Why current AI agents are slow, brittle, and hard to trust
The current generation of AI-driven automation — Claude Cowork, browser-using agents, similar tools — uses a scan-decide-execute pattern. The AI watches the screen at every step and chooses the next action. This means every step is a non-deterministic AI judgement. Slow because each step is an inference call. Brittle because two visually similar states can get different decisions. Hard to audit because the decisions happen in the model's weights at runtime.
For most consumer use this is fine. For business use — where actions need to be fast, repeatable, and traceable — it's a problem.
What Proxy does
Proxy uses scan-code-execute instead. Each action is a deterministic routine that handles a specific step without AI involvement at runtime. The AI is involved once — at the point of writing the routine — and after that, the routine runs at code speed, identically every time, with a complete audit trail of what it did.
Anything with a digital interface
Web applications. Native applications running on the user's machine. Cloud platforms. Internal tools. Industrial control panels. SaaS dashboards. Anywhere there's a digital interface a person could operate, Proxy can scan, generate code against, and execute. Not just web automation — automation across whatever the user's actual environment requires.
Once, or every day, or every five minutes
Run a Proxy automation when you want it. Or schedule it. Or trigger it on events. Pull the daily sales report from the CRM and email it to the team every morning at 8. When a new high-priority ticket lands in the queue, kick off the response workflow. Reconcile the spreadsheet against the source system every Friday afternoon. The same architecture handles one-shot tasks and ongoing automation; the difference is just when execution gets triggered.
Self-healing when things change
Systems change. Pages get redesigned. APIs get new fields. UIs get rebuilt. So every Proxy module has built-in failure detection and the ability to call for help. When a module hits something it doesn't recognise, Proxy calls Forge — which generates an updated module that handles the new state. Deterministic execution resumes. The system has just learned how to handle one more environmental variation.
The longer Proxy runs against a target, the more it has handled. The rarer call-for-help events become.
The result
Code-speed execution. Code-auditability. Code-reproducibility. With AI as the repair mechanic when the environment shifts. Reliability and speed from code. Flexibility and depth from AI. Together: the perfect hybrid. On-demand automation when you need it; scheduled automation when you need it forever.