Harness Engineering Practices P19. Turn Failures into Data Assets (The Harness's Own Retro Loop) 🎯 Point A harness that repeats the same failures is an unimprovable black box. Log failures and feed them back into harness improvement. 📝 Overview Log every human intervention, rollback, and escaped defect with its cause, then feed this into harness improvements (new gates, new instructions, new tools). The harness should have CI for itself. This is what separates maturity L3 (measuring) from L4 (continuously improving). 🔍 Explanation When agents fail, most organizations conclude "the model is bad." But the real question is "why couldn't the harness prevent this failure?" A human intervention means the harness lacked a guardrail or verification. A needed rollback means the circuit breaker didn't trigger. An escaped defect means the verifier was insufficient. Recording these events with root cause analysis and converting them into harness improvement actions (adding gates, updating instruction files, improving tools) is the loop that matures a harness into a product. 🛠 How to Practice - Record all human interventions, rollbacks, and escaped defects in structured logs with cause classification - Run regular retrospectives (weekly or biweekly) to identify recurring failure patterns - For each failure pattern, select and implement the most effective improvement action (new gate, instruction addition, tool improvement) - Measure improvement action effectiveness and retract low-impact ones to try different approaches 💼 Use Cases - Weekly analysis of issue-to-PR agent failures to identify harness improvement points - CI auto-maintenance: tracking false positive causes to improve triage logic - Incident response: deriving observability access improvements from cases where agent recommendations were inaccurate ⚠ Pitfalls Adding a rule after every failure leads to "Scaffolding Ratchet" (AP3). Turning failures into data assets isn't about adding more rules — it's about root cause analysis and choosing the most effective improvement. Logging without analysis accumulates data without generating value. Regular retrospective processes are essential. #HarnessEngineering# #ContinuousImprovement#

"I'm going to tell you my story because I think it's an amazing story of continuous improvement, of how teeny-tiny steps compound over time." Over the past year, my work has transformed completely—and it all started with a broken ankle and a willingness to get curious about AI. In this article, I share how I went from a skeptic to building multiple AI products, even though I had a limited engineering background. Here's what you'll learn: 🔧 How small experiments can snowball into real products (I launched my first AI tool just three weeks after starting to experiment) 🧠 Why AI evals became the missing discovery habit—a feedback loop that helped me continuously improve my products 🎯 How personal productivity experiments taught me the skills I needed to build production AI products 🤝 Why I still believes in talking to customers, but see new opportunities for AI to be additive rather than replaceable 💡 The hero's journey framework I used to make sense of my transformation 🚀 Real examples of the products I built: Interview Coach, Business Fundamentals Coach, AI-generated interview snapshots, and more The core message? You don't have to be an engineer to figure this out. You have access to expert tutors 24/7, and teeny-tiny steps compound over time. Read the article (or watch the talk): ❓ What's one small experiment or skill you've been curious about trying but haven't started yet? Share your thoughts in the comments below.

Sustainability is a shared responsibility. Through innovation, collaboration, and continuous improvement, Intel Foundry is working with customers, suppliers, and industry partners to help advance a more sustainable future for semiconductor manufacturing. #IntelFoundry# #Sustainability#

KuCoin announced that its Artificial Intelligence Management System (AIMS) has officially achieved ISO/IEC 42001:2023 certification, reinforcing its capabilities in responsible AI deployment, governance, and continuous improvement. As the world’s first international standard for AI management systems, ISO/IEC 42001 establishes an internationally recognized framework covering KuCoin’s AI management and supporting functions to drive transparent, secure, and compliant AI integration across risk control, AML, fraud detection, market surveillance, and customer operations.

Zcash has unparalleled cryptographers, security engineers, and security researchers. And the community is heavily focused on continuous improvement and hardening the network. That's why it engages world class security researchers to look for bugs. And that's why the recent potential exploit was found. It wasn't by accident and it's a vote of confidence, not a cause for alarm. When it comes to any L1, there will be bugs. What's important is that there are world class researchers focused on hardening the network and staying ahead of the bad guys. This has always been and always will be the dynamic of building software that is secure. Onward.

Another solid day at Hacker Summer Camp connecting with AppSec folks in the trenches. The recurring theme isn't just finding vulns, it's getting them fixed—and more importantly, preventing them upstream. That means working *with* developers, understanding their pain points, and making security less of a roadblock. It's about pragmatic wins and continuous improvement, not just tool deployment. If you're at Black Hat, swing by booth #4943# to share war stories and strategies for effective developer collaboration. Also, don't miss Katie Paxton-Fear and Milan Williams today at 1:30 PM at our booth, talking about 'Overcoming the Fear of Security Risk with AI-Assisted Development'—a critical topic for practitioner success.

