CURRENT | Foundations

Field Guide

The Misidentification That Breaks Production Contracts

By Rina Takahashi— January 14, 2026

Feature image for article: The Misidentification That Breaks Production Contracts

Your web agent extracts hotel prices perfectly for three months, then starts returning subtly different data structures—not errors, just variations. Your competitor's system breaks entirely when a site redesigns, but fails predictably. Same automation label, opposite failure modes. The industry treats deterministic automation and probabilistic agents as interchangeable solutions. When you're operating across thousands of sites with real production dependencies, that misidentification becomes operationally expensive. It determines what reliability guarantees are architecturally possible.

Field Guide

The Misidentification That Breaks Production Contracts

By Rina Takahashi— January 14, 2026

Your web agent extracts hotel prices perfectly for three months, then starts returning subtly different data structures—not errors, just variations. Your competitor's system breaks entirely when a site redesigns, but fails predictably. Same automation label, opposite failure modes. The industry treats deterministic automation and probabilistic agents as interchangeable solutions. When you're operating across thousands of sites with real production dependencies, that misidentification becomes operationally expensive. It determines what reliability guarantees are architecturally possible.

Tools & Techniques

Tools in Context

Why 30,000 Teams Choose the Slower Parser

The most popular HTML parser in web automation is five times slower than its alternatives. Cheerio dominates with 30,000 stars and 11 million weekly downloads while faster options sit in obscurity. Teams aren't missing the benchmarks. They're optimizing for the real bottleneck: hours spent maintaining extraction logic across thousands of sites that change without warning, not milliseconds spent parsing HTML strings.

Tools in Context

When Five Milliseconds Costs Three Hours

At ten thousand pages, parsing speed is a benchmark number. At a million pages, it's three hours of compute time. The gap between fast and slow HTML parsers stops being academic when you're processing massive volumes daily. Teams reaching for node-html-parser aren't chasing optimization for its own sake. They've hit the scale where that 10-millisecond difference per page determines whether their pipeline meets its throughput requirements.

Tools & Techniques

Tools in Context

Why 30,000 Teams Choose the Slower Parser

The most popular HTML parser in web automation is five times slower than its alternatives. Cheerio dominates with 30,000 stars and 11 million weekly downloads while faster options sit in obscurity. Teams aren't missing the benchmarks. They're optimizing for the real bottleneck: hours spent maintaining extraction logic across thousands of sites that change without warning, not milliseconds spent parsing HTML strings.

Tools in Context

When Five Milliseconds Costs Three Hours

At ten thousand pages, parsing speed is a benchmark number. At a million pages, it's three hours of compute time. The gap between fast and slow HTML parsers stops being academic when you're processing massive volumes daily. Teams reaching for node-html-parser aren't chasing optimization for its own sake. They've hit the scale where that 10-millisecond difference per page determines whether their pipeline meets its throughput requirements.

Tools & Techniques

Tools in Context

Why 30,000 Teams Choose the Slower Parser

The most popular HTML parser in web automation is five times slower than its alternatives. Cheerio dominates with 30,000 stars and 11 million weekly downloads while faster options sit in obscurity. Teams aren't missing the benchmarks. They're optimizing for the real bottleneck: hours spent maintaining extraction logic across thousands of sites that change without warning, not milliseconds spent parsing HTML strings.

Tools in Context

When Five Milliseconds Costs Three Hours

At ten thousand pages, parsing speed is a benchmark number. At a million pages, it's three hours of compute time. The gap between fast and slow HTML parsers stops being academic when you're processing massive volumes daily. Teams reaching for node-html-parser aren't chasing optimization for its own sake. They've hit the scale where that 10-millisecond difference per page determines whether their pipeline meets its throughput requirements.

In Dialogue With Complexity

An Interview with Port 9222

In Dialogue With Complexity

An Interview with Port 9222

Pattern Recognition

Agents Deploy Faster Than Security Can Follow

Enterprises are shipping agents into production without the security infrastructure to contain them. Exabeam just launched Agent Behavior Analytics because traditional monitoring doesn't work for this new digital workforce. Chinese cyberspies already weaponized Claude Code in September attacks. Only 8.6% of companies actually have agents in production, yet 72% of S&P 500 firms now flag AI as material risk.

