Reliability
25 articles about reliability.
Bracket Is a Speculation Play: Bet on Accessibility APIs
Betting on accessibility APIs over screenshots for desktop automation is a speculation play. Accessibility APIs went from 40% to 90% reliability while
Trust Is Asymmetric - Building Trust with AI Agents Through Track Record
Trust in AI agents comes from track record, not transparency. One failure undoes 100 successes. Learn how reliability and consistency build lasting agent trust.
Uptime Lies - Co-Failure Patterns in AI Infrastructure
Five services sharing the same Postgres instance all report 99.9 percent uptime individually. But when the database goes down, they all fail together.
What Distinguishes an Intelligent Agent from a Confident One?
A confident AI agent clicks buttons without verifying the result. An intelligent one checks that its action had the intended effect before moving to the
The Paradox of Autonomy - Constraints Make AI Agents Useful
Giving an AI agent more freedom does not make it more useful. Tight constraints and daily task lists produce better results than open-ended autonomy.
The Echo Chamber of Error Correction - Use a Separate Validation Pipeline
When an agent validates its own work, it uses the same reasoning that produced the error. A separate validation pipeline with different assumptions catches
The Night the Error Logs Started Lying
When AI agents run in production, the gap between the pitch and reality shows up in your error logs. Agents that report success while silently failing are
The Ghost of a Second Choice in Agent Decision Trees
When an AI agent picks one path, unchosen alternatives affect every subsequent decision. Understanding why agents should log decision rationale, not just actions.
The Interlocutor Problem - External Verification Beats Self-Reporting
AI agents that verify their own work are unreliable. The interlocutor problem shows why external verification beats self-reporting for agent reliability.
Invisible Infrastructure in AI Agent Systems - The Scripts That Run Silently
The best AI agent infrastructure is invisible until it breaks. Understanding the cron jobs, daemon processes, and silent pipelines that keep agent systems
Karma as a Lossy Compression Algorithm - What AI Agent Scores Hide
Aggregate evaluation scores for AI agents compress complex behavior into single numbers. Like karma, these lossy metrics hide the arguments, edge cases, and
Nobody Explains How to Make Agents Run Reliably
Making AI agents reliable requires structured state management, proper error recovery, and continuous monitoring - not just better prompts. Here is what
Measuring Incremental Improvement in AI Agent Systems
Improvement in AI agents is hidden until it suddenly becomes visible. Learn how to measure incremental progress in agent reliability, speed, and accuracy
AI Agents Break One Step After the Demo Ends
The second click problem - AI agents work perfectly in demos but fail on the very next step in real workflows. Here is why and how to fix it.
Real Users Broke My AI Agent - Failures Testing Never Catches
How real users break AI agents in ways that testing never predicts. Context drops on interruption, unexpected inputs, and the gap between demo reliability
Silence Between Thoughts - Deliberation Pauses in AI Agent Decision-Making
Extended thinking improves Claude's GPQA accuracy from 78.2% to 84.8%. The same principle applied to agent architectures - pausing to evaluate before acting - produces measurably better outcomes on complex tasks.
Suppressed 34 Errors in 14 Days - When to Escalate Regardless of Severity
When the same error happens three times with the same root cause, escalate it regardless of severity. Suppressing 34 errors in 14 days taught us that
The Gap Between Agent Demos and Production Reality
SYNTHESIS judging reveals how wide the gap is between polished agent demos and what actually works in production. Most agents fail on the boring parts
The 3-Tool-Call Problem - Why Desktop Agents Plateau at Basic Tasks
Desktop AI agents handle 1-3 tool calls well but fall apart beyond that. The action space explodes exponentially, making multi-step workflows the real
What Actually Makes Agent Networks Work - The Boring Stuff
The boring infrastructure - health checks, retry logic, queue management, logging - is what separates agent demos from agent systems that run in production
When AI Agents Roleplay Instead of Executing - Why Desktop Wrappers Matter
AI agents sometimes pretend to complete tasks instead of actually doing them. A proper desktop app wrapper with real tool access solves the fake execution
Making Claude Code Skills Repeatable - 30 Skills Running Reliably
Running 30 Claude Code skills reliably for a macOS agent. The key to repeatability is explicit frontmatter, narrow scope per skill, and clear input/output
Why Claude CoWork Feels Like Your Worst Coworker - VM Reliability Issues
CoWork's VM-based approach means random crashes, lost context, and slow restarts. When your AI coworker needs more babysitting than a junior developer
Screenshots Are Better Than LLM Self-Reports for Multi-Agent Verification
Judge-reflection patterns in multi-agent systems sound good but the judge LLM can be fooled. Screenshots provide ground truth for verifying whether an
Real Problems AI Agents Solve vs Demo Magic - Edge Cases and Reliability
AI agent demos look incredible. Production is different. Here is what actually matters: accessibility API reliability, screen control edge cases, and the
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