We envision a future where humans and AI work together in harmony. As AI capabilities improve, the total amount of work humanity can produce will rise dramatically. This is not just about speed. It is about increasing the surface area of what is possible, and making more ambitious outcomes achievable with the same human time.

In that future, AI will not simply assist with tasks. It will participate in the day-to-day fabric of execution. It will draft, research, monitor, summarize, propose actions, and run workflows. Humans will remain the source of intent, judgment, and accountability, but the operating rhythm of work will change.

Communication Becomes the Default

On a daily basis, humans will interact with other humans and with AI agents. Coordination will not be a side activity. It will become the primary activity, because the number of parallel efforts and open loops will increase.

Crucially, responsibility cannot be fully assigned to an AI in the way it can be assigned to a person. Even if an agent performs the work, humans will still need to confirm, clarify, and approve. This creates a higher baseline of communication, and it makes follow-ups, handoffs, and alignment a constant.

As deep work becomes relatively rarer, “talking to agents” becomes a form of managerial work. People will spend more time specifying intent, correcting direction, validating output, and ensuring that the right things are happening for the right reasons.

The Need: AI Assistants for HITL and Coordination

This shift creates demand for a new category of AI assistants. Not assistants that only generate content, but assistants that coordinate work across humans and agents while keeping humans in the loop where it matters.

These assistants must support HITL workflows end-to-end. They should manage communication across channels, preserve context, and maintain continuity when work becomes fragmented across people, tools, and agent threads.

They should be able to track hundreds or thousands of open items and conversations without losing the thread. They should also be able to close items on your behalf where appropriate, but only when the intent is clear, the risk is low, and the outcome is verifiable.

Harmony as an Interface to the World

Agents like Harmony act as an interface between the user and the external world, including other agents. Harmony is not only a productivity layer. It is an interaction layer that helps the user stay oriented, in control, and effective.

Harmony should speed up HITL flows, but it should also observe what is happening as work unfolds. It should learn the patterns of how decisions get made, how follow-ups get resolved, and what “done” actually means for a specific person and a specific team.

Over time, this enables progressive automation. The system begins with simple, low-risk actions. As confidence grows through repeated exposure to real workflows and feedback, the system can take on more complex coordination and execution.

Work Shifts Up the Stack

A major portion of human work will shift toward decision-making, delegation, and reviewing outputs. People will spend less time producing first drafts and more time shaping direction, setting priorities, and judging quality.

That means the assistant must be strong at supporting high-leverage moments. It should help users make decisions with context, delegate with clarity, and review efficiently with the right framing, references, and comparisons.

The goal is not to remove human judgment. The goal is to make judgment easier to apply at scale, even when the number of moving parts grows far beyond what a human can track unaided.

From Inbox to Visibility Layers

For now, we are starting with reports and the work-items graph. These are practical entry points because they create immediate visibility. They reduce the cost of staying up to date, and they help prevent important work from silently slipping through cracks.

But the future requires more than an inbox. The “AI inbox” can act as a HITL maintainer, continuously catching and organizing open loops. Over time, other interfaces will emerge that operate at higher levels of abstraction.

Users will need ways to explore what is happening across projects, commitments, people, and timelines. They will want to see pictures of work. Not just a list of messages, but a coherent model of reality that can be navigated, zoomed in, and understood.

Learning From the User Becomes the Asset

A major task of systems like Harmony is to learn and mimic user behavior. This is not about personalization in a superficial sense. It is about learning how a user thinks, how they decide, what they consider important, and how they want outcomes to be achieved.

To learn well, the tool must make it easy for users to spill their minds. It should capture information naturally, collect context continuously, and learn from it over time. The best systems will feel like they are always present, always aware, and always helpful, without being intrusive.

Collecting and learning from this stream of context becomes a core part of truly intelligent systems like Harmony. Over time, this super-personal data may become one of the most precious assets of the future, because it encodes not just information, but intent, preference, and judgment.