The gap is obvious: agents get broad permissions to stay functional, but organizations lack basic controls. No inventory of deployed agents. No behavioral monitoring. No approval loops for high-impact actions.

The fix isn't complicated. Define least-privilege access before deployment. Monitor for anomalies. Treat agent failures like production incidents, not experiments.

Pattern Recognition

Agents Deploy Faster Than Security Can Follow

Enterprises are shipping agents into production without the security infrastructure to contain them. Exabeam just launched Agent Behavior Analytics because traditional monitoring doesn't work for this new digital workforce. Chinese cyberspies already weaponized Claude Code in September attacks. Only 8.6% of companies actually have agents in production, yet 72% of S&P 500 firms now flag AI as material risk.

The gap is obvious: agents get broad permissions to stay functional, but organizations lack basic controls. No inventory of deployed agents. No behavioral monitoring. No approval loops for high-impact actions.

The fix isn't complicated. Define least-privilege access before deployment. Monitor for anomalies. Treat agent failures like production incidents, not experiments.

Deployment confusion:

Only 8.6% have production agents despite 80% claiming adoption, revealing fundamental disagreement about what deployment means.

Attack vectors:

Tool misuse and privilege escalation dominated Q4 2025 incidents, while memory poisoning carries persistence risk despite lower frequency.

Identity gaps:

Agents don't fit existing IdP models, never change passwords, and have no HR offboarding process when decommissioned.

Risk disclosure:

S&P 500 companies flagging AI risk jumped from 12% in 2023 to 72% in 2025 as reality replaced hype.

Board pressure:

2026 marks the shift from counting pilots to counting dollars as pragmatism replaces optimism in enterprise strategy.

Questions Worth Asking

The questions that matter most aren't the ones vendors answer in demos. They're the ones that surface three months into production, when the initial excitement has worn off and you're dealing with the actual system.

We've watched enough teams move from prototype to production to recognize the patterns. The questions that predict success aren't about capabilities. They're about operational reality. What happens when your agent encounters the messiness of real users, real data, and real infrastructure.

These are the questions we ask ourselves when evaluating approaches.

Questions Worth Asking

The questions that matter most aren't the ones vendors answer in demos. They're the ones that surface three months into production, when the initial excitement has worn off and you're dealing with the actual system.

We've watched enough teams move from prototype to production to recognize the patterns. The questions that predict success aren't about capabilities. They're about operational reality. What happens when your agent encounters the messiness of real users, real data, and real infrastructure.

These are the questions we ask ourselves when evaluating approaches.

Tool Reliability

What's Your Tool Calling Failure Rate?

Production systems see tool calling fail 3-15% of the time. Does your architecture handle that gracefully, or does every failure cascade into user-visible errors? You need to build for failure from day one, not discover it three months in.

Context Engineering

Are You Drowning Your Agent in Context?

Most production failures are context failures, not model failures. Models with 200K token windows still degrade when critical information gets buried in noise. Just-in-time retrieval beats pre-loading everything. Stop treating context windows like infinite resources.

Production Gap

What Changes Between Demo and Production?

Demos lack cost-per-query budgets, p99 latency targets, and security postures guarding against prompt injection. A demo for five users is nearly free. An agent for 500 employees making thousands of queries daily can shock you with API bills.

Human Intervention

How Many Steps Before Human Intervention?

68% of production agents execute at most 10 steps before requiring human oversight. If your architecture assumes fully autonomous operation, you're designing for the exception. Build for supervised autonomy from day one.

Day-2 Operations

Who Maintains This After Launch?

When backend APIs undergo breaking changes, does your architecture insulate the agent or require prompt rewrites? The engineering discipline required for maintenance often exceeds the initial deployment effort. Agents degrade constantly without active management.

Integration Reality

Can Your Data Architecture Support Agents?

Traditional enterprise systems weren't designed for agentic interactions. Most agents still rely on APIs and conventional data pipelines, creating bottlenecks. Nearly half of organizations cite data searchability as a challenge. Your data architecture determines what's possible.