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Map workflows, automate E2E tests, and ship faster with QA Wolf (Sponsored)
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QA Wolf’s AI agent maps and tests your app’s most complex user flows. It turns your prompts into real Playwright and Appium code that runs 12x faster and more reliably than other computer-use agents.
What sets our AI apart:
Maps 200+ test cases in minutes instead of weeks of manual planning.
Executes tests 12x faster than computer-use agents.
Runs entire suites 100% parallel with consistent results.
Produces open-source tests your team owns, with zero vendor lock-in.
This week’s system design refresher:
RAGs vs Agents
Build with Claude Code: New Cohort Launch
Forward Proxy, Reverse Proxy, and API Gateway Explained
How does a request actually travel through Claude Code?
How does Claude Code keep long sessions from running out of context?
RAGs vs Agents
Ask an LLM about your company's data and it will guess. The two patterns that fix this are RAG and agents, and they solve different problems.
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RAGs: RAGs combine LLMs with retrieval to ground answers in 4 steps.
Step 1: The user query is embedded and sent to a retrieval step.
Step 2: Retrieval pulls the most relevant chunks from a knowledge base (PDFs, wikis, etc.)
Step 3: Those chunks are pasted into the prompt as context.
Step 4: The LLM writes the answer, grounded in the retrieved text.
One retrieval. One generation. Cheap, predictable, and easy to debug.
Agents: Agents wrap LLMs in a reasoning loop with tools to take action.
Step 1: The user query goes into the agent runtime. A reasoning loop wrapped around an LLM.
Step 2: The LLM reads the goal and picks a tool (Read, Write, Edit, Bash, etc.)
Step 3: The runtime executes the tool and feeds the result back to the LLM.
Step 4: The LLM reasons again, picks the next tool, and loops until the task is done.
More flexible. More tokens. Harder to debug because errors drift across steps.
The rule of thumb: Use RAG when the answer lives in your documents. Use an agent when the answer requires action on other systems.
Over to you: When do you prefer RAG over agent?
Build with Claude Code: New Cohort Launch
We’re launching a new 2 day intensive, cohort based course called Build with Claude Code, taught by John Kim, who has trained hundreds of engineers at Meta to use Claude Code in real production workflows.
The course starts soon on May 28.
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