A truly proactive system doesn't wait for a prompt; it notices, infers, and acts while the user stays in flow. Pull that off, and the computer feels less like a tool you operate and more like an intelligent partner.

I've been working on systems that build towards this idea. This is some of the thinking behind that work. The core challenge isn't getting a model to be smart; it's architecting the system so intelligence can surface at the right moment, with the right scope, in a way people actually trust.

Passive awareness

Most AI tools are reactive– you ask, they answer. But real work doesn't happen in isolated question-answer pairs. You're switching contexts, building momentum over hours, racing toward a deadline. For an agent to actually help, it needs passive awareness: the ability to watch what you're doing across different time horizons and offer the right intervention at the right moment.

The thought is that different patterns emerge at different scales. A keystroke means nothing on its own, but a cluster of edits in the same file over twenty minutes signals focus. A month of similar weekends suggest a ritual of weekly blogging. If the system only listens at one tempo, it either drowns in noise or misses the pattern entirely.

Micro-signals

Keystrokes, cursor hovers, the file you just saved or a link you copied. A lightweight on-device watcher notices patterns without pausing for permission.

Session context

The document you've edited for twenty minutes, the Slack thread you keep revisiting. At this scale the agent can surface light suggestions–"clip these TODOs into your task list?"–because it sees a coherent chunk of work.

Narrative arc

Multi-day patterns like a weekly metrics brief or the branch you always merge on Fridays. A higher-level model can spot looming risks or tee up longer-horizon reminders.

Add modality (text, calendar, file diffs, ambient sound) as a second axis and you get a grid of signals the system can subscribe to or ignore on demand. Each layer publishes tidy, typed events upstream (timestamp, confidence, proposed action) so the next layer can reason without drowning in raw data.

The coordination problem

Awareness alone isn’t enough; the system has to decide what to do with what it knows. That’s where the architecture starts to matter. You need a division of labor between fast, local instincts and slower, deliberative judgment. Think of this as the difference between reflex and reasoning.

In human terms, skills are the sensory neurons in your fingertips–fast, local, and precise–capable of reflexive capture or action without waiting for the brain’s deliberation. The orchestrator is the nervous system deciding which signals to amplify, which muscles to move, and how much force to apply as a whole. Get that balance right, and the system feels coordinated rather than chaotic.

Local micro-skills

Tiny, opinionated models that watch a narrow band of signals, propose one specific action, and emit a confidence score. They live on the same device as the user, so they're fast, private, and cheap to run.

Cloud (or larger) orchestrator

Holds wider context–ongoing projects, personal norms, policy rules–and decides which skill may act, with what parameters, and whether the user must be asked first. Think of it as a probabilistic planner plus policy engine.

Why the bigger brain sits on top:

  • Context radius – it can weigh cross-domain hints that don't fit in a quantized sidekick
  • Conflict resolution – when two skills disagree, the orchestrator arbitrates with a memory longer than 20 tokens
  • Policy updates – a single place to inject new rules ("never auto-send legal docs") is easier to audit and patch

Designing a loop

Control loops don’t spring into existence fully formed; they start with tight, local feedback and expand outward as confidence and predictability grow. A thermostat learns the response curve of a room before it coordinates with HVAC zones. PID controllers tune gain and damping before linking into cascaded systems. The same principle applies here: start with bounded feedback, measure error, correct drift, then widen the control horizon.

We’re now trying to orchestrate the same kind of feedback ecology, but with black boxes of varying intelligence instead of resistors and transducers. Each micro-skill is a local loop, fast and measurable; the orchestrator acts as a meta-controller, fusing their signals, enforcing constraints, and keeping the overall system stable.

What we’re engineering isn’t a single brain but a dynamic ensemble of partial intelligences, stitched together by feedback and trust.

  • Starting small and local – wire up one micro-signal (e.g., opens a new link) and one safe action (bookmark link later, create a summary)
  • Log accept/reject feedback – the fastest way to tune confidence thresholds
  • Layer in session context – once the micro-reflex is solid, let the orchestrator condition actions on the active project or window
  • Expand the narrative arc – only after the shorter loops hit >50% acceptance should you promote multi-day interventions
  • Expose an autonomy slider – even Tesla lets you dial Autosteer; proactive software should too

Trust, reversibility, and continuous eval

Proactivity only works if it earns the right to act. Trust isn’t a switch; it’s a feedback loop that has to be observable, reversible, and self-correcting. Every proactive system lives inside that loop.

Scope of practice
Each skill should declare what it touches and what it won’t. Boundaries aren’t bureaucracy– they’re how the system communicates its shape. A skill that knows its limits is one you can let run without constant supervision.

Reversibility as first principle
Every action should be a reversible diff, not a commitment. When users can roll back any change with a single click, autonomy stops feeling like a gamble.

Continuous evaluation
Each skill is tested against past event streams before and after changes. Human feedback sets acceptance thresholds; failed actions can be replayed locally until they pass. Only then does the skill return to active rotation. Evaluation isn’t abstract– it’s part of the same feedback loop that tunes confidence and keeps errors from amplifying upstream.

Trust, in the end, isn’t granted to the system as a whole. It accrues, loop by loop, in the way each part behaves under uncertainty.

Future

The hard part is that all of this wiring–the awareness loops, the arbitration layers, the feedback channels– don't scale neatly. Every new mode of awareness adds another circuit to stabilize. Each skill needs calibration against the others, and small timing errors compound into noise. It feels less like software engineering and more like debugging a living nervous system with parts that hear at different speeds.

Building proactive AI still means composing brittle parts into something that behaves as if it were whole. For now, that coherence comes from orchestration and policy. Over time, it might come from systems that learn their own balance–models that tune reflexes and heuristics through experience rather than explicit wiring. But even that kind of learning depends on structure: streams of events, bounded actions, and feedback loops that make consequence legible.

Until then, the work is equal parts engineering and patience: wiring, tuning, and re-tuning the loops that let intelligence emerge.