AI Engineer

Using Spec-Driven Development for Production Workflows - Erik Hanchett, AWS

User Signal Dies at the Retrieval Boundary - Sonam Pankaj, StarlightSearch

Structuring the Unstructured - Cedric Clyburn, Red Hat

Agents Building Agents - Alfonso Graziano, Nearform

Browser Agents Don't Need Better Models. They Need Better Eyes. - Kushan Raj, ARK

The 100-Tool Agent Is a Trap - Sohail Shaikh & Ankush Rastogi, Prosodica

Stop Writing Tone Instructions. Layer Them. - Isadora Martin-Dye, Isadora & Co

Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI

Building a personalized AI Research OS transforms fragmented research notes into a structured, evolving knowledge base. This system moves beyond static note-taking by utilizing a three-layer architecture: raw data, a YAML-based index, and an LLM-generated wiki layer. By prioritizing local file systems like Obsidian and...

Agents in Production: How OpenGov Built and Scaled OG Assist - Gabe De Mesa, OpenGov

Recursive Coding Agents - Raymond Weitekamp, OpenProse

Production Evals For Agentic AI Systems - Nishant Gupta, Meta Superintelligence Labs

Agentic systems require a fundamental shift in evaluation strategy, moving away from static model benchmarks toward production-grade infrastructure. While traditional benchmarks measure isolated model capabilities, they fail to capture the complexities of real-world workflows, including tool failures, API outages, and ...

A Genius With Amnesia - Victor Savkin, Nx

The Miranda Hypothesis: How Hamilton Poisoned Persona Evals - Jacob E. Thomas, Results Gen

Build Systems, Not Code - Angie Jones, Agentic AI Foundation

Building effective agentic systems requires applying traditional software engineering disciplines rather than relying solely on large, monolithic prompts. Systems thinking and workflow design ensure agents function as reliable components with defined boundaries, dependencies, and failure modes. Decomposition and modula...

The Log Is The Agent - Ishaan Sehgal, Omnara

The identity of an AI agent resides in its log—an append-only history of every input, output, tool call, and state transition—rather than in the model or runtime environment. Much like a video game save file preserves a character’s progress regardless of the hardware, a durable log allows agents to be resumed, scaled, ...

6 Things to Know about AIE World's Fair 2026

The Production AI Playbook: Deploying Agents at Enterprise Scale — Sandipan Bhaumik, Databricks

Transitioning AI systems from experimental demos to production requires a structured framework centered on five critical pillars: evaluation, observability, data foundation, orchestration, and governance. Many projects fail by prioritizing model selection over defining business-specific success metrics or establishing ...

Your Agent's Biggest Lie: "I Searched the Web" — Rafael Levi, Bright Data

You Might Not Need 50 Diffusion Steps — Ziv Ilan, Nvidia

Why MCP and ChatGPT Apps Use Double Iframes — Frédéric Barthelet, Alpic