AI-NATIVE. HUMAN-DECIDED.

Turn a fuzzy goal into
a testable outcome.

Bring the goal and whatever evidence you have. IntentLoop finds the uncertainty that matters, suggests the smallest useful experiment, and leaves the consequential calls to humans.

01Align
02Sense
03Respond

A WORKED EXAMPLE

Pick your pain. See the loop.

Choose the problem that feels familiar and see how IntentLoop turns it into a decision.

USE INTENTLOOP HEREInspect & AdaptDecide whether the evidence supports adapting the next experiment.
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.

WHERE IT FITS

Bring a better decision into the room.

Before PI Planning

Turn a proposed initiative into a measurable outcome, expose missing evidence, and arrive with an experiment the ART can discuss.

During a Portfolio Sync

Interpret mixed performance signals, identify the uncertainty blocking a decision, and determine whether to adapt, continue, or gather evidence.

At Inspect & Adapt

Bring the latest results back to the loop, compare them with the expected signal, and revise the recommended next move.

Across AI-Native SAFe

Shape PI Outcomes, run Sense and Respond cycles, govern AI experiments, and direct investment toward measured customer and business value.

Use IntentLoop before a meeting to prepare a decision, during the conversation to challenge assumptions, or afterward to continue the evidence loop.

THE METHOD

How IntentLoop works

IntentLoop is a human-governed outcome agent. It prioritizes uncertainty and revises its guidance as evidence changes; accountable people retain every consequential decision.

Align

Clarify the measurable change.

Turn strategic intent into a testable outcome, surface missing information, and identify what would strengthen the decision.

Sense

Interpret—not just repeat—the evidence.

Examine the leading and competing explanations, then identify the signal that would distinguish between them.

Respond

Make the next decision smaller.

Turn that signal into the smallest useful experiment and surface the tradeoff only accountable people can decide.

HELP & FAQ

Good questions create better outcomes.

What makes IntentLoop an outcome agent?+

IntentLoop works toward a bounded objective: make the next outcome decision more defensible. It maintains an outcome state, selects the highest-value uncertainty, proposes a small experiment, and revises its status and recommendation when new evidence is added. It does not execute consequential actions or replace accountable people; human governance is part of its design.

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.

What happens when I add details or new evidence?+

Add either kind of update under Align. IntentLoop runs the whole loop again, shows what changed, and may suggest a different next move. Your update also appears in the inputs list and copied decision brief.

What happens when I record a human decision?+

Recording a decision is optional. You can accept either suggested path or choose another path and describe its bounded experiment. “Help me prepare this decision” organizes the selected path, accountable role, evidence, tradeoff, meeting question, and guardrails without making the decision for you. IntentLoop then turns a recorded choice into an active experiment, identifies the evidence to bring back, and evaluates later evidence against that commitment. Use an accountable role rather than a person’s name, and avoid confidential or regulated details. When you continue the loop, the selected path and experiment—not the accountable role or rationale—are sent to the configured AI service. The full record is preserved only if you explicitly save a return link; saved decisions receive the same encryption and 30-day expiration as the rest of the loop. Decision content is never included in anonymous analytics.

Will I always get exactly the same result when I rerun a loop?+

No. Generative AI can vary its wording, interpretation, and readiness score slightly even when the inputs are unchanged. Material changes should usually come from added details or evidence. For consequential decisions, treat the analysis as coaching input, validate its claims, and compare representative examples before changing the configured model.

What is the IntentLoop Outcome Readiness Rubric?+

The rubric estimates how ready the supplied information is to support an evidence-based decision. It evaluates outcome clarity (30 points), measurability (20), evidence strength (30), and decision context (20). Expand “Why this score?” to see what earned points and the most important reason points were withheld in each category. The score does not predict whether an initiative will succeed or measure progress toward its target. Relevant information may improve readiness; additional text alone will not. The rubric is an independent IntentLoop model informed by AI-Native SAFe concepts—not an official SAFe assessment or endorsed by Scaled Agile, Inc.

What do the coaching styles change?+

Supportive acknowledges useful foundations and frames gaps constructively. Direct is concise, neutral, and action-oriented. Unfiltered leads with the hardest truth and challenges weak claims plainly. Style changes the delivery—not the readiness score, rubric, evidence standards, conclusions, or recommendation.

How are Sense and Respond different?+

Sense interprets the evidence, develops a competing explanation, and identifies the signal that would distinguish between possibilities. Respond converts that insight into a bounded experiment and identifies the human choice or tradeoff that remains.

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—and can I save my loop?+

Your entries are sent to the configured AI service to generate the analysis. IntentLoop retains anonymous daily counts such as completed analyses, optional fields used, coaching style, score range, and saved-link activity; it does not store your submitted wording in analytics or create visitor profiles. IntentLoop does not retain a loop unless you explicitly choose Save return link. Saved loops preserve the inputs, analysis, and selected coaching style; they are encrypted, automatically deleted after 30 days, and accessible to anyone who has the unlisted link. The browser that created a link can extend it by 30 days at any time or delete it; simply visiting never extends expiration. Avoid entering confidential, personal, or regulated information unless your organization has approved that use.

PREFERENCES

Choose your experience.

Focused keeps the core outcome loop simple. Full adds coaching styles and, when enabled by the site administrator, browser-only supporting data.

Focused experience is active.