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feedback loop

41 articles · Page 1 of 1
DataFunSummit
DataFunSummit
Aug 14, 2026 · Artificial Intelligence

How Ontology‑Driven Harness Engineering Enables Controllable Agent Execution

The article analyses why current AI agents often act beyond business rules, proposes an ontology‑driven Harness Engineering framework that provides built‑in architectural constraints, context engineering, and a verifiable feedback loop, and demonstrates its practical realization through the Knora platform with real‑world case studies.

AI agentsContext EngineeringHarness Engineering
0 likes · 20 min read
How Ontology‑Driven Harness Engineering Enables Controllable Agent Execution
DataFunSummit
DataFunSummit
Jul 29, 2026 · Artificial Intelligence

Harness Engineering’s Semantic Foundation: Ontology‑Driven, Controllable Agents

The article analyses why the current wave of AI agents often “runs away” from business rules, proposes an ontology‑driven semantic base to make agents safely controllable, details three technical pillars—architecture constraints, context engineering, and feedback loops—and illustrates the Knora implementation with a concrete work‑order change workflow.

AI agentsContext EngineeringKnora
0 likes · 20 min read
Harness Engineering’s Semantic Foundation: Ontology‑Driven, Controllable Agents
Shuge Unlimited
Shuge Unlimited
Jul 22, 2026 · R&D Management

Why Agents Guess Bugs and How Two Matt Pocock Skills Keep Debugging on Track

The article dissects Matt Pocock’s /tdd and /diagnosing-bugs skills, showing how explicit feedback signals, red‑capable commands and a six‑phase linear debugging flow prevent agents from guessing root causes and improve the reliability of AI‑assisted bug triage.

AI agentsTest‑Driven Developmentdebugging workflow
0 likes · 20 min read
Why Agents Guess Bugs and How Two Matt Pocock Skills Keep Debugging on Track
DataFunTalk
DataFunTalk
Jul 12, 2026 · Artificial Intelligence

Harness Engineering’s Semantic Foundation: Ontology‑Driven Controllable Agents

The article analyzes why the current Agent boom suffers from uncontrolled behavior, proposes a multi‑dimensional safety framework built on ontology‑driven constraints, context engineering, and feedback loops, and demonstrates its practical realization through the Knora platform with real‑world case studies.

AI agentsContext EngineeringEnterprise AI
0 likes · 20 min read
Harness Engineering’s Semantic Foundation: Ontology‑Driven Controllable Agents
Machine Heart
Machine Heart
Jul 5, 2026 · Artificial Intelligence

Is Loop Engineering the Next AI Buzzword? Key Gaps Before It Goes Mainstream

Loop Engineering has sparked intense debate as the next AI engineering paradigm, with proponents highlighting its feedback‑driven autonomous loops and critics pointing to unresolved model limits, cost, observability, and a lack of consensus on its practical advantages over Harness Engineering.

AI agentsHarness EngineeringLoop Engineering
0 likes · 6 min read
Is Loop Engineering the Next AI Buzzword? Key Gaps Before It Goes Mainstream
DataFunSummit
DataFunSummit
Jul 4, 2026 · Artificial Intelligence

How Ontology‑Driven Architecture Enables Controllable AI Agents

The article analyzes the limitations of current Agent‑centric AI solutions and proposes an ontology‑driven “Harness Engineering” framework that embeds business rules directly into the semantic layer, providing architecture constraints, context engineering, and feedback loops to achieve safe, auditable, and business‑controllable agent execution.

AI AgentContext EngineeringControl
0 likes · 18 min read
How Ontology‑Driven Architecture Enables Controllable AI Agents
DataFunSummit
DataFunSummit
Jul 2, 2026 · Artificial Intelligence

Harness Engineering’s Semantic Foundation: Ontology‑Driven Controllable Agent Execution

The article analyzes why current AI agents, despite impressive demos, often act beyond business rules, proposes an ontology‑driven semantic base called Harness Engineering to embed constraints, context, and auditability directly into the agent’s execution flow, and details the Knora implementation that demonstrates these concepts in real‑world scenarios.

