Practice Makes Perfect

The future of work isn't blocked by AI capabilities.

It's blocked by missing infrastructure.

Here's the problem:

→ Agents are trained on Wikipedia, not Workday

→ They pass coding interviews but can't navigate Slack

→ They write perfect copy, then crash the CMS

→ They generate plans but get lost in workflows

The primary reason the AI agents we have today fail is due to a lack of repetitions, not intelligence.

We have solved this problem:

→ Pilots train in flight simulators before real planes

→ Surgeons practice in simulations before real patients

→ Self-driving cars log millions of miles before real roads

Yet we are trying to deploy AI agents directly to production with no equivalent practice environment.

That's the gap Mechanize is closing:

→ High‑fidelity sims of Gmail, Slack & more

→ Metrics for speed, accuracy & recovery

→ Failures auto‑generate fresh training data

→ Standardizing common workflows

Think of them as providing a set of benchmarks that show which agents can actually do the job.

Tamay Besiroglu & Ege Erdil have the background:

→ Co‑founded Epoch AI, creator of industry benchmarks

→ Authored work on compute scaling & AI‑driven growth

→ Forecasted cross‑domain capability jumps years ahead

→ Proved breakthroughs follow realistic training grounds

Their key insight: agents stumble not from lack of parameters but from never experiencing real work.

If they succeed: Picture a customer‑service agent that's rehearsed thousands of chats. Now picture accounting agents that have seen every invoice edge‑case.

The potential to eliminate friction from everyday workflows is massive.

For centuries, how work happens has been locked inside:

→ Employee brains

→ Unwritten rules

→ Institutional memory

Their simulations make the invisible visible, so we can refine the processes that run every organization.

Flight sims didn't just train pilots; they rewrote aviation.

Mechanize wants to do the same for every support ticket, email, and spreadsheet.