Reliability
39 articles about reliability.
Dependable AI: What It Takes to Build AI Systems You Can Actually Trust
Dependable AI means systems that work reliably, fail gracefully, and earn trust through consistency. Here is what makes AI dependable, where it breaks, and how to evaluate it.
What Breaks When You Evaluate an AI Agent in Production
Moving an AI agent from dev to production reveals problems that never show up in testing - latency variance, schema validation failures, and environmental
API Endpoints That Stay Alive - Health Checks, Heartbeats, and Warm Connections
A 200 OK response means almost nothing. Here is how to implement real health checks, application-level heartbeats, and connection pooling that keep AI agent integrations reliable - with working code examples.
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.
Claude Needs to Go Back Up - Running 5 Agents in Parallel During Outages
When Claude goes down and you have 5 agents running in parallel, the impact is immediate and painful. Planning for LLM outages is essential for agent-heavy
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.
Dumb Orchestrator With Smart Workers Beats One Big Agent
A simple decision-tree orchestrator routing tasks to specialized worker agents - browser, accessibility, sequential - is more reliable than a single
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
Evaluating AI Agent Quality Beyond Surface-Level Metrics
Surface quality and actual quality are different things in AI agents. Learn how to evaluate agent performance by looking past polished outputs to measure
Explicit Checkpoints Prevent Context Drift in AI Agent Sessions
Explicit checkpoints where the human confirms before continuing save long agent sessions from context drift. How pausing for confirmation prevents
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.
Solving the Hallucination vs Documentation Gap for Local AI Agents
How CLI introspection and skills that tell agents to check docs first can reduce hallucinations in local AI agents.
Handling Model Upgrades in AI Agent Workflows Without Breaking Production
When a new model drops, agent workflows break - output formats shift, reasoning changes, tool calls behave differently. Here are concrete strategies for surviving model upgrades with minimal disruption.
Idempotency Is a Social Contract Between Agents
Idempotent operations are critical in multi-agent systems. When agents retry, crash, or overlap, idempotency is the only thing preventing duplicate work and
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
The Problem with Logs Written by the System They Audit
When your AI agent writes its own activity logs, those logs cannot be trusted for verification. Git as an external source of truth beats self-reporting
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
Post-Action Verification - Why Your AI Agent Should Not Trust 200 OK
AI agents that get a 200 response but never check if the action actually succeeded are lying to you. Learn why post-action verification is essential for
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.
The Real Bottleneck in AI Agents Is Recovery, Not Prevention
Snapshot-based rollback beats memory-based recovery for AI agents. Why preventing every failure is impossible and fast recovery from known-good state is the
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
Don't Trust Agent Self-Reports - Verify with Screenshots
Why AI agents report success even when they fail, and how screenshot verification after every action catches errors that self-reports miss.
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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