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Observability is essential when AI agents drift in production. Day 3 guides you through setting up Langfuse, exploring initial traces, and adding custom trace metadata, culminating in a time-aware web scraper tool to diagnose and improve your DeepSearch agent.
Showing Sources In The Frontend outlines an evaluator-optimizer loop for LLM workflows and recommends displaying sources in the UI as card-like entries (favicon, title and snippet). It reframes the agent as a decision loop—answer when you have enough information, otherwise search—and adapts frontend patterns to present sources clearly.
Observability is crucial for AI-driven products since responses can drift. Day 3 shows how to see, debug, and improve agents using Langfuse, traces, and custom metadata, and to build a time-aware web scraper tool.
Using Vercel AI SDK's useChat, you can prototype a chat quickly, but turning it into a durable, persistent product requires backend persistence, real-time updates, and solid routing. The workshop guides you through building a POST /api/chat endpoint that streams OpenAI completions, using Drizzle to create and return a chatId, saving messages as they stream, rehydrating the UI with past conversations, and keeping the sidebar in sync via route revalidation.
Ce workshop pousse à des défis avancés : créer une boucle évaluatrice-optimisatrice et afficher les sources côté frontend. La logique actuelle revient à un simple if/else : si l’IA a assez d’information, elle répond ; sinon elle lance une recherche. L’objectif est d’adopter le motif évaluation-optimisation et d’afficher les sources comme des cartes dans l’interface.
Observability is crucial for AI-driven products; Day 3 covers seeing, debugging, and improving agents. You'll set up Langfuse, explore traces, and add custom trace metadata, then build a time-aware web scraper tool. By the end, your DeepSearch agent becomes understandable and deployable with confidence.
This workshop demonstrates turning a quick prototype with the Vercel AI SDK into a durable, persistent chat app. It covers streaming completions via a POST /api/chat endpoint, using Drizzle to create and return a chatId, saving messages as they stream, and rehydrating the UI while routing by chatId for seamless reloads.
Day 6 teaches task decomposition and how to build modular loops for robust, optimizable AI. It helps you take control of your agent's logic and fix brittle LLM agents.
Day 3 montre comment observer et déboguer un agent IA en production grâce à l'observabilité et aux traces. Vous configurez Langfuse, explorez les premières traces et ajoutez des métadonnées de trace personnalisées, puis implémentez un outil de scraping temporel.
Day 4 focuses on turning subjective agent vibes into objective, data-driven evaluation using LLM Evals. It introduces Evalite, an open-source eval framework built on Vitest to run tests without cloud providers, and guides you to define success criteria and write a first scorer to aim for a 100 score. By end, you'll have a foundational evaluation setup for the DeepSearch agent.
Using Vercel AI SDK's useChat, this workshop guides turning a quick chat prototype into a persistent, streaming chat app. It covers streaming completions, creating/storing chat IDs, routing by chatId, and persisting messages in real time, plus rehydrating UI from the database and updating the sidebar via route revalidation. By the end you’ll have an end-to-end persistent chat experience with routing, saving, and reloading conversations.
Ce workshop transforme un prototype de chatbot en expérience persistante: persistance des messages en streaming, création d’un chatId et routage vers /chat/:chatId, sauvegarde en DB et réhydratation de l’UI. Vous verrez comment router, revalider le sidebar et recharger les conversations de manière fiable.
Day 3 focuses on observability for AI agents. It guides you to set up Langfuse, inspect traces, and add custom trace metadata to diagnose and improve your DeepSearch agent. By the end, you'll transform it from a mystery into a well-understood, diagnosable system.
Observability is essential when building AI-driven products, enabling you to see, debug, and improve agent behavior as conversations and calls grow. Day 3 guides you through setting up Langfuse, exploring traces with custom metadata, and building a time-aware web scraper tool to turn the DeepSearch agent into a well-understood system.
In this workshop you'll tackle advanced challenges: an evaluator-optimizer loop and showing sources in the frontend. The description frames the system as an evaluator in a loop and proposes frontend source cards—favicon, title and snippet—similar to patterns in Anthropic's Building Effective Agents.
This workshop reframes the typical LLM agent as an evaluator-optimizer loop: one call generates a response while another evaluates it, then feedback guides the next step. It emphasizes moving logic from a pure agent to an evaluative loop and optimizes around it. It also covers displaying sources in the frontend as card-like items (favicon, title, snippet) to mirror DeepResearch patterns.
That workshop reframes the agent as an evaluator-optimizer loop: one LLM call generates a response while another provides evaluation and feedback. It also covers showing sources in the frontend with a card-like UI (favicon, title, snippet) to surface provenance and improve traceability.
Observability is essential when building AI-driven products. Day 3 teaches you to see and debug agent behavior using Langfuse, explore traces, and add custom trace metadata, then to build a time-aware web scraper tool to improve your DeepSearch agent.
This workshop examines how much control the LLM should have over the app's control flow, pitting agents against workflows. It notes that more agentic systems offer flexibility, while deterministic workflows improve predictability. In upcoming exercises, the dial will be turned back toward determinism to test concrete patterns.
The article surveys additional paths toward code independence, examining how to reduce vendor lock-in and improve portability across runtimes and tooling. It highlights patterns and architectures that decouple software from single platforms, encouraging modularity and open standards.