Automation & Agentic AI Reliability Insights
The IDE Needs a Flight Recorder, Not Just an AI Chat Panel
Antigravity, Claude Code, Codex, Cursor, and JetBrains AI already prove the agentic IDE is real. The next battle is not autonomy. It is accountability: durable traces, replayable runs, approval boundaries, and proof that the agent actually did what it claimed.
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How to Test AI Agents: A Practical Harness-Based Guide
A practical guide to testing AI agents with harnesses, traces, outcome checks, deterministic graders, and automation-testing discipline instead of vague prompt reviews.
Read Article →AI Agent Reliability Checklist for Engineering Teams
A field checklist for teams adopting AI agents: traces, outcome checks, approval gates, clean environments, regression evals, and postmortems.
Read Article →How to Debug AI Coding Agents When They Lie About Success
A practical debugging workflow for AI coding agents that confidently claim success while tests, diffs, or runtime evidence disagree.
Read Article →Agent Observability vs LLM Observability: What Actually Matters
LLM observability explains model calls. Agent observability explains action: tools, state changes, traces, approvals, failures, and outcomes.
Read Article →The AI Agent Postmortem Template I Use
A practical AI-agent postmortem template that turns vague agent failures into timelines, evidence, root cause, blast radius, and harness improvements.
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