AI agentsContext EngineeringKnora
0 likes · 19 min read
Harness Engineering’s Semantic Foundation: Ontology‑Driven Controllable Agent Execution
DataFunSummit
DataFunSummit
Jun 12, 2026 · Artificial Intelligence

How Ontology‑Driven Harness Engineering Enables Controllable AI Agent Execution

The article analyzes why current AI agents often act unpredictably in complex enterprises, proposes an ontology‑driven Harness Engineering framework that embeds multi‑dimensional safety constraints, context engineering, and feedback loops, and demonstrates its practical implementation through the Knora platform and a real‑world work‑order change example.

AI agentsContext EngineeringHarness Engineering
0 likes · 18 min read
How Ontology‑Driven Harness Engineering Enables Controllable AI Agent Execution
Architect
Architect
Jun 11, 2026 · Artificial Intelligence

Why More Automation Means More Human Judgment in Loop Engineering

Loop Engineering shifts focus from one‑off prompt engineering to continuous feedback loops that discover work, assign tasks, verify results, and record state, showing that the more automated the loop becomes, the more essential human judgment remains to define goals, budgets, and stop conditions.

AIAgentAutomation
0 likes · 22 min read
Why More Automation Means More Human Judgment in Loop Engineering
AI Engineering
AI Engineering
Jun 2, 2026 · Artificial Intelligence

Why Your Enterprise AI Looks Impressive Yet Produces Garbage Results

Even with the world’s best large language models, chaotic internal notes, calls, and processes turn enterprise AI output into junk; a five‑layer architecture—capture, retrieval, source‑truth, permission, and feedback—plus a six‑question test can turn a noisy "company brain" into a useful tool, as shown by Single Grain’s dramatic time‑saving results.

AI architectureAutomationEnterprise AI
0 likes · 7 min read
Why Your Enterprise AI Looks Impressive Yet Produces Garbage Results
Architect
Architect
May 30, 2026 · Artificial Intelligence

Claude Code Self‑Repair Explained: Writing Error Feedback into the Harness

The article shows how to turn Claude Code’s occasional mistakes into a reliable feedback loop by using a CLAUDE.md entry file, Hooks, Permissions and Skills, so errors become visible, verifiable and can be written back into the harness for future runs.

AI agentsAgent EngineeringCLAUDE.md
0 likes · 22 min read
Claude Code Self‑Repair Explained: Writing Error Feedback into the Harness
ShiZhen AI
ShiZhen AI
May 28, 2026 · Artificial Intelligence

Beyond Prompt Tuning: How OpenAI Built a Production-Ready Tax Agent

OpenAI’s recent tax‑agent case shows that reliable AI agents require a closed‑loop workflow—trace logging, expert feedback, systematic evaluation, and Codex‑driven code improvements—rather than mere prompt tweaking, achieving up to 97 % draft accuracy across 7,000 filings.

AI AgentCodexOpenAI
0 likes · 6 min read
Beyond Prompt Tuning: How OpenAI Built a Production-Ready Tax Agent
Code Mala Tang
Code Mala Tang
May 25, 2026 · R&D Management

How Enterprises Can Implement AI‑Native Development: Specs, Process Redesign, and Feedback Loops

The talk shows that true AI‑native development requires upgrading specifications, redesigning the entire development pipeline, establishing closed‑loop feedback, and layering rollout by business type, rather than merely adding an AI coding assistant, and presents data from ten pilot projects demonstrating efficiency gains.

AI-native developmentEnterprise AISoftware Engineering
0 likes · 10 min read
How Enterprises Can Implement AI‑Native Development: Specs, Process Redesign, and Feedback Loops
DataFunTalk
DataFunTalk
May 4, 2026 · Artificial Intelligence

Building a Semantic Foundation for Harness Engineering: Ontology‑Driven Controllable Agents

The article analyzes why current AI agents lack reliable control, defines a multi‑dimensional safety framework, and proposes an ontology‑driven architecture—implemented in the Knora platform—that embeds business rules directly into agents, enabling deterministic validation, auditability, and large‑scale efficiency gains.

