Good morning, AI enthusiasts!
AI engineering is slowly becoming less about writing prompts and more about building systems that don’t surprise you in production.
That’s exactly where this week’s issue goes.
Let’s get into it.
This week in What’s AI, I break down the agent pipeline I use to research, write, illustrate, package, translate, and publish content, while keeping the most important 10% human.
Nova handles research, Brown coordinates the writing workflow, parallel workers produce the visuals, and a separate loop develops titles and translations. Together, they have reduced two or three days of production work to roughly five hours of my time.
The useful part is not simply what I automated. It is how the workflow preserves my voice, where I still intervene, and why removing the final human decisions would make the output worse. Read the full breakdown, or watch the video to see the pipeline and diagrams in action.
When we designed the evaluation lessons for our 10-Hour LLM Fundamentals course, we looked for checks that improve reliability without forcing you to rebuild your pipeline. This is one of the simplest.
When an LLM judge compares answer A with answer B, run the evaluation twice and reverse their order on the second call.
If the same answer wins twice, keep the result. If the winner changes, mark the comparison as undecided or send it for human review. This exposes position bias: the judge may prefer an answer partly because it appears first or second, not because it is better. Track how often your results flip. Frequent reversals can reveal an unclear rubric, answers that are too similar, or a judge that is not reliable enough for the task.
— Louis-François Bouchard, Towards AI Co-founder & Head of Community
Mikieldus has built Hoshi, an agent workspace designed around a simple idea: an agent should do more than talk. Each user gets an isolated cloud computer with its own filesystem, shell, running processes, and credentials, so the agent can work directly with code and complete tasks. Teams can also publish their existing setup as a boilerplate, while interactive forms, charts, and choices replace long text transcripts when decisions are needed. Check it out here, give it a try, and share your feedback with the creator in the thread.
Kimi may be leading some benchmark tables, but it has not yet displaced the models people already rely on. Only 10% of respondents regularly use it, while Claude and GPT account for nearly two-thirds of the selections.
It again reinforces that better scores may earn attention, but reliability, integrations, familiarity, and developer experience determine whether a model becomes part of someone’s daily workflow.
What would it actually take for you to switch your primary model? A clear performance advantage, lower cost, better tooling, stronger coding ability, or something else? Share what matters most in your day-to-day workflow.
The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!
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Jadexrose is recruiting for AI engineering roles at startups and is looking for someone to discuss AI engineering concepts, do some mock interviews, and build stuff. If this sounds like it would help you as well, connect with them in the thread!
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Divyanshijoshi is currently looking for people to work together on AI research, especially in interdisciplinary fields with a focus on machine unlearning, LLM bias mitigation, and studying bugs in software engineering. If this sounds interesting, reach out to her in the thread!
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Mikieldus is looking for a team to develop his project. If you want to work on projects and need more details, write to him in the thread!
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When Your Agents Go Dark: Observability in Multi-Agent Systems with OpenTelemetry by MongoDB
When a multi-agent system fails, ordinary logs may show the error without revealing which agent, tool call, or handoff caused it. This article shows how to trace that path with OpenTelemetry. You will see how to instrument an agent handoff, inspect the resulting trace, and capture failures that application logs often miss. It also explains when full distributed tracing is worthwhile and when a simpler setup will do.
- Building Intelligent Feedback Systems: A Deep Dive into Conditional Agentic Workflows with LangGraph by Sandip Palit
This piece walks you through building a conditional agentic workflow for a customer review triage system using LangGraph, LangChain, Groq, and Pydantic. The author uses llama-3.3–70b-versatile at zero temperature to extract sentiment, then routes reviews through a StateGraph: positive feedback triggers a quick thank-you node, while negative reviews are passed through structured diagnosis for issue type, tone, and urgency before generating tailored resolutions.
GRPO removes PPO’s separate value model and estimates advantage by comparing outputs within a group, reducing memory use while simplifying the training loop. This article implements the algorithm from scratch, making the reward normalization, clipping, and stability trade-offs easier to understand before you use a production library.
Not every workflow needs an autonomous agent. This guide compares six LangGraph patterns, from simple chains and routing to evaluator loops and orchestrator-worker systems, then helps you decide which structure fits the problem. It also grounds every pattern in working code, clarifying how structured output and tool binding turn a plain LLM into something genuinely capable. It is especially useful when you understand LangGraph’s components but are unsure how to assemble them.
A large dataset can still give you the wrong answer when the sample is biased. Through famous historical examples and a simple Python simulation, this article shows why representativeness matters more than raw sample size and gives you a practical checklist for questioning the data behind a statistic. It closes with a five-step field kit and a flowchart for spotting tilted data before trusting statistics.
If you are interested in publishing with Towards AI, check our guidelines and sign up. We will publish your work to our network if it meets our editorial policies and standards. LAI #136: Build Faster With Agents, Debug Their Failures, and Evaluate Them More Reliably was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.