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Deploy AI agents

Deploy AI agents into production, not just another pilot.

Most agent pilots never ship. This is what it actually takes to put an AI agent into supervised production, in your stack, with evals, guardrails, and an owner, and how Gaper gets you there.

Book a free AI assessmentSee AI agent development
gaper · ship pipeline
✓Scope
✓Build
✓Eval
✓Deploy
●Live
prototype→production
In one sentence

Deploying an AI agent means moving it from a demo into supervised production: wired into your real systems and data, gated by evals and guardrails, running in your cloud, monitored with traces, and owned by a team that can operate it and roll it back.

Pilot to prodThe gap we close
Model-agnostic
In your cloudYour auth, your data
You own itCode and runbook
Free AI assessment

Bring one messy workflow. We will show whether an agent, automation, SaaS product, or no build is the right next move.

Find your first agent workflow
01

Why most agent pilots never reach production

A demo only has to work once, on clean data, with someone watching. Production has to work on the messy 20 percent: real edge cases, real permissions, real consequences. That unglamorous middle, integration, evaluation, guardrails, and a clear owner, is where most pilots quietly stall.

  • Demos skip integration and real data
  • No evals means no way to trust a change
  • No owner means no one ships it
Production launchWhat Gaper hands over
doneWorkflow map

Inputs, systems, owners

doneAgent build

Tools, prompts, permissions

readyEval suite

Known cases and edge cases

readyGo-live runbook

Approvals, traces, rollback

Handoff packagesource codedashboardrunbookowner training
02

What production-ready actually means

A production agent is not just a good prompt. It is evaluated before every release, guarded on risky actions, observable end to end, and reversible. It runs where your data lives and escalates to a human when it should.

  • Evals gate every prompt and tool change
  • Guardrails and approvals on risky steps
  • Traces, rollback, and an SLA from day one
Release gate
Eval suitePolicy checkHuman fallbackRelease

p95 latency 1.2s

eval pass 12/12

rollback ready

03

How Gaper deploys an agent

We scope one workflow, build it with the connectors and data it needs, gate it with evals in a sandbox, then move to supervised production with monitoring and an owner. You get the code and the runbook, not a black box.

  • Sandbox first, supervised production second
  • Deployed in your cloud, your auth
  • You own the code and can operate it without us
Ship pipeline
TriggerRetrieveDecideAct

p95 latency 1.2s

eval pass 12/12

rollback ready

Where it pays off

Concrete places agents earn their keep.

01
ticket82% resolved
#4821Damaged ordernew
Agent

Policy matched. Refund ready for approval.

Lookup orderApprove refund
human-gated

Eval suite

Regression tests that gate releases before a prompt or tool change reaches users.

02
ledger31 hrs saved
Stripe$18,240matched
Bank$18,240clear
audit-ready

Guardrails & approvals

Hard limits and human sign-off on signatures, submissions, and other risky actions.

03
pipeline+18% coverage
LeadFitBrief
91

account score

CRM updated
crm synced

Observability

Traces of every tool call, retrieval, and decision, with cost and quality visible.

04
reviewHIPAA path
Credentialing packet3 checks passed
Human review required
review queue

Fallback & escalation

A clean handoff to a human when confidence is low or policy requires it.

05
extract14 fields
Invoice no.TotalDue date
2 exceptions routed
exceptions out

Runbook & rollback

Documented ownership, on-call, and a one-step rollback when something drifts.

06
answerfresh docs
How do I request access?
Answer drafted3 cited sources
HR policyOkta SOP
sources shown

Security & residency

SSO, RBAC, PII redaction, and deployment inside your cloud for regulated data.

FAQ

Common questions.

What does it mean to deploy an AI agent in production?+
It means the agent is wired into your real systems and data, gated by evals and guardrails, running in your cloud, monitored with traces, and owned by a team that can operate and roll it back, not a sandbox demo.
Why do most AI agent pilots fail to reach production?+
The demo is the easy 20 percent. The hard part is integration with real systems, evaluation against real data, guardrails on risky actions, human approval gates, and an owner who runs it. That gap is exactly what an implementation partner closes.
Can you deploy the agent inside our cloud?+
Yes. For regulated or sensitive workloads we deploy in your cloud with your security controls, SSO, RBAC, PII redaction, and full audit logging, so data never leaves your boundary.
How long does it take to deploy an AI agent?+
A first working build can land in as little as 24 hours for a scoped workflow. Supervised production depends on integration and governance, typically weeks rather than quarters, scoped up front.
What keeps a production agent safe and reliable?+
Evals that gate every release, guardrails and approvals on risky steps, grounded retrieval with citations, end-to-end observability, and a one-step rollback with a documented runbook.
Who owns and operates the agent after launch?+
You own the code and documentation. We can hand it fully to your team or run it under an SLA with monitoring, evals, and ongoing improvement, your call.
Keep exploring
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Production AI agents, shipped with an owner

Want agents like these in your stack?

Book a free assessment, we'll map where an AI agent creates real leverage in your workflows and scope the first one to ship.

Build, deploy, runYour cloudYou own the code