InfoQ - Artificial Intelligence

Yelp Unifies ML Model Training with Training Orchestrator

Claudio Masolo — Tue, 21 Jul 2026 10:00:00 GMT

Yelp has launched Training Orchestrator. This new internal framework replaces individual team Spark training scripts. Now, it uses a configuration-driven, DAG-based execution model.

By Claudio Masolo

Podcast: Strands Agents with Clare Liguori

Clare Liguori — Mon, 20 Jul 2026 11:00:00 GMT

Thomas Betts talks with Clare Liguori, the technical lead on the open source Strands Agents SDK. The conversation covers how Strands Agents has grown from a Python SDK to a full agent harness running in production. Clare shares some lessons learned from building agents at scale, shifting to a model-driven architecture, and what comes next as the LLMs that underpin agents continue to improve.

By Clare Liguori

Presentation: From OTEL to SLMs: Distilling Frontier Model Behaviour from Production Telemetry

Ben O'Mahony — Fri, 17 Jul 2026 13:17:00 GMT

Ben O'Mahony discusses building custom AI-powered Language Server Protocols (LSPs) that go beyond standard rule-based checkers. He explains how to instrument AI agents natively with OpenTelemetry to track concrete user actions (accepting, dismissing, or regenerating code fixes) as implicit labels, creating a continuous data flywheel to distill frontier capabilities into cheaper, local SLMs.

By Ben O'Mahony

Presentation: Postgres for Production Agents: Your Relational Foundation for Enterprise AI

Gwen Shapira — Wed, 15 Jul 2026 12:57:00 GMT

Gwen Shapira shares how teams are scaling AI features using PostgreSQL for mission-critical apps. She explains how to leverage Postgres's multi-modal capabilities - including JSONB parsing and high-recall HNSW vector indexing - to deliver deterministic and semantic context to LLMs. She also discusses vector quantization to speed up queries by 4x and strategies for managing agentic memory.

By Gwen Shapira

Article: Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture

Stella Berhe, Stephan Bragner, Vikram Maran, Anand Jayaraman — Tue, 14 Jul 2026 09:00:00 GMT

AI makes the first 80% of development feel fast, but hides architectural complexity until it's too late. To prevent system instability, engineering leaders must shift from raw throughput to systemic comprehension. By unifying spec-anchored SDD, TDD, and automated fitness functions into a repo-bound "Context Store," teams can ensure AI agents and human reviewers evolve code safely.

By Stella Berhe, Stephan Bragner, Vikram Maran, Anand Jayaraman

How DoorDash Built an AI Shopping Assistant That Doesn’t Rely on the LLM Alone

Leela Kumili — Mon, 13 Jul 2026 14:08:00 GMT

DoorDash details the architecture behind Ask DoorDash, its AI-powered conversational shopping assistant, combining LLMs, specialized AI agents, MCP-based tooling, and an intelligence layer with persistent consumer memory and live backend data. Early results show up to 24% higher checkout conversion, 17% larger baskets, and improved intent accuracy using memory-backed sessions.

By Leela Kumili

Podcast: Governance in the Age of AI: a Conversation with Sarah Wells

Sarah Wells — Mon, 13 Jul 2026 11:00:00 GMT

In this podcast, Michael Stiefel spoke to Sarah Wells about the relationship of governance to software architecture. Governance enables teams to work effectively by establishing procedures that minimize system complexity, improve security, and reduce repetitive tasks. Targeted checklists help engineers by reducing the stress over these procedures.

By Sarah Wells