests as Drafts, Not Deliverables The shift is mental before it is technical. You cannot review AI-generated tests the way you review human-written tests. Human tests come with intent. AI tests come with patterns. You need a different review lens. First, look for every hardcoded wait. Replace it with a state-based

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Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10 The RAG Reality Check Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small , top-k=5, stuff into context. It works for demos. Then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks drown signal in noise Customer tickets: Conversational context needs overlap, not fixed windows Latency: 500ms embedding + 200ms vector search + 300ms LLM = 1s+ per query We rebuilt our retrieval layer from first principles. Here's what actually moves metrics. Chunking: One Size Fits None # rag/chunking.py from abc import ABC , abstractmethod from dataclasses import dataclass @dataclass class Chunk : text : str metadata : dict token_count : int chunk_id : str class ChunkingStrategy ( ABC ): @abstractmethod def chunk ( self , document : str , metadata : dict ) -> list [ Chunk ]: ... class FixedTokenChunker ( ChunkingStrategy ): """ Baseline. Good for homogeneous content. """ def __init__ ( self , chunk_size = 512 , overlap = 50 ): self . chunk_size = chunk_size self . overlap = overlap class RecursiveChunker ( ChunkingStrategy ): """ Respects structure: markdown headers, code blocks, paragraphs. """ def __init__ ( self , separators = [ " \n ## " , " \n ### " , " \n\n " , " \n " , " " ], chunk_size = 512 ): self . separators = separators self . chunk_size = chunk_size class SemanticChunker ( ChunkingStrategy ): """ Uses embedding similarity to find natural boundaries. """ def __init__ ( self , model = " text-embedding-3-small " , threshold = 0.7 ): self . model = model self . threshold = threshold class AgenticChunker ( ChunkingStrategy ): """ LLM decides boundaries. Expensive but highest quality for complex docs. """ def __init__ ( self , model = " gpt-4o-mini " ): self . model = model Our production config by

Building Production-Grade LLM Evaluation Pipelines: From Vibes to Metrics How we replaced "looks good to me" with automated evaluation catching 92% of hallucinations before deployment The Problem: Why "Vibe Checks" Fail in Production Three months ago, our team shipped a RAG-based customer support assistant. It worked great in testing — we'd ask it questions, read the answers, and say "yeah, that looks right." Then it hit production. A customer asked about their billing cycle. The assistant confidently cited a policy that didn't exist. Another asked about API rate limits and got numbers from a competitor's documentation. By the time we caught it, 500+ users had seen hallucinated responses. The post-mortem was brutal: we had zero automated evaluation . Our test process was literally "ask 5 questions, read answers, thumbs up." What Production Evaluation Actually Needs Academic benchmarks (MMLU, HellaSwag) don't tell you if your system works for your use case. Production evaluation needs: Domain-specific judges — Your criteria, not generic "helpfulness" Speed — Evaluation must run in CI/CD, not overnight Regression detection — Know immediately when a prompt change breaks things CI/CD integration — Block merges that degrade quality Golden dataset management — Versioned, stratified, growing test cases Architecture: The Evaluation Pipeline ┌─────────────┐ ┌──────────────┐ ┌────────────────────┐ ┌──────────────┐ │ Test Cases │────▶│ LLM Under │────▶│ Judge Ensemble │────▶│ Metrics & │ │ (Golden Set)│ │ Test │ │ - Faithfulness │ │ Regression │ └─────────────┘ └──────────────┘ │ - Instruction F. │ │ Detection │ │ - JSON Schema │ └──────┬───────┘ │ - Custom LLM │ ▼ └────────────────────┘ ┌──────────────┐ │ Dashboard/ │ │ PR Comments │ └──────────────┘ Core Abstractions # eval/base.py @dataclass ( frozen = True ) class TestCase : id : str input : dict [ str , Any ] expected : dict [ str , Any ] | None = None tags : list [ str ] = field ( default_factory = list ) # ["edge-case",

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . I spent a week deploying a CrewAI agent to AWS Bedrock AgentCore. The SDK wasn't on PyPI. The error messages were 200 OKs. The container crashed without logs. And the naming regex rejected hyphens without telling me why. This is the full debugging trail. Every failure was silent. Every fix required reading source code nobody documented. Table of Contents The Project Failure 1: The SDK That Doesn't Exist on PyPI Failure 2: The 200 OK That Means Failure Failure 3: The Container That Crashed With No Logs Failure 4: The Naming Regex Nobody Documented The Two-Client Split Nobody Mentions What I Learned The project I built a resume-tailoring AI agent with CrewAI and Amazon Bedrock. It takes a job description, analyzes your resume, identifies gaps, and rewrites bullet points to match what the role actually needs. Locally it worked perfectly. CrewAI orchestrates the agents, Bedrock Nova Pro handles the LLM calls, and the output is solid. Deploying it to production was the problem. AWS launched Bedrock AgentCore in June 2026 as a managed runtime for AI agents. You containerize your agent, push the image, and AgentCore handles scaling, memory, and invocation. Sounds simple. It was not simple. Failure 1: The SDK that doesn't exist on PyPI The docs say to install bedrock-agentcore-client . I ran: pip install bedrock-agentcore-client It installed successfully. No errors. That's because there's a placeholder package on PyPI with that name. It installs, imports fail silently, and your container builds successfully with a broken dependency inside. The real SDK lives in AWS's CodeArtifact registry. You need to configure pip to pull from a private index: aws codeartifact login --tool pip \ --domain amazon-agent-runtimes \ --repository agent-runtimes-pypi \ --domain-owner 600427722194 Then install from there. The PyPI package is a trap. Nobody warns you. Hours lost: 3. The error only appears at runtime

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This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Over the weekend, several current and former advisors to President Donald Trump on AI publicly lobbed insults at the country’s leading AI companies. David Sacks, the president’s AI and crypto “czar” until…

The lawsuit from the states alleges that the deal would harm movie theaters, basic cable distributors, and audiences.