IntentLoop
AI-Native SAFe outcome steering

Turn a fuzzy goal into
a testable outcome.

Then get a recommended next move based on the evidence you have. One outcome is all you need to start.

Start with your outcome See what you’ll get
01Align
02Sense
03Respond

A WORKED EXAMPLE

See the shift before you start.

IntentLoop distinguishes delivery from impact, reads the evidence, and turns the analysis into a concrete decision.

STARTING GOAL

“Improve onboarding by delivering an AI assistant.”

Output framed as an outcome
42/100
OUTCOME LOOP PREVIEW

Delivery succeeded. The outcome didn’t—yet.

Align
Increase successful self-service onboarding from 45% to 70% this quarter.
Sense
Usage is high, but completion moved only three points and support contacts increased.
Respond
Adapt. Test whether customers misunderstand the identity-verification step before adding features.
01

What outcome are you working toward?

Start with one sentence. Imperfect is fine—we’ll help make it useful.

0 / 1,200
+ Add context and guardrails Optional

Who is affected, and what current conditions should shape the analysis?

What must remain true while the team experiments or changes direction?

Use appropriate data. Entries are sent to the configured AI service only to generate this analysis. Don’t enter confidential, personal, or regulated information unless your organization has approved it.

03

THE METHOD

A continuous learning loop,
not another status report.

Align

Give AI a clear destination.

Connect strategic intent, measurable outcomes, specifications, context, and guardrails before asking AI to act.

Sense

Let evidence challenge assumptions.

Use customer signals, metrics, and experiments to distinguish what happened from what the team merely expected.

Respond

Make the next decision smaller.

Continue, adapt, pivot, or stop based on evidence—while keeping accountable human judgment in the loop.

04

HELP & FAQ

Good questions create
better outcomes.

What makes a strong outcome?+

A strong outcome describes a meaningful change for a customer or the business, includes a way to measure that change, and usually includes a timeframe. “Launch an assistant” is an output; “increase successful self-service onboarding from 45% to 70% this quarter” is an outcome.

What counts as evidence?+

Useful evidence includes customer behavior, operational metrics, interviews, support themes, experiment results, and observed workflow changes. Delivery completion alone proves that an output shipped—not that an outcome occurred.

Do I need to complete every field?+

No. Only the desired outcome is required. Evidence makes the Sense step more useful, while context and guardrails help produce safer, more relevant recommendations.

Does IntentLoop make the decision for my team?+

No. It structures the decision and surfaces assumptions. Product, domain, technical, and governance experts remain accountable for deciding what happens next.

How is my information used?+

Your entries are sent to the configured AI service to generate the analysis. IntentLoop does not intentionally retain your submissions. Avoid entering confidential or personally identifiable information unless your organization has approved that use.