Agent Infrastructure
OmniPilot
An agent that operates real interfaces — seeing, planning, acting, and verifying across desktop and mobile environments.
The workflow
See the system
in context.
Explore an illustrative interface, then go deeper into the architecture and decisions behind the work.
- See
- Understand
- Plan
- Act
- Verify
- Recover
“Organize the project files and prepare a client update.”
Client update
The work, organized into a useful handoff.
01 / Inside the system
The problem to solve
Most automation stops where the API ends. Real business work happens in interfaces — desktop applications, browsers, terminals, file systems, mobile devices — where no clean programmatic boundary exists.
OmniPilot is engineering evidence that this boundary is solvable: an execution system that perceives interfaces, plans multi-step work, acts on real environments, and — critically — verifies that each action actually happened before continuing.
02 / Inside the system
How it fits together
- 01
Perception
Screen capture, UI-tree parsing, and OCR combine into a grounded representation of what is actually on screen.
- 02
Understanding
Elements are identified and related — buttons, fields, tables, state — forming a model of the current interface.
- 03
Planning
Tasks are decomposed into ordered steps with tool selection, preconditions, and expected post-states.
- 04
Execution
Input control across mouse, keyboard, shell, and mobile debugging bridges — scoped to the permissions of the task.
- 05
Verification
After every action, the environment is re-perceived and compared against the expected post-state.
- 06
Recovery
Mismatch triggers re-perception, alternate paths, or escalation — never silent continuation on a broken assumption.
03 / Inside the system
From input to outcome
See
Capture the environment — pixels, accessibility trees, and text — at the fidelity the task requires.
Understand
Ground raw perception into actionable elements and a coherent state model.
Plan
Decompose the objective into steps, each with an expected outcome that can be checked.
Act
Execute one step in the real environment — click, type, run, move — within scoped permissions.
Verify
Re-perceive and confirm the expected post-state. The loop does not advance on assumption.
Recover
On mismatch: retry, take an alternate path, roll back, or escalate to a human with full context.
04 / Inside the system
Decisions that matter
Verify after every action
Not at the end of a task — after each step. Compounding unverified actions is how agents fail silently.
Permission scopes per task
The agent receives only the access the current objective requires — nothing ambient, nothing standing.
Deterministic fallbacks
Where a reliable scripted path exists, it is preferred over model-driven interaction. Models handle ambiguity, not everything.
Replayable action logs
Every perception, decision, and action is recorded so runs can be inspected, debugged, and evaluated.
05 / Inside the system
When things go off-script
- element not found
- Re-perceive the environment and re-ground before retrying.
- ambiguous state
- Stop and ask — ambiguity is escalated, never guessed through.
- repeated step failure
- Escalate with the full action log and current state attached.
- long task interruption
- Resume from checkpoints rather than restarting the entire objective.
06 / Inside the system
What this demonstrates
Make the next move
What could this unlock for you?
A workflow in your business may look like this. Let’s explore what a useful system would involve.