SMART RPA · BROWSER AGENT
A self-evolving AI worker in the browser
Teach it once, and it gets faster with use — self-healing selectors absorb UI changes and workflow caching reuses paths it has already walked, so repeat work no longer needs babysitting.
This is one example agent we built.View the other showcase →
Scripts run — but not for long
Brittle scripts break
Traditional RPA binds to fixed selectors; one front-end change and the script is dead the next day.
High maintenance cost
Every UI or process change means rewriting scripts by hand — long-term upkeep eats real headcount.
Follows steps, can't adapt
Scripts only run a fixed flow; faced with exceptions or new situations they can't reason or fix themselves.
Teach it once, faster with every use
The first run reasons through the task with an LLM; after that it reuses the path it walked — repeat work at near-script speed, with UI changes self-healing.
- 1
The first run reasons through the task with an LLM, understanding UI intent instead of memorizing coordinates.
- 2
Paths and decisions it walked are recorded as a workflow cache, reused directly on repeat tasks.
- 3
From the second run, repeat tasks reuse the cache at near-script speed — 10–100× faster, with model cost dropping.
- 4
When the UI changes, self-healing selectors find the semantically equivalent element across the a11y tree and the visuals, and finish the job.
LLM reasons through the task — slower
Cache hit, path reused
Near script speed, 10–100× faster
Illustrative · depends on task complexity
Why it doesn't break on every redesign
Self-healing selectors
Self-Healing Selectors
After a redesign it finds the semantically equivalent element across the a11y tree and visuals — renamed buttons, moved nodes — and keeps running, no rewrite needed.
Workflow caching · faster with use
Workflow Caching
The first run reasons via LLM; later runs reuse the cached path. Repeat tasks run 10–100× faster, with model cost falling in step.
Dual-channel page understanding
Dual-Channel Understanding
It reads the a11y tree and a visual screenshot at once, cross-checking both channels so complex front-end frameworks can't fool it.
Human approval gates
Human Approval Gates
High-risk actions (submit, pay, delete) pause first, resuming only after a human approves via IM push — and archiving on timeout.
Go live → learn → speed up
Go live
Describe the task in plain language and the agent runs it in the browser the same day.
Learn
Paths and decisions it walked settle into a workflow cache — more fluent every run.
Speed up
Repeat tasks reuse the cache at near-script speed; UI changes self-heal, with no maintenance.
Want a browser agent you never maintain?
Tell us your process and we'll build, deploy and keep evolving it for you.