emendra

Staging environments for AI agents

Catch what your agent gets wrongbefore it ships.

Feed in your traffic. Get a real, working copy of your system, automatically.

01Feed the traces

Plug in your traces. That's the entire setup.

02Rebuild the data model

Your data model, inferred automatically. No schema to write.

03Generate the tools

Real, compiled tools. No LLM at runtime.

04Compare old vs new agent

Same eval, both agents. Differences flagged automatically.

The problem

Where does your eval actually run?

Your eval scores the reply. It still needs something real to run against, and today that's either production or a mock you hand-built and now have to maintain forever.

Production

One bad prompt swap and it approves a $1M invoice that should've been rejected.

10 of 10 tools, all live

Real customers. Real damage.

Hand-build mocks

Weeks to build, stale the moment your real system changes.

1 working, 2 half-built, 7 untouched

A month of your best engineer's time, wasted.

Emendra

The whole world your agent acted on, rebuilt from your own traffic. Fork it, run your eval again.

10 of 10 tools, fully working

The environment your eval was missing.

How it works

Real database. Real code. Synthetic world.

Table synthesis

Builds tables for effects you never logged

If your traffic implies something happened, we create a table for it, so every side effect stays trackable.

observedsynthesized

orderscustomerspaymentssubsitems
fork · 12 msfork · 12 msfork · 12 msfork · 12 ms
Single-file world

One file holds your whole environment

Copy it instantly. Every test, every agent version, runs in its own isolated copy.

Tool generation

Generated code, not recorded answers

Every tool runs real logic over your database, so new questions get computed, not just looked up.

World completion

A complete world, not just your traffic

Traffic only shows what your agent touched. We fill in the rest, so even untested scenarios have real data to work with.

What you get

Not just wrong answers. Wrong behavior.

A bad reply, a write nobody asked for, a loop that burns tokens. Every difference surfaces, ranked by how much it actually matters.

old_agentnew_agentrefund.diff

customer asks for a refund

1 issued the refund
2+cancelled their subscription

Never made it to production

  • Dropped the production database
  • Approved a $1M invoice
  • Charged the same card three times
  • Cancelled every active subscription
  • Looped for 40 minutes on one ticket
  • Refunded an order twice
  • Emailed 12,000 customers by mistake
  • Granted admin to a trial user
  • Wiped a customer's order history
  • Burned $2,400 of tokens overnight
  • Closed 400 tickets without reading them
  • Shipped to a deleted address

Your customers are not a test suite

Ship the change. Not the surprise.

Feed in your traffic and see what your new agent breaks, before it breaks it on someone real.