Resources

AY Automate / Operating Framework

We don't ship workflows. We ship systems that learn.

A one-off automation is a snapshot of your best day. It goes stale the moment the work changes. FIRE is the loop we run instead: find the real work, instrument it with evidence, roll it out for real, and evolve it from what the field pushes back. Then it runs again.

FindInstrumentRolloutEvolve↻ repeat
01

The loop, phase by phase

F

Find

the real work

Map how the work actually happens, not how the doc says it does.

  • Sit with the people doing the work
  • Watch a top performer make the call
  • Map every step, exception, and handoff
  • Encode their judgment, not a generic prompt
CheckpointAn operating map: the workflow today vs. with AI built in.
I

Instrument

evidence, not vibes

Build the agent and turn non-determinism into numbers you can trust.

  • Agent with tools, guardrails, memory
  • Golden dataset of 20+ real cases
  • Evals for pass rate, failure modes, escalation
  • Full audit trail of every step
CheckpointA measured agent with known failure modes and a full trail.
R

Rollout

inside the business

Make it work in the real stack, never force a migration.

  • Build on top of existing systems
  • Start in a sandbox, earn trust
  • Increase autonomy gradually
  • Monitor everything in production
CheckpointLive in production, recoverable, watched, owned.
E

Evolve

close the loop

Learn from the gap between what we predicted and what the field hit.

  • Pull what people overrode or rejected
  • Collect the reasons, in their words
  • An agent finds the pattern the logic missed
  • Feed it into the next run, repeat
CheckpointA system that is sharper this cycle than last.
↻Evolve feeds Find. Every improvement makes the next bottleneck obvious, so the loop starts again, one level up.
02

The first turn, in 30 days

DAYS 1 TO 4

Find

Audit the workflow with the people who own it.

DAYS 5 TO 14

Instrument

Build the agent, the golden set, the evals, the audit trail.

DAYS 15 TO 24

Rollout

Sandbox to production, autonomy dialed up, monitored.

DAYS 25 TO 30 AND BEYOND

Evolve

First feedback pulled, pattern found, next cycle armed.

03

Why the loop wins

one-off

A workflow

Your best rep, on one good day, frozen in a prompt.

  • -Built once on what we knew then
  • -Goes stale as the work shifts
  • -No memory of where it was wrong
  • -Improves only when someone rebuilds it
FIRE

A learning system

Every rep, sharper every cycle, on its own.

  • →Instrumented so it knows its own failure modes
  • →Learns from what the field pushes back
  • →Closes the gap between predicted and real
  • →Compounds: better this month than last

A workflow is a snapshot. A system makes every rep better, every day.

04

Frequently asked questions

What is the FIRE framework?

FIRE is AY Automate's operating loop for shipping AI into a real business: Find the real work, Instrument it with evidence, Rollout inside the business, and Evolve from what the field pushes back. Then the loop runs again, one level up.

How is FIRE different from a normal automation project?

A normal automation project ships a workflow once and stops. FIRE treats that as turn one of a loop. Evolve feeds back into Find, so the system keeps getting sharper instead of going stale the moment the underlying work changes.

What happens after the first 30 days?

The first 30 days get you through one full turn of the loop: Find, Instrument, Rollout, Evolve. After that, Evolve keeps pulling overrides and rejections from the field, and the next cycle starts, correcting what the first turn got wrong.

Do I need a forward deployed engineer to run FIRE?

FIRE is the method our forward deployed engineers run when they embed inside a team. You do not need to hire an FDE to understand the loop, but running it well inside a real business, with real access and real stakes, is what an embedded engineer is for.

Run FIRE in your business

A forward deployed engineer runs this loop inside your team.

This is the operating method our forward deployed engineers run when they embed with a client: find the work, instrument the agent, ship it into production, and evolve it from what actually happens.

Book a callSee forward deployed engineeringThe FDE in 30 days roadmapWhat a forward deployed engineer does
FIRE is AY Automate's operating loop for shipping AI into a real business: Find, Instrument, Rollout, Evolve, then repeat. The 30-day view above is the first turn of the loop, not the whole story. The value is in cycle two and beyond, when the system starts correcting itself. Synthesized from our forward deployed engineering practice, the audit, evals, and deployment method, and the self-learning systems idea articulated by Taimoor Tariq at GTMBase. Credit is real, not a partnership claim.