AIAgentBusiness Control
0 likes · 17 min read
Building a Semantic Foundation for Harness Engineering: Ontology‑Driven Controllable Agents
Data Party THU
Data Party THU
Apr 27, 2026 · Artificial Intelligence

Three Overlooked Failure Points in RAG Pipelines and How to Build a Feedback Loop

The article analyzes silent failures in Retrieval‑Augmented Generation pipelines, identifies three gaps—retrieval relevance, LLM confidence masking uncertainty, and missing fault signals—and presents a practical feedback‑loop architecture with relevance gating, post‑generation evaluation, session tracing, and user‑signal logging to make production RAG systems trustworthy.

LLMRAGfeedback loop
0 likes · 13 min read
Three Overlooked Failure Points in RAG Pipelines and How to Build a Feedback Loop
DataFunSummit
DataFunSummit
Apr 24, 2026 · Artificial Intelligence

How Ontology‑Driven Agents Enable Controllable Execution in Harness Engineering

The article analyzes why current AI agents often act unpredictably, defines a multi‑dimensional notion of safe and controllable execution, proposes an ontology‑driven semantic foundation with architecture constraints, context engineering, and feedback loops, and demonstrates the Knora implementation through concrete workflow examples.

AI AgentContext EngineeringKnora
0 likes · 20 min read
How Ontology‑Driven Agents Enable Controllable Execution in Harness Engineering
DataFunSummit
DataFunSummit
Apr 20, 2026 · Artificial Intelligence

Why Ontology‑Driven Agents Are the Key to Safe, Controllable Enterprise AI

The article analyses the current hype around AI agents, explains why pure prompt‑based constraints fail in complex business scenarios, and proposes an ontology‑driven Harness Engineering framework that embeds architectural constraints, context engineering, and a traceable feedback loop to achieve secure, business‑level controllability.

AI agentsContext EngineeringEnterprise AI
0 likes · 21 min read
Why Ontology‑Driven Agents Are the Key to Safe, Controllable Enterprise AI
Ma Wei Says
Ma Wei Says
Apr 18, 2026 · Artificial Intelligence

Designing AI Agent Memory: Evolving from Context Stitching to a Self‑Optimizing Cognitive System (Part 2)

The article details a five‑stage framework that upgrades an AI agent's memory from simple token trimming to a dynamic, self‑learning cognitive system, covering vector retrieval, memory lifecycle management, user profiling, explicit/implicit feedback loops, and long‑term governance.

AI AgentHybrid RetrievalMemory Architecture
0 likes · 12 min read
Designing AI Agent Memory: Evolving from Context Stitching to a Self‑Optimizing Cognitive System (Part 2)
Architecture Musings
Architecture Musings
Apr 7, 2026 · Artificial Intelligence

Why I Reject the Equation Agent = LLM + Harness

The article argues that equating an AI agent with merely an LLM plus engineering harness oversimplifies the agent’s true cognitive core—memory, planning, and tool use—and warns that such a formula risks cementing a temporary engineering compromise into a lasting ontological definition.

AI planningAgent ArchitectureAutonomous Agents
0 likes · 10 min read
Why I Reject the Equation Agent = LLM + Harness
Architect's Ambition
Architect's Ambition
Mar 18, 2026 · Artificial Intelligence

From Zero to a Real AI Agent: Master Its Core Essence, Not Just API Calls

The article explains why an AI Agent is more than a simple LLM API call, outlines its four essential modules—memory, planning, tool use, and feedback—shows how they differ from ordinary models, and offers practical steps and common pitfalls for building a production‑grade single‑agent system.

AI AgentLLMMemory
0 likes · 13 min read
From Zero to a Real AI Agent: Master Its Core Essence, Not Just API Calls
Software Engineering 3.0 Era
Software Engineering 3.0 Era
Mar 10, 2026 · Fundamentals

Feedback Loops Secure Quality in SE 3.0; Second‑Order Control Gets Smarter

By applying control theory, the article explains how multi‑level feedback loops form the structural guarantee of quality in Software Engineering 3.0, distinguishes first‑ and second‑order control, maps SE 1.0‑3.0 evolution, and proposes an agent‑driven DevOps model that enables real‑time, self‑evolving software systems.

