AI Engineer

Frontier results, on device - RL Nabors, Arize

The Future Is Domain-Specific Agents - Justin Schroeder, StandardAgents

Domain-specific agents represent the future of AI, shifting from monolithic, context-heavy general-purpose models toward a compositional, multi-agent architecture. Current development practices rely on "inheritance," where adding excessive tools and context to a single agent leads to diminishing returns and integration...

You Can't Prompt the Room: The Last Skill AI Won't Replace - Balázs Horváth, VisualLabs

AI has fundamentally shifted the bottleneck in software development from writing code to identifying the right problems to solve. While AI excels at pattern recognition and code generation, it cannot replace the human ability to elicit requirements, understand stakeholder needs, and "read the room." To build truly valu...

Deterministic Infra for Non-Deterministic AI Agents - Nishant Gupta, Meta Superintelligence Labs

The transition from AI chatbots to autonomous agents shifts the primary engineering challenge from model intelligence to infrastructure reliability. Modern cloud infrastructure, designed for short-lived and deterministic workflows, faces a "great mismatch" when hosting stateful, long-running agents that exhibit probabi...

The Agentic AI Engineer - Benedikt Sanftl, Mutagent

The "Agentic AI Engineer" framework replaces manual, slow development cycles with automated, agentic loops to build and maintain AI agents at scale. This approach utilizes two primary phases: an offline loop for initial specification, build, and evaluation, and an online loop for production monitoring and continuous im...

The Prompt is the Platform - Dominik Tornow, Resonate HQ

Using RL Agent to Detect and Remediate ETL Pipeline Failures - Anna Marie Benzon

Automating ETL failure remediation requires a hybrid architecture that balances autonomous decision-making with strict operational guardrails. By integrating deterministic anomaly detection, tabular Q-learning for action selection, and external safety overrides, systems can effectively compress incident response loops ...

Your Agent Failed in Prod. Good Luck Reproducing It. - Tisha Chawla & Susheem Koul, Microsoft

Debugging AI agents in production requires shifting the focus from achieving bitwise determinism to ensuring system replayability. Because LLM outputs are inherently non-deterministic due to hardware-level factors, request batching, and mixture-of-experts routing, attempting to force absolute consistency is a futile ef...

Voice In, Visuals Out: The Agony and the Ecstasy - Allen Pike, Forestwalk Labs

AI-Driven Multi-Document Correlation for Financial Compliance - Varsha Shah, Independent

We Cut 94% of AI Coding Tokens With a Local Code Index - Rajkumar Sakthivel, Tesco

Your Agent Is Wasting Tokens and You Don't Know It - Erik Hanchett, AWS

OpenClaw in Your Hand: Building a Physical AI Terminal - Lech Kalinowski, Callstack

Bypassing the Multimodal Tax: Hybrid RAG, SQL RRF & UI Telemetry - Abed Matini, Ogilvy

AI System Design: From Idea to Production - Apoorva Joshi, MongoDB

When All Context Matters: Extended Cache Augmented Generation - Luis Romero-Sevilla, Orbis

Research to Reality: Bringing Frontier ML Research to Production - Vaidas Razgaitis, Higharc

Building an Autonomous Engineering Org - Angie Jones, Agentic AI Foundation

HTML is All You Need (for Agents to Make Graphics) - Amol Kapoor, Nori

Turbocharge Your Agent's Retrieval with TurboQuant - Shashi Jagtap, Superagentic AI