AI workflows
AI Feeds API Patterns: Enrich Content Without Losing the Source
Design a bounded enrichment workflow for summaries and labels, with source evidence, review states, and sensible failure handling.
A FEEDS API LAB COLLECTION
AI workflows
Context, enrichment, models, and bounded agents.
Explore the layers between a feed item and an AI-assisted decision. Start with enrichment when you need a summary or category attached to an existing record. Move to the context pipeline when a user question must retrieve the right evidence across many records. The model-release guide helps you track changes in the systems doing that work, while the agent guide covers what happens when a reading workflow can propose an action. Each layer introduces different checks; do not treat one successful model response as proof that the whole workflow is ready. Keep a readable trace from source to result and a route for human review. These articles focus on bounded designs that you can evaluate with your own examples, rather than assuming that additional autonomy or a larger model is automatically the right next step.
Your reading list
AI workflows
AI Model Feeds API: Build a Useful Model Release Watchlist
Track model releases with revision identifiers, model-card evidence, evaluation context, and a clear distinction between claims and measurements.
AI workflows
Agent AI Feeds API: Design Event-Driven Workflows with Guardrails
Connect feed events to bounded agent workflows with explicit permissions, idempotent actions, approval steps, and meaningful stop conditions.
AI workflows
LLM Feeds API: Build a Source-Grounded Context Pipeline
Turn changing feed items into selected, traceable context using sensible chunking, retrieval tests, freshness rules, and citation checks.