A reminder from Atomic Habits by James Clear: “The purpose of setting goals is to win the game. The purpose of building systems is to continue playing the game. True long-term thinking is goal-less thinking. It’s not about any single accomplishment. It is about the cycle of endless refinement and continuous improvement. Ultimately, it is your commitment to the process that will determine your progress.”

We're proud to announce our partnership with @ChelseaFC. You'll find us on their training kit. Because the work that makes the difference happens long before match day. In the preparation. The repetition. The hours that never make the highlight reel, but make everything else possible. The same is true in legal work. The best outcomes are earned long before they're visible. That's why it means more that Chelsea's own legal team uses Legora, putting in the quiet, careful work behind the scenes so the club can perform at its best. As Rob Hamblin, General Counsel for Chelsea FC, put it: "Having Legora present on the training kit of our men's, women's and Academy teams is a reflection of our shared commitment to preparation, development and continuous improvement." We're proud to support a team that understands what it takes to win, on and off the pitch. Here’s to the long game.

How do you actually build evaluation benchmarks for AI agents at scale? LangChain just shared their full approach. Title: How We Build Agent Environments & Tasks URL: ❓ What exactly makes up an agent "task"? 💡 A task has three components: an input, an environment, and a test script. The environment hosts agent execution, and a rubric defines scoring criteria. A "world spec" consolidates shared domain knowledge across related tasks — API schemas, data generation methods, trace parsing scripts — in one place. ❓ How do you create tasks efficiently at scale? 💡 LangChain uses a two-step pipeline. First, a "spec generation" phase where a coding agent scans repositories, groups traces, maps credentials, and auto-generates an initial world spec. Then a "Spec2Task" phase converts that spec into runnable evaluation tasks. The spec from the very first task becomes the foundation, iteratively refined through subsequent task creation cycles. ❓ What are the most common mistakes when building evaluation tasks? 💡 Three pitfalls stand out: ・Don't skip running tasks with real agents — paper evaluation won't surface environment flaws ・Calibrate difficulty across model tiers (e.g. gpt-5.6-Luna vs Sol) — what's hard for one may be trivial for another ・Match the data generation method to the data type: LLM-based approaches for free-text, SQL scripts for tabular data ❓ Is a benchmark "done" once you've built it? 💡 Not at all. Continuous improvement from production data is the core idea. Real production traces feed back into cost modeling, prompt simplification validation, and tool configuration testing — the benchmark evolves alongside the system it measures. Treating evaluation environment engineering as ongoing rather than a one-time project is the key practical insight here. #AIAgents# #LLMEvaluation#

Still shipping your entire schema to a Text-to-SQL agent on every request? You're losing both accuracy and money 💸 Here's how a knowledge graph fixes both. Title: How a Neo4j semantic layer makes your Text-to-SQL agent smarter and cheaper URL: 💸 Overview This post explains how to use a knowledge graph (Neo4j) as a semantic layer to make Text-to-SQL agents both smarter and cheaper. Instead of dumping the full schema every time, the agent retrieves only the subgraph relevant to the question — a GraphRAG approach. ❓ Challenges Solved Most implementations store schema info in static YAML or Markdown and send the whole thing on every request. That creates three serious issues. ・High token cost: transmitting the entire schema repeatedly is expensive ・Contextual noise: irrelevant tables degrade accuracy and trigger hallucinations ・Poor maintainability: flat files go stale as business semantics evolve 💡 Methodology & Proposed Approach The graph stores database structure (schemas, tables, columns, types), constraints, column dictionaries, a business glossary, and usage patterns. The agent retrieves only relevant context in three steps. ・Semantic similarity search: vector indices identify matching columns and terms ・Shortest-path search: find possible joins between identified tables ・Additional context: gather schema definitions, business terms, and sample values Results are formatted as JSON with tables and join paths in milliseconds. 🌍 Use Cases / Experimental Results The post reports improvements that matter directly for production. ・Token reduction: 20-30% on average, up to 10x on simple queries ・Accuracy (multi-table joins): ~98% (Neo4j) vs ~90% (YAML) ・Accuracy (complex CTEs with window functions): ~94% (Neo4j) vs ~85% (YAML) ・Token use scales with complexity (simple ~1,800 / multi-join ~5,000 / advanced ~7,300) The graph captures dynamic usage patterns like join frequencies and behavioral relationships, enabling continuous improvement that static files simply can't model. #TextToSQL# #KnowledgeGraph#