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Linyb Geek Road
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Linyb Geek Road

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Linyb Geek Road
Linyb Geek Road
Oct 28, 2026 · Artificial Intelligence

11 Harness Engineering Mistakes That Wreck AI Teams

This article outlines eleven common misconceptions in Harness Engineering—from treating it as advanced prompting to neglecting observability and memory management—that cause AI agent teams to fail, emphasizing that Harness must be iteratively built from real failure modes, not copied, and that human engineering discipline is the true foundation.

AI agentsEngineering DisciplineEnterprise AI
0 likes · 15 min read
11 Harness Engineering Mistakes That Wreck AI Teams
Linyb Geek Road
Linyb Geek Road
Oct 26, 2026 · Artificial Intelligence

Which Tasks Suit AI Agents? 5 Questions to Decide

This article presents a framework for deciding which tasks to delegate to AI agents, emphasizing verifiable acceptance criteria and risk control over task difficulty, and outlines four collaboration modes based on verifiability and risk.

AI agentsAnthropicOpenAI
0 likes · 11 min read
Which Tasks Suit AI Agents? 5 Questions to Decide
Linyb Geek Road
Linyb Geek Road
Oct 24, 2026 · Industry Insights

Why 30% Personal AI Gains Don't Translate to Team Speed: The First Pitfall of AI Adoption

The article analyzes why individual productivity gains from AI coding tools fail to improve team delivery speed, using Huolala's year-long retrospective to reveal three pitfalls—unclear tool boundaries, context overload, and adversarial code review—and proposes standardizing tools, defining usage boundaries, marking AI-generated code, and integrating AI into workflows to turn personal gains into organizational efficiency.

AI AdoptionAI codingCode Review
0 likes · 8 min read
Why 30% Personal AI Gains Don't Translate to Team Speed: The First Pitfall of AI Adoption
Linyb Geek Road
Linyb Geek Road
Oct 23, 2026 · Artificial Intelligence

Why AI Agents Get Expensive in Production: Cost Breakdown & Control Strategies

This article explains why AI agents become slower, costlier, and less stable when deployed to real users, detailing the five-layer cost stack (tokens, tools, retries, context growth, human ops) and providing actionable strategies for budgeting, observability, and cost control.

AI AgentObservabilityRetry Strategies
0 likes · 21 min read
Why AI Agents Get Expensive in Production: Cost Breakdown & Control Strategies
Linyb Geek Road
Linyb Geek Road
Oct 19, 2026 · Artificial Intelligence

Spec-Driven Development: How Executable Specs Make AI Coding Production-Ready

This article introduces Spec-Driven Development (SDD), a methodology that replaces vague prompts with structured specifications to constrain AI coding agents, detailing a seven-element spec template, a review-gated workflow, context injection layers, automated verification aligned with acceptance criteria, and version-controlled spec management to make AI-generated code reliable for production deployment.

AI-assisted developmentSpec-Driven Developmentai-coding-agents
0 likes · 21 min read
Spec-Driven Development: How Executable Specs Make AI Coding Production-Ready
Linyb Geek Road
Linyb Geek Road
Oct 18, 2026 · Operations

HolmesGPT: AI-Powered SRE Agent That Correlates Alerts, Logs, Metrics for Root Cause Analysis

This article introduces HolmesGPT, an open-source AI SRE agent that investigates production incidents by correlating Kubernetes resources, Prometheus metrics, Loki logs, Alertmanager alerts, and deployment changes, providing evidence-based root cause hypotheses without executing fixes, and outlines practical adoption steps including read-only mode, alert-assisted analysis, and postmortem documentation.

AI SRE AgentAlertmanagerHolmesGPT
0 likes · 12 min read
HolmesGPT: AI-Powered SRE Agent That Correlates Alerts, Logs, Metrics for Root Cause Analysis
Linyb Geek Road
Linyb Geek Road
Oct 15, 2026 · Industry Insights

Palantir Ontology: Start with One Decision Loop, Not an Enterprise Map

This final article in the Palantir Ontology series argues that the first version of an ontology should be defined by a single, repeatable decision loop — not a comprehensive enterprise model — and provides a four-week rollout plan, metrics for value tracking, and guidance on when to expand based on observed reuse.

Agent IntegrationDecision LoopIncremental Development
0 likes · 23 min read
Palantir Ontology: Start with One Decision Loop, Not an Enterprise Map
Linyb Geek Road
Linyb Geek Road
Oct 14, 2026 · Artificial Intelligence

Don't Give Agents a Master Key: Building Task-Scoped Toolboxes for Enterprise AI

This article argues that enterprise AI agents should not receive unlimited system access but instead be equipped with narrowly scoped, task-specific toolboxes. It outlines four capability layers—reading facts, calling stable functions, generating suggestions, and executing defined actions—and six production gates to safely graduate agents from read-only to autonomous execution.

AI agentsEnterprise AIMCP
0 likes · 20 min read
Don't Give Agents a Master Key: Building Task-Scoped Toolboxes for Enterprise AI