For teams deploying AI agents

Deploy AI agents intoproduction, in weeks.

Lab0 is an AI forward-deployed engineer. It deploys software into the enterprise, your legacy SaaS and your AI agents, through the same discovery, integration, testing, and go-live. Buy an agent or have us build one; either way it lands in production with the integration, guardrails, and approvals it needs. Demos are easy. Production is the part we do.

Bought & custom agents·Eval gates · dry-run · rollback·Backed by Y Combinator

Everyone can build an agent demo. Almost no one can get it into production.

The model isn't the hard part anymore. The hard part is the same one enterprise software always had: integration, permissions, testing, and the org trusting it enough to turn it on. That's a deployment problem, not a model problem, and it's where agents stall.

POC
Stuck in the pilot
Most enterprise agents never leave the demo. A model that works in a sandbox isn't a system the business can run, the gap between the two is the entire job.
Access
No path to the systems of record
Agents are only useful when they can read and act on real data, the same ServiceNow, SAP, Workday, and custom-API surfaces Lab0 already integrates against.
Blast radius
Permissions nobody scoped
What can the agent touch, write, or trigger? Without scoped permissions and a kill-switch, security won't sign off, and they're right not to.
Non-determinism
You can't unit-test a judgment call
Agents don't pass or fail like a config. They need eval suites, guardrails, and staged rollout instead of deterministic assertions, a different testing discipline most teams don't have.
Human-in-loop
Autonomy is a spectrum, not a switch
Some steps should run unattended; others need a person to approve. Deciding where the line sits, per workflow, is the work, and it changes how the whole thing is shipped.
Trust
Change management is the real blocker
The org has to trust it before it goes live, and keep trusting it as it drifts. That's monitoring, rollback, and approvals as standing capabilities, an implementation problem, not an AI one.
01Demos are cheap; production deployments aren't
02Agents need scoped access to systems of record
03Eval suites replace deterministic test assertions
04Trust and rollback are standing capabilities, not setup

How Lab0 deploys an AI agent.

The same lifecycle we run for enterprise software, with the test and control phases upgraded for agents. Discovery and integration look familiar; evals, guardrails, and tuned autonomy are the agent-native parts. Bought or custom, the workflow is the same.

01

Discover the workflow & scope

Agents gather context across Slack, email, docs, and existing systems, map the workflow the agent will own, and define what it's allowed to touch, the access, permissions, and blast radius, before anything is wired.

$lab0 discover --target agent --scope workflow,permissions
02

Integrate the agent & set guardrails

Wire the agent, one you bought or one we build, into your systems of record, scope its permissions, and set guardrails and the autonomy line per step: where it runs unattended versus where a person signs off.

$lab0 build --integrate --guardrails --autonomy=supervised
03

Eval, dry-run & go live

Run eval suites against real cases, preview every action as a controlled dry-run before it writes to a live instance, then promote, with approval, monitoring, and rollback staying on your team as the agent runs.

$lab0 validate --evals --dry-run --promote-on-approval

Build the agent rollout yourself, or run it as a product.

You can stand up an agent in an afternoon. Getting it safely into production, integrated, scoped, evaluated, and trusted, is the part that takes most teams quarters and a standing team. That's the part Lab0 productizes. If you already have that muscle in-house, you may not need us.

DimensionDIY agent rolloutDeployment as a productLab0
Time to productionMonths in POC purgatory while integration, permissions, and trust get sorted by hand.Pilot to production in weeks, the lifecycle runs as a product that gets faster each time.
IntegrationGlue code to each system of record, rebuilt per agent and per environment.Wired into ServiceNow, SAP, Workday, and custom APIs through the same consoles we already use.
TestingManual spot-checks; non-determinism makes regressions hard to catch.Eval suites and dry-run change previews before anything writes, catch drift before it ships.
Safety & controlAutonomy and permissions decided ad hoc, often after something breaks.Scoped permissions, tuned autonomy, approval gates, monitoring, and rollback on your team.
Bought vs customVendor hands you the agent and waves goodbye at the integration boundary.Agent-agnostic: deploy the agent you bought, or we build one, we own the boundary either way.

Bought or built, we deploy either.

Lab0 doesn't sell you an agent. It gets any agent into production. Picked a vendor? We integrate, scope, and ship it. Nothing off-the-shelf fits? We build the agent too. Same lifecycle, same safety model, no lock-in.

Bought agents

Your vendor, in your stack

You chose an agent platform. Lab0 integrates it into your systems of record, scopes its permissions, evaluates it, and ships it, owning the integration boundary most vendors leave to you.

Custom agents

Built when nothing fits

When no off-the-shelf agent matches the workflow, Lab0 builds it, including multi-agent systems with an orchestrator and specialist agents, through the same discovery-to-go-live lifecycle.

Agent-agnostic

Any model, any framework

Not betting on one model or framework. The deployment muscle, integration, evals, guardrails, approvals, is what carries over, whatever the agent underneath is.

Controlled by design.Every agent action is previewed as a dry-run controlled change before it's applied to a live instance. Scoped permissions, eval gates, approval, and rollback stay with your team, and monitoring runs as the agent does, because agents drift.

Book a demo.

If you've got an agent stuck in a pilot, bought or half-built, give us the one that won't make it to production, and we'll run the deployment end to end. If it's already live and behaving, you don't need us.