O'Reilly

Agents Building Agents - Alfonso Graziano, Nearform

Jun 28, 2026 · 30:14

Alfonso Graziano, a tech lead at Nearform, presents a production workflow where a coding agent (Claude Code) builds and iteratively improves a target AI agent by running evals, inspecting failures, and autonomously fixing system prompts, tool contracts, or code. He demonstrates that this loop raised eval accuracy from 18% to 83% on a naive agent and achieved a 10% improvement on a human-optimized production agent. For live data, the system collects user feedback traces, clusters failure modes with root-cause analysis, and generates fix proposals, which are validated by subject matter experts and added to a golden dataset. The method relies on spectrum development, quality gates, and observability, enabling semi-autonomous agent improvement without cheating on eval sets.

Practical GraphRAG: Making LLMs smarter with Knowledge Graphs — Michael, Jesus, and Stephen, Neo4j

Jul 22, 2025 · 19:46

Michael Hunger and Stephen Chin of Neo4j present GraphRAG as a method to enhance LLMs by integrating knowledge graphs, achieving more accurate, contextual, and explainable answers than standard RAG. They highlight limitations of vector search, showing GraphRAG improves relevance and reduces hallucinations. They detail a three-step construction process: lexical graphs, entity extraction, graph enrichment with algorithms. They demonstrate open-source tools like Neo4j Knowledge Graph Builder and GraphRAG Python package, and show an agentic approach using domain-specific retrievals. They cite studies showing three times improvement in accuracy.

How to Fail at AI Strategy: Hamel Husain & Greg Ceccarelli

Apr 13, 2025 · 17:03

Greg Ceccarelli and Hamel Husain argue that the most reliable path to AI failure is to follow a set of inverted worst practices, including cultivating disconnect between executives and builders, promising unrealistic AI capabilities, drowning communication in jargon, and avoiding data analysis. They describe how to divide your company by incentivizing secrecy and using jargon like 'agents' to exclude domain experts, ensuring that AI projects are disconnected from real needs. The speakers advocate faking strategy by highlighting random paragraphs from last year's report, announcing vague goals like 'become the global AI leader in everything,' and creating a massive backlog with no timeline. They recommend throwing tools at problems—buying expensive vector databases or switching frameworks—without understanding root causes, and blindly trusting off-the-shelf evaluation metrics like BLEU and ROUGE. Crucially, they insist on never looking at data, using complex systems inaccessible to domain experts, and trusting gut feelings over evidence. This inverted guide guarantees wasted resources, alienated teams, and spectacular failure.

Why Agent Engineering — swyx

Mar 24, 2025 · 11:45

Swyx argues that 2025 is the year of agent engineering, pivoting the AI Engineer Summit to focus exclusively on agents and explaining why agents are now viable due to advancements in reasoning, tool use, model diversity, and a 1000x cost reduction in GPT-4-level intelligence over 18 months. He highlights that OpenAI's ChatGPT grew 33% to 400M users in three months after shipping agentic O1 models, and projects it will reach 1B users by year-end. Swyx cites Simon Willison's crowdsourced definitions and OpenAI's new agent definition, while calling for a halt to demos of flight booking agents and Reddit astroturfing. He frames agent engineering as the evolution of AI engineering, distinct from MLE and software engineering, with practical use cases like coding and support agents having clear product-market fit.