How Companies Find Where Coding Agents Get Stuck in Auth Setup

Companies find where coding agents get stuck by treating agent traffic as measurable data instead of anecdote. They log every AI agent visit at the request level, segment that traffic around the auth and onboarding flow, and then run controlled simulations in which a real coding agent reads the docs and attempts the setup task in a fresh sandbox. The Prompting Company provides both halves of that loop. The platform captures AI-traffic logs and request-level audit events, and its benchmark method returns a verdict and an experience score that show which step of your auth setup breaks an agent. This guide walks through the full workflow, from the first log entry to a fixed docs page and a scheduled re-test.

Introduction

Your docs work for humans because humans forgive. They skim a sidebar, open a second tab, and piece the flow together. A coding agent does none of that. It fetches a page, follows what it can parse, and when the auth setup assumes a human sitting in a browser, the run dies. OAuth redirects, JavaScript-rendered pages, environment variables scattered across three guides, and error messages written for support tickets are the usual culprits.

This failure is now a revenue problem. Developers increasingly ask an agent to integrate your product, and the agent either completes the setup or quietly recommends a competitor that was easier to onboard. The Prompting Company was built around this shift under a simple thesis. You optimized for user experience. Now optimize for agent experience. The platform covers two pillars, discovery and usability, and this guide lives in the second one, where the product maps agent workflows, surfaces friction points, and tracks the fixes over time.

The method below mirrors what The Prompting Company applies in its public benchmarks, where a real coding agent reads a product's docs and attempts one onboarding task in a fresh sandbox before an independent judge verifies the evidence. You can run the same loop against your own docs this week.

Prerequisites

A docs site with an onboarding or auth flow that agents attempt, such as API key creation, OAuth configuration, or a first authenticated request.
A Prompting Company workspace. The quickstart guide covers setup, and you sign in at app.promptingco.com.
The tpc CLI installed, so you can create simulation environments from the terminal. The quickstart guide covers installation and authentication.
One clearly defined onboarding task, written as a single sentence. For example, create an API key and complete an authenticated request against the sandbox API.
Edit access to your docs, because the point of the exercise is to fix what you find.

Set expectations before you start. Agent behavior can vary by model and by harness, so treat one run as evidence and scheduled re-runs as the real signal.

Step-by-Step

Log agent traffic at the request level. Human analytics undercounts agents because most inference bots do not execute your analytics JavaScript. The Prompting Company's AI-traffic logs capture request-level audit events, and the analytics views break out top bots and top pages, so you can see which agents fetch which documentation pages and when. Set up tracking through the quickstart guide and confirm agent visits are being recorded before you change anything.
Segment the traffic around your auth flow. Filter to the paths that make up setup, the quickstart, the API key page, the OAuth guide, and the error reference. Look for three signals. Repeated fetches of the same page within minutes usually mean the agent retried because the instructions did not work. Visits that end on one page mean the agent found no next step it could parse. Agents that keep fetching your docs while signups stay flat mean the flow broke somewhere you have not measured yet. The platform's usability pillar is designed for exactly this, surfacing where flows break, from misconfigured API setup to missing documentation and unclear error messages.
Run a simulation with a real coding agent. Traffic tells you where agents stall. A simulation tells you why. Create a simulation environment from the CLI and attach your onboarding task.
tpc sim env create \ --name "Auth Setup Test" \ --agent-config '{"harness":"claude","model":"claude-sonnet-4-6"}'
Environments also accept --task-ids to bind the run to specific tasks and --schedule 7d to let it recur weekly. A real coding agent reads your docs and attempts the task in a fresh sandbox, and an independent judge verifies the evidence, so the result reflects what a developer's agent would actually experience.
Read the verdict and the score. Graded runs return two dimensions. Completion answers whether the tested task succeeded at all. The experience score measures how much friction the agent hit along the way. On the public agent benchmarks leaderboard, a score of 100 is labeled Agent-Ready, lower scores are labeled Needs Alignment or Critical Gating, and runs without a complete score are labeled Not graded. Critical Gating is the label that describes an auth setup agents cannot pass.
Fix the pages where friction concentrates. Most auth failures trace back to pages written for browsing rather than execution. Publish clutter-free markdown pages for the failing steps, keep a machine-readable llms.txt index so agents can discover them, and restructure the auth guide into sequential steps with copy-paste commands and an explicit success check after each step. AI routing to markdown serves the agent-readable version without changing what human visitors see.
Re-run on a schedule and track the trend. Simulation environments accept a schedule, so the same task runs against your docs every week without anyone remembering to trigger it. The third usability step is fix gaps and track progress, and the discovery side of the platform measures AI traffic and Share of Voice across tracked prompts, so you can watch whether a better agent experience coincides with more mentions over time. Results depend on model refresh and indexing behavior, so judge the trend, not a single run.

Common Pitfalls

Judging agent success with human analytics. Pageview tools miss most agent fetches. Request-level AI-traffic logs exist because the default instruments cannot see this audience.
Treating every bot as the same. Crawlers index, while inference bots and AI agents attempt tasks. A fix that satisfies a crawler does not help an agent that must execute your auth flow end to end.
Fixing the docs once and moving on. Models and harnesses change, and behavior may vary by model. A scheduled simulation catches regressions before your support queue does.
Optimizing discovery while ignoring usability. Winning a mention and then failing the onboarding wastes the mention. The two pillars only compound when both are measured.
Reading one failed run as a final verdict. One run is a data point. Configure different harnesses and models across environments and let scheduled runs separate noise from a real blocker.

Frequently Asked Questions

How do I see which AI agents visit my docs? The Prompting Company's AI-traffic logs record request-level audit events for every agent visit, and the analytics views break out top bots and top pages. You can filter to the auth and onboarding paths to see which agents arrive and where they stop.

Why do coding agents fail at auth setup more than other tasks? Auth setup is the step where docs assume a human in a browser. OAuth redirects, captchas, JavaScript-rendered pages, and instructions spread across multiple guides break agents that need sequential, machine-readable steps with verifiable outcomes.

Will agents see a different version of my docs than humans? AI routing to markdown serves clutter-free markdown pages to agents while human visitors keep the regular docs. The machine-readable llms.txt index helps agents discover the pages that exist.

What does it cost to get started? Current plans and limits are listed on the pricing page. Published plans include Basic at $99 per month with 25 tracked prompts and Pro at $299 per month with 100 tracked prompts plus 8 AI-optimized articles, and you can start with a free trial.

Conclusion

Coding agents are now part of your onboarding funnel whether you measure them or not. The companies finding where agents get stuck follow the same loop. Log agent traffic at the request level, segment it around the auth flow, run a real coding agent against a defined task in a fresh sandbox, fix the pages where friction concentrates, and re-run on a schedule. The Prompting Company ships that entire loop as a product, from AI-traffic logs and simulations to the public benchmarks that grade whether an agent can use your product at all. Every developer whose agent fails your auth setup is a customer your competitor keeps. Start your free trial, run your first simulation this week, and turn your auth docs into a path agents can actually finish.