Page 476 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

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Item Details
Internal MVP in testing at with knowledge professionals across product,
Testbeds or Pilot operations, development, design, and other cognitively demanding
Deployments roles—supporting clarity, resilience, and access to mindful productivity
in modern work. Available at [8].
Code repositories Private (in development.)

2 Use Case Description

2�1 Description

This use case introduces Attached Intelligence – a new paradigm for AI that works with the user,
not for or against them. Unlike traditional automation or command-based agents, Attached
Intelligence is collaborative: it supports users through structured thinking, intentional action,
and guided reflection [2], while preserving human agency and adaptability.

Modern professionals and knowledge workers are inundated with decisions every day—
ranging from routine to highly strategic. While some are simple, they still require attention
and mental energy [1]. Others demand deep context, domain knowledge, and the ability to
weigh competing priorities. These decisions are rarely made in isolation; they are often layered
with cross-functional considerations, time pressure, and shifting goals.

Even routine decisions—like prioritizing tasks, responding to messages, or switching between
tools—consume the limited cognitive bandwidth available. This reduces the attention and focus
professionals can dedicate to deeper, high-impact work and strategic decision-making [1].
The result is a fragmented workday, rising decision fatigue, and a sense of being busy without
meaningful progress.
Our aim is to support workers in knowledge labor sectors who are vulnerable to burnout
and disruption in the evolving AI-enhanced workplace. By enhancing cognitive clarity and
reflective capacity, we help them make more intentional, high-quality decisions. Rather than
just managing tasks, we support the entire cognitive loop of thinking, doing, and reflecting [1]
[2] so that decisions become more aligned, agile, and effective over time.
We are developing a productivity framework embedded in a Think Do Reflect workflow. It is
powered by the Attached Intelligence Multi-agent System (AIM-1) — a modular, LLM-powered
system designed to operate as a collaborative thinking partner. AIM-1 enables context-aware,
reflective decision support by orchestrating specialized agents that work together to guide
users through structured thinking, actionable
planning, and ongoing reflection—supporting user agency at each step without taking over
the decision.

This attached system integrates into the user‐s flow without taking over or overwhelming them.
The assistant, as part of AIM-1, helps users structure their thinking, clarify actions, and reflect
on outcomes. Conversations and actions are continuously broken down into evolving topics
and stored in a personalized, dynamic knowledge graph. This allows users to zoom in or out
of their context—whether they need a high-level overview or a deep dive into a specific topic or

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