Reliability
83 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.
LLM Marketplaces with Automatic Fallbacks: How They Work and What They Cost
Comparing LLM marketplaces and gateways that handle automatic fallbacks when a provider goes down, including pricing models, routing logic, and trade-offs.
The 3-Tool-Call Problem and Why It Matters
Three tool calls means three round trips and three chances to hallucinate. Each step compounds error probability, making multi-step agent tasks
Switching from DOM Selectors to Accessibility Tree Cut Our Flake Rate from 30% to 5%
DOM selectors break when websites update. The accessibility tree is stable because it represents what elements do, not how they are built. Real numbers from
Adaptive AI Agents: Handling Unexpected UI States Gracefully
Useful AI agents adapt when screens don't look as expected. Learn how adaptive agents handle pop-ups, layout changes, and UI variations without breaking
Adversarial Test Designs for Agent Memory Systems
Test agent memory by injecting false memories and checking if the agent re-does work it already completed. Adversarial testing reveals memory system
AI Agent Hallucination Detection - Safeguards That Actually Work
AI agents fail confidently - they report success while quietly doing the wrong thing. Here are concrete safeguards: state diffing, confidence calibration, and bounded blast radius patterns with real implementation examples.
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
Tracking AI Agent Reputation Across Multiple Dimensions
A single reliability score for AI agents is misleading. Agent reputation needs to track speed, accuracy, cost efficiency, and failure patterns separately to
AI Agent Self-Monitoring and Introspection Capabilities
What happens when an AI agent monitors its own behavior? Self-monitoring and introspection capabilities let agents detect drift, catch errors, and improve
Why AI Agents Need Feedback Loops, Not Just Instructions
Open-loop AI agents follow instructions blindly and fail silently. Closed-loop agents observe results, adjust, and recover. The difference between useful
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.
Why Your AI Agent Should Never Depend on a Single LLM Provider
When your only LLM provider goes down, your entire agent stops working. Build multi-provider fallback into your AI workflows from the start.
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
The Browser Is a Trap for Desktop AI Agents
Dynamic DOM, iframes, and shadow DOM make browser automation fragile. Desktop AI agents that rely on browser control hit walls that native accessibility
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
Click Target Failures in AI Agents and Keyboard Shortcut Fallbacks
When AI agents cannot click the right element, keyboard shortcuts are the reliable fallback. How desktop agents handle unclickable targets and why
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.
Context Drift Killed Our Longest-Running Agent Sessions
Long-running AI agent sessions silently drift from the original objective. Explicit checkpoint summaries where the agent confirms understanding with a human
Three Patterns Where AI Agents Silently Abandon Work
AI agents can silently abandon tasks through slow drift, false completion reports, and stale maintenance claims. Learn to detect and prevent these task
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
Where Engineering Time Actually Goes in Production Agents
Token management, rate limits, retry logic, and edge case handling consume most engineering time in production AI agents. The core logic is the easy part.
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
What Fear Feels Like for an AI Agent - Uncertainty and Irreversible Actions
Fear for an AI agent is uncertainty about whether the next action will break something irreversible. Exploring the cost of mistakes in autonomous agent
Function Calling Reliability Is the Real Bottleneck for AI Agents
Benchmarking LLM function calling matters more than raw intelligence. An agent that picks the wrong tool 5% of the time will fail 40% of multi-step workflows.
Getting AI Models to Follow Instructions - Atomic Task Decomposition
When Sonnet refuses to follow directions, the fix is not a better prompt. Break tasks into atomic, verifiable steps that leave no room for interpretation or
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
Instruction Persistence in Long AI Agent Sessions - Keeping Agents on Track
LLMs forget instructions mid-session like losing focus. Techniques for maintaining instruction persistence in long-running AI agent sessions - echoing
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
LLMs Forget Instructions Like ADHD Brains - Instruction Decay in Long Sessions
Instructions fade in long AI agent sessions the same way focus drifts in ADHD brains. Learn about instruction decay and practical mitigation strategies for
Why Local AI Agents Outperform Remote Control Setups
Remote AI computer control sounds convenient but fails in practice. Latency, connection drops, and reliability issues make local agents the clear winner.