AI agentsControl TheorySE 3.0
0 likes · 10 min read
Feedback Loops Secure Quality in SE 3.0; Second‑Order Control Gets Smarter
Data STUDIO
Data STUDIO
Oct 21, 2025 · Artificial Intelligence

Building a Self‑Learning LangGraph Memory System with Feedback Loops and Dynamic Prompts

This article walks through the design and implementation of a two‑layer memory architecture for LangGraph agents, covering short‑term and long‑term stores, various storage back‑ends, prompt engineering, utility functions, node definitions, human‑in‑the‑loop interrupt handling, and how user feedback is captured and used to continuously update the agent’s behavior.

AgentLLMLangGraph
0 likes · 43 min read
Building a Self‑Learning LangGraph Memory System with Feedback Loops and Dynamic Prompts
Bighead's Algorithm Notes
Bighead's Algorithm Notes
Oct 14, 2025 · Artificial Intelligence

How TS‑Agent Uses LLMs and Reflective Feedback to Automate Financial Time‑Series Modeling

TS‑Agent is a modular LLM‑driven framework that formalizes financial time‑series modeling as a three‑stage iterative decision process, leveraging structured knowledge bases, dynamic memory, and a feedback‑driven code‑editing loop to outperform AutoML baselines in accuracy, robustness, and auditability.

AutoMLKnowledge BaseLLM
0 likes · 12 min read
How TS‑Agent Uses LLMs and Reflective Feedback to Automate Financial Time‑Series Modeling
DataFunSummit
DataFunSummit
Oct 9, 2025 · Artificial Intelligence

Why AI Coding Agents Still Struggle: Context Limits, Knowledge Gaps, and the Road to Human‑Like Assistants

This talk examines the core challenges facing AI coding agents—limited context windows, knowledge accumulation, and software‑engineering complexity—while outlining practical solutions such as context providing, RAG, fine‑tuning, online learning, feedback loops, and multi‑agent collaboration to move toward truly human‑like, continuously learning coding assistants.

AI codingCoding AgentRetrieval-Augmented Generation
0 likes · 24 min read
Why AI Coding Agents Still Struggle: Context Limits, Knowledge Gaps, and the Road to Human‑Like Assistants
Dual-Track Product Journal
Dual-Track Product Journal
Jul 25, 2025 · Product Management

Mastering Product Requirements: From Deep User Insights to Seamless Delivery

This guide walks product managers through the full lifecycle of requirement analysis, management, prioritization, delivery, and verification, emphasizing data‑driven decisions, multi‑dimensional evaluation, clear acceptance criteria, and continuous feedback loops to ensure products truly solve user problems and align with business goals.

Requirement Analysisdeliveryfeedback loop
0 likes · 7 min read
Mastering Product Requirements: From Deep User Insights to Seamless Delivery
DataFunTalk
DataFunTalk
Nov 2, 2024 · Artificial Intelligence

Embodied Intelligence: Core Concepts, Three Elements, and Four Functional Modules

This article introduces embodied intelligence, explains its basic definition, three essential elements (body, intelligence, environment), and details the four functional modules—perception, decision, action, and feedback—while describing the sensors and algorithms that enable physical AI systems to interact with the real world.

AI roboticsEmbodied Intelligenceaction module
0 likes · 13 min read
Embodied Intelligence: Core Concepts, Three Elements, and Four Functional Modules
Tencent Cloud Developer
Tencent Cloud Developer
Nov 21, 2023 · Fundamentals

Understanding Business Debt and Physical Debt: Structured Thinking and Management

The article argues that both business and personal health debts inevitably accumulate like entropy, but can be managed through structured thinking—identifying, categorizing, prioritizing, and continuously monitoring and feedback‑driven actions such as mindfulness, exercise, and disciplined system maintenance—to reduce complexity and sustain growth.

