Teams come to us after watching agent demos. They've seen an agent navigate complex workflows, extract data from dozens of sites, execute tasks that used to take hours. The first question: "Can we automate our entire competitive monitoring process?"

Sure. And that's exactly when things fall apart.

Early deployments taught us something unexpected. Teams treated workflows as monolithic. Either fully automated or fully manual. The agents worked fine. Trust collapsed. A pricing agent would encounter a site redesign, extract incorrect data, and suddenly the entire competitive intelligence process felt unreliable. Or teams kept humans manually verifying every data point, creating bottlenecks that negated automation value.

Operating web agents across thousands of sites revealed this: workflows aren't single decisions. They're bundles of different decision types, each requiring different infrastructure and human involvement.

Three Decision Types

When we help teams design workflows, we start by decomposing each decision point. What matters is the nature of the decision itself.

Information retrieval involves no judgment. You're gathering data from defined sources: checking inventory across hotel sites, monitoring competitor pricing, extracting product specifications. The decision was made when you designed the workflow. The challenge is web complexity. Authentication labyrinths, rate limits, site structure changes. That's infrastructure work, not judgment work. This becomes reliable, auditable infrastructure that operates at scale.

Pattern application involves codifiable logic. You're applying consistent rules: flagging price discrepancies above thresholds, routing data based on criteria, identifying anomalies. The pattern is clear enough to codify, but web variability means you need verification initially. As the pattern proves reliable across thousands of runs (handling regional variations, A/B tests, seasonal changes), verification becomes spot-checking. The infrastructure question here: how do you measure pattern reliability at scale?

Contextual judgment requires knowledge that resists codification. Should we respond to this competitive move? Does this inventory discrepancy indicate a real problem or a data artifact? These decisions stay human because the context that informs them can't be captured in rules. Organizational strategy, stakeholder relationships, market nuance. But agents collapse the information-gathering that used to bury these judgments under busywork.

The Pattern That Breaks

Competitive monitoring isn't one thing. It's information retrieval (gathering pricing data), pattern application (flagging significant changes), and contextual judgment (deciding strategic response). Each requires different observability, different error handling, different SLA commitments. When you treat the workflow as monolithic, you can't build infrastructure that serves each component appropriately.

Designing for the Right Boundaries

Before deploying agents, map each decision point to these categories:

• Information retrieval becomes automated infrastructure
• Pattern application starts with human verification, then gradually automates as you measure reliability across production runs
• Contextual judgment stays human, but agents remove the routine gathering that used to consume analyst time

The teams that get this right understand the decomposition before they build. Technical capability isn't the constraint. Organizational clarity about decision types is. They design workflows that augment human judgment rather than replacing it inappropriately or bottlenecking it unnecessarily.

Here's what scale teaches you: the best automation doesn't replace human decision-making. It removes the information-gathering that used to bury judgment under busywork. Humans focus on decisions that actually require human context.