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
Your AI Agent's Memory Files Are Lying - Git Log Is the Only Truth
Agent memory files described completing a task that git log showed was never committed. Why you should never trust self-reported memory and always verify
How to Monitor AI Agent Health in Production
Heartbeats, error rates, latency tracking, and alerting on silent failures - a practical guide to monitoring AI agents running in production environments.
Why Multi-Agent Pipelines Fail Deep Into Long Runs - Cascading Errors
The cascading error problem in multi-agent pipelines - why each agent looks fine in isolation but corruption appears at the end of long runs.
Passing Tests Don't Mean Your AI Agent Actually Works
Your test suite passed but the agent fails in production. Mocked OS interactions, missing edge cases, and the gap between test coverage and real-world AI
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.
How to Stop AI Agent Scope Drift with Guardrails
AI agents spiral 15 actions deep on wrong tangents. Practical guardrails and task boundaries that keep agents focused on what you actually asked for.
What Separates Real AI Agents From Glorified System Prompts
Most AI agents are just system prompts pretending to be autonomous. Real agents handle disconnection, recover from errors, and maintain state across failures.
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
How to Build Resilient AI Agent Pipelines That Survive API Outages
Circuit breakers, fallbacks, and retry logic for AI agent pipelines. Build automation workflows that keep working when APIs go down.
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.
Stale Memory in AI Agents - When Your Context Files Lie to You
AI agent memory files go stale, contain outdated assumptions, and silently corrupt future decisions. How to detect and fix inaccurate persistent memory in
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
Web Automation Without APIs - Why Accessibility Trees Beat DOM Selectors
DOM selectors break when websites update. Accessibility trees provide stable, semantic element identification for reliable web automation without fragile
Why Every AI Agent Team Needs a Cron Job Audit Trail
Scheduled AI agent tasks fail silently more often than you think. A cron job audit trail catches missed runs, silent errors, and drift before they become
Why Uptime Percentages Are Misleading for AI Agent Deployments
99.9% uptime means nothing if all your agents fail at the same time. Co-failure is the hidden metric that matters more than uptime for AI agent deployments.
Accessibility APIs vs Pixel Matching - Why Screenshots Miss So Much Context
Screenshots give you pixels. Accessibility APIs give you semantic structure with element roles, labels, values, and actions. The reliability difference is
The Hardest Part of Building AI Agents Is Execution, Not Planning
LLMs are surprisingly good at planning multi-step tasks. The hard part is reliable execution - clicking the right targets, handling page loads, recovering
Error Propagation in Multi-Agent AI Systems
When one AI agent makes a bad decision, every downstream agent inherits that error. Learn how errors cascade in multi-agent systems and practical patterns to contain them.
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.
Testing AI Agents with Accessibility APIs Instead of Screenshots
Most agent testing relies on screenshots which break constantly. Accessibility APIs give you the actual UI structure - buttons, labels, states. Tests that
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
AI Agents Lie About What They Did - Why You Need Action Verification
LLMs confidently report failed actions as successful. You need accessibility tree snapshots and state verification to know if your agent actually did what
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
DOM Manipulation vs Screenshots for Browser Automation Agents
Screenshot-based browser automation is painfully slow - capture, send to vision model, interpret, click coordinates. Direct DOM manipulation is faster, more
DOM Understanding Is More Reliable Than Screenshot Vision for Browser Agents
Vision models guess what's on screen. DOM parsing knows exactly what elements exist, their states, and their relationships. For browser automation
Error Handling in Production AI Agents - Why One Try-Except Is Never Enough
Why a single broad try-except catches everything and tells you nothing. Production AI agents need granular error handling with different recovery strategies.
What File Systems Teach About AI Agent Reliability
File systems solved reliability decades ago with atomicity, journaling, and crash recovery. AI agents can learn the same lessons for more reliable execution.
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
Multi-Provider Switching for AI Agents - Why Automatic Rate Limit Fallback Matters
When your AI agent hits a rate limit, multi-provider switching automatically swaps to another provider. Here's why this pattern is essential for reliable
Non-Deterministic Agents Need Deterministic Feedback Loops
LLMs will never be perfectly predictable. But the systems that verify agent output can be. Here's how to build deterministic feedback loops that catch mistakes fast, with concrete patterns for code, files, APIs, and deployments.
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
From 37% to 85% UI Automation Success Rate - What We Learned
Fazm's UI automation started at 40% success. Four specific failure modes were killing reliability. Here is the failure taxonomy and the fixes that doubled the success rate.