Monitoring agent-readiness · live

AI agents onboard your customers. We keep your product working when they do.

Every release you ship — and every model update — can silently break the workflows agents rely on to adopt your product. Docka continuously tests whether real agents can still get the job done, and hands your team the fix — so you don't lose the customers who arrive through an agent.

Live with Manticore Search — agents get the runbook, humans get the docs.
readinesslive
L3 · 84%
fully-functional
readiness matrix · job × model5 platforms
OpusGPTGemKimi create_task assign_owner set_dependency export_report create_webhook
passpartialfail
readiness over releasesdrift
every v8.4 release reopens a corner agents fail in — caught on the next re-run.
Agent-readiness monitoring for any product customers now adopt through an agent.We watch every surface an agent touches — docs · API · UI
01 — The problem

The customer you lose to an agent, you never see.

More of your signups now arrive through an AI agent — a developer's coding assistant, a buyer's research agent, an operator wiring you in. The agent reads your docs, tries your API, clicks through your UI, and either gets the job done or gives up.

When it gives up, nothing shows up in your funnel. No error, no ticket, no churn event — just a customer who quietly went to whatever competitor's agent could finish the job.

And it's not an audit you pass once. Readiness decays — every release you ship and every model that ships moves the failure to a corner you'd never check.

AI agent attempts a job  on your product
your product  docs · API · UI
✓ completes
new customertracked · in your funnel
✗ gives up
lost — silentlyno error · no ticket · no trace
↑ the right-hand path is invisible to you today
02 — What we do

We measure your product the way an agent experiences it — then keep measuring.

01 · Probe

Run your real jobs through real agents

Your critical jobs-to-be-done, run through frontier and open models in a live sandbox. Graded by execution — pass or fail, in a real container. No model opinion, no partial credit.

02 · Fix

Close the gaps agents fall into

Where agents break, we hand your team the fix: a machine-readable runbook or typed interface that makes the job reliable — zero-install, so agents pick it up automatically.

03 · Monitor

Re-check on every release and model

Then we watch. Each product release and model update, we re-run the matrix and flag the corner that just started failing — before your customers find it. This is the part that recurs.

Docka is an agent test-harness run by engineers who've operated infrastructure at scale. The agents do the measuring; our people read the failures and design the fix. We're QA for the AI agents now using your product on your customers' behalf — not for your human users; your team owns that.

03 — Proof

Measured, not asserted.

Every number comes from real agents executing real jobs in a sandbox — replay-verified, not a model's self-report.

Readiness — one product, across interfacesopus-4.8 · 2026-07-08

docs+runbook+typedΔ cost create_task2% assign_notify2% set_dependency2%
7 → 0
failures gone after a runbook
~2%
of the token cost via typed interface

Live re-runsurface: UI

create_taskFAIL
assign_and_notifyFAIL
set_dependencyFAIL
↳ + Docka runbook0/15 PASS
↳ + typed interfacePASS · ~2%

Live in production: Manticore Search serves Docka runbooks at its docs today — agents get the lean version, humans get the full page.

04 — What you get

Start with a look. Stay for the monitoring.

Assess

Where agents break

Free · scoped · no install

We run a few of your critical workflows through a frontier agent and show you exactly where they fail. If they pass — you're fine today, and we tell you so.

Fix

Runbook & interface

Per workflow · one-time

We build the machine-readable runbook or typed interface that makes each broken job reliable for agents. Yours to publish.

The product
Monitor

Continuous agent-readiness

The smoke detector. We re-measure every release and model update, and alert you the moment a workflow starts failing — priced to what a broken agent workflow costs you.

05 — Fair questions

The ones you're already asking.

Isn't this our release team's job?

By construction, no one owns it. Docs own prose, engineering owns the product, QA owns human flows, DevRel owns evangelism — the agent's experience across every version × model is unowned. You don't run your own uptime monitor or security scanner either; you buy the horizontal tool.

Won't smarter models just fix this themselves?

No — the failures live in stale or absent knowledge, which capability doesn't touch. A smarter model still can't know the version you shipped last week or your proprietary feature. Model progress doesn't close the gap; it moves it.

Is this just a wrapper around an LLM?

It's a harness that runs your real jobs in real containers and grades them by execution — plus the people who read the failures and design the fix. The measurement across the version × model matrix is the durable layer.

Why now

Agent-readiness is becoming a category. We'd rather you own yours early.

The share of your customers who meet your product through an agent only goes up from here. The first products to stay reliable for them keep the customers the rest quietly lose.