Conway's LawHealth Managemententropy
0 likes · 18 min read
Understanding Business Debt and Physical Debt: Structured Thinking and Management
Continuous Delivery 2.0
Continuous Delivery 2.0
Aug 22, 2023 · Fundamentals

Why End‑to‑End Testing Strategies Often Fail and How to Build Effective Feedback Loops

The article examines why end‑to‑end testing strategies frequently break in practice, illustrates common pitfalls through a realistic scenario, and proposes a more reliable testing pyramid that emphasizes fast, reliable, isolated unit and integration tests while establishing quick feedback loops for developers.

End-to-Endfeedback loopintegration testing
0 likes · 10 min read
Why End‑to‑End Testing Strategies Often Fail and How to Build Effective Feedback Loops
Architect's Guide
Architect's Guide
Oct 4, 2022 · Fundamentals

Key Insights on System Architecture, Evolution, and Feedback Loops

This article shares practical experiences and concepts about software architecture, covering stakeholder‑driven definitions, non‑functional requirements, iterative evolution, closed‑loop feedback, microservice adoption, organizational impact, and the soft skills needed for effective architects.

DevOpsfeedback loopsoft skills
0 likes · 14 min read
Key Insights on System Architecture, Evolution, and Feedback Loops
HelloTech
HelloTech
Nov 22, 2021 · Operations

Five Key Stages to Improve Tech Talk Event Operations

By launching an MVP, standardizing SOPs, instituting speaker quality controls, implementing a Kirkpatrick‑based feedback loop, and enhancing participation through user‑centered tweaks, the TechTalk platform transformed from ad‑hoc sessions to a stable, high‑impact bi‑weekly event, illustrating the power of iterative, cross‑functional process improvement.

Internal TrainingProcess OptimizationSOP
0 likes · 10 min read
Five Key Stages to Improve Tech Talk Event Operations
DataFunSummit
DataFunSummit
Aug 10, 2021 · Artificial Intelligence

Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding

The article examines the rapid growth of recommendation systems, highlighting the need for industrial‑grade benchmarks, transparent explainability, and addressing algorithmic confounding caused by feedback loops, while discussing how these issues affect both users and content providers in the AI‑driven ecosystem.

AIExplainabilitybenchmark
0 likes · 12 min read
Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding
Yanxuan Tech Team
Yanxuan Tech Team
Nov 27, 2020 · Product Management

Designing Effective Decision‑Type Products: Core Principles and Practices

This article explains what decision‑type products are, outlines their evolution stages, and details the essential design roadmap—including domain modeling, data value extraction, process standardization, system integration, and building a sustainable decision‑feedback loop—to help product teams create automated, data‑driven solutions that continuously improve business outcomes.

Domain modelingSimulationdata value
0 likes · 21 min read
Designing Effective Decision‑Type Products: Core Principles and Practices
Continuous Delivery 2.0
Continuous Delivery 2.0
Feb 25, 2020 · Operations

Leveraging Feature Flags for Controlled Changes and Rapid Feedback Loops

Feature flags enable controlled system changes, allowing teams to monitor business and technical metrics, quickly roll back harmful updates, and operate within a rapid feedback loop that informs subsequent iterations, though many modern product teams struggle to integrate flag platforms with analytics systems for richer insights.

Continuous DeliveryFeature FlagsSoftware Operations
0 likes · 3 min read
Leveraging Feature Flags for Controlled Changes and Rapid Feedback Loops
DataFunTalk
DataFunTalk
Aug 28, 2019 · Artificial Intelligence

Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding

Recommendation systems, driven by recent economic and deep‑learning advances, face critical issues such as the lack of unified industrial benchmarks, limited explainability for users and content providers, and feedback‑loop induced data confounding, prompting calls for open datasets, transparent models, and collaborative optimization across stakeholders.

AIExplainabilityRecommendation Systems
0 likes · 15 min read
Challenges and Future Directions for Recommendation Systems: Benchmarks, Explainability, and Data Confounding