AI autonomy, measured in human interruptions

Your agent worked.
You still had to step in.

Latchline shows exactly where AI workflows still need people, why they need them, and what to automate next.

Reserve a $250 audit → See the product demo

For AI teams with agents in production or serious pilots. First 10 founding audits only.

The invisible problem
“We automated the workflow.”
Someone still checks it 37 times a day.
Latchline finds the hidden labor
12.6% of decisions still require a person.
12.6%
Three pains we solve

The demo looked autonomous. Production told a different story.

AI teams already know how to measure tokens, traces and latency. The human work that keeps the system alive is usually scattered across Slack, approvals, edits and memory.

01

“Why does this need me again?”

The same agent keeps escalating edge cases, but nobody has a clean count of how often or why.

Latchline creates the baseline.
02

“Where is the manual work hiding?”

Approvals, edits, takeovers and corrections happen in different tools, so “autonomy” becomes a vibe instead of a number.

Latchline turns intervention into evidence.
03

“Didn't we already fix this?”

A human solves the issue, then the correction disappears. Next week, another person rescues the same workflow.

Latchline turns repeated corrections into rules.
Early product prototype

Watch one intervention become one less interruption.

Click through the story. This is the core product loop in plain English.

Latchline / Refund Workflow
Live sample
Current run
Agent is handling the request.
AUTONOMOUS
18.4%current HIR
$4,120monthly human rescue cost
12.1%projected HIR after learned rules
Evidence receipt
10:02
Agent receives refund request$187 order • damaged item • customer requests full refund
10:02
Human edits refund decisionReason: ambiguous policy for damaged items over $150
10:03
Latchline records the correctionHuman action + reason + before/after state become reusable evidence
next run
Same pattern completes without escalationProposed rule was approved and the workflow no longer needs a rescue
Right now: We can see the agent working. We cannot yet prove why humans keep interrupting it.
Why this matters: if your team cannot measure the human rescues, it cannot know whether automation is actually improving. Latchline turns “this agent feels needy” into a baseline, a reason, a cost and a next action.
The product is built for the person who gets pinged when the “autonomous” system quietly stops being autonomous.
AUTONOMY REPORT

Where your humans are still working

A shareable executive summary: HIR, labor cost, top causes, recommended automations.
EVIDENCE RECEIPT

One intervention, preserved

Human changed: DENY → APPROVE

Reason: policy wording conflicts with damaged-item exception. Suggested rule drafted automatically.

candidate rule ready
Noah Kagan rule: sell before building the cathedral

We're preselling the result, not promising vaporware.

The first product is an Autonomy Audit. You give us one AI workflow. We show you exactly where human labor is still hiding and what to automate next.

FOUNDING OFFER • FIRST 10

Autonomy Audit

$750 total

$250 today to reserve. Remaining $500 when your audit begins.

  • One production or serious-pilot AI workflow
  • Human Intervention Rate baseline
  • Top intervention causes and repeated failure patterns
  • Estimated human labor cost + ROI model
  • Three highest-value automation opportunities
  • 30-day measurement plan + executive readout
Reserve one of 10 spots →

If the workflow is not analyzable after intake, the reservation is refunded.

AFTER THE AUDIT

30-Day Design Partner Pilot

$3,500 starting

For teams that want Latchline instrumented against a live workflow while the product is still being shaped.

  • Event ingestion / lightweight connector
  • Live HIR dashboard
  • Evidence receipts
  • Weekly intervention-pattern review
  • Learned-rule proposals
  • Before/after HIR and ROI report
Apply as a design partner

Pilot customers influence the product. The goal is to convert successful pilots into recurring software.

Research / category thesis

The Human Intervention Gap

A practical whitepaper on measuring the human rescues hidden inside AI-agent workflows, defining Human Intervention Rate, and turning corrections into safer autonomy.

Read the whitepaper PDF →Read online
LATCHLINE / 2026
The Human Intervention Gap
Why AI autonomy needs a measure of human rescue.
FAQ

Questions a real buyer will ask.

What exactly counts as a human intervention?

Any eligible agent decision or workflow step where a person has to approve, edit, redirect, deny, supply missing context, or take over. The audit defines the denominator carefully so high-risk controls are not mislabeled as “bad automation.”

Is this another observability tool?

No. Traditional observability explains what the system did. Latchline centers the point where human judgment entered the system, why it entered, what changed, what that cost, and whether the correction can reduce future intervention.

What do I need for an Autonomy Audit?

One AI workflow with enough evidence to reconstruct runs: logs, traces, tickets, approval history, human edits, or a representative export. We can start with CSV/JSON and do not require a deep production integration for the founding audit.

Will you try to eliminate all human oversight?

No. The useful target is less unnecessary intervention while preserving success and appropriate controls. High-risk decisions may intentionally require people. Latchline makes that boundary explicit instead of hiding it.

How quickly do we get something useful?

The founding audit is designed to produce a baseline and recommendations within roughly five business days after usable workflow evidence is received. A live design-partner pilot follows only if the audit exposes a worthwhile recurring problem.

Why should I join this early?

You get a concrete operational audit now, influence the metric and product while the category is forming, and receive preferred founding-customer terms if the software becomes useful enough to keep.