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Five ways to get AI agents into production, and when each one fits
Most companies choose between a large systems integrator, an in-house build, a no-code automation tool such as n8n or Zapier, a SaaS agent tool, or a partner that brings its own platform, like Agentifiers. The right choice depends on where agents must run, who owns the code afterwards, and how much governance the workflow needs.
Side by side
The options at a glance
| Comparison | Large systems integrator | In-house build | No-code automation tools | SaaS agent tools | Agentifiers |
|---|---|---|---|---|---|
| Where agents run | Wherever the program designs it | Your cloud | The tool's cloud, or your own servers where self-hosting is offered | The vendor's cloud | An AWS account in your org, or hosted Deployments.ai in ours |
| Platform to build first | Often part of the program | All of it | None | None | None — we set it up, or you use hosted Deployments.ai |
| Who redesigns the workflow | The integrator, with your team | You | You | You | We do, with your team, for internal agents |
| Human approvals | Whatever the program builds | Whatever you build | Depends on the tool and how each flow is built | Depends on the product | Approval-required tool calls, enforced by the platform |
| Audit trail and run history | Whatever the program builds | Whatever you build | In the tool's run history | In the vendor's product | In your platform, exportable |
| If you part ways | Depends on the contract | Nothing to part with | Rebuild the flows elsewhere | Start over | Keep the code and your data |
| Best fit | Enterprise-wide programs under existing agreements | A platform team with time, and agents at the core of your product | Low-risk, low-volume automations owned by one team | A standard use case the product already covers well | Governed agents in your own AWS account, one workflow at a time |
Option 1
When a large systems integrator is the right call
If you are running an enterprise-wide AI program across many departments, regions, and systems, and you already have a master agreement with an integrator, they can staff the work at a scale few specialist partners match. They are also a natural fit when agents are one part of a larger change that includes ERP projects, data platforms, and change management.
Ask early which platform the agents will run on, who owns it after the program ends, and what happens to the code and the run history when the integrator leaves.
Option 2
When to build in-house
Build it yourself when you have platform engineers with time, agents are core to what you sell, and you want to own every layer. The cost is building the platform before the first agent ships: tenancy, releases, approvals, evaluations, traces, and audit.
Teams on AWS can start from managed building blocks such as Amazon Bedrock AgentCore. We compare that route inbuilding on AgentCore yourself vs working with Agentifiers. Once our platform’s source is released (coming soon), self-hosting it for your own use will be another way to build in-house without starting from zero.
Option 3
When a no-code automation tool is enough
No-code automation tools now include AI agent features, such asn8n’s AI Agent nodeand Zapier Agents. They are faster to start, people who aren’t engineers can assemble flows, and they are the right choice for low-risk, low-volume work owned by one team, where a person checks the result and a wrong step is cheap to undo. If that describes your workflow, start there.
Look harder when actions move money, change access, or touch customers. Then you need approval points the system enforces, evaluations before each change, an audit trail you can export, and a run location your security team accepts. Check how any tool handles each of those before you rely on it, and readhuman-in-the-loop by design for where approvals belong.
Option 4
When a SaaS agent tool fits
If a SaaS product already covers your use case well and your security team accepts the vendor’s cloud, a SaaS agent tool is the quickest start. There is no platform to build and nothing to deploy.
The trade-off is that your data, your process knowledge, and your audit trail end up in someone else’s product, and the agents carry the vendor’s brand. If you part ways, you start over.
Option 5
Where Agentifiers fits
Agentifiers fits when you want AI agents doing real work in your own AWS account without building an agent platform first. For internal agents, we deploy the platform in an AWS account in your AWS Organization (BYOC), brand it as yours, and agentify one workflow at a time with your team, measured against your own baseline.
Small businesses without an IT team or an AWS account can have usset up and run AI agents for them on hosted Deployments.ai, usually in Slack or a simple internal tool.
Product teams get the same multi-tenant platform, Deployments.ai, to build agents into their own product, set up in their AWS account or hosted and managed by us in ours. Seeagents in your product. Either way, if we part ways, you keep the code and your data.
We are not the right fit if you need an enterprise-wide transformation program, or if the work is low-risk enough that a no-code tool will do.
Checklist
Ten questions to ask any AI agent partner
- Where will the agents run, and whose cloud account is it?
- Who holds the encryption keys, and which model providers will see our data?
- Who owns the agent code afterwards, and where does it live?
- Which actions need a person’s approval, and is that enforced by the system or only asked of the model?
- How is each release evaluated before it reaches production, and against what data?
- Can we roll a bad release back to a known version?
- What does the audit trail record, and can we export it?
- Who redesigns the workflow with the people who do the work today?
- How will we measure the result against our own baseline?
- What do we keep if we part ways, and what does the handover look like?
Our answers are on BYOC, the platform, andinternal agents.
Read next
- Compare
Building on Amazon Bedrock AgentCore yourself, or working with Agentifiers
For teams on AWS weighing managed building blocks against a finished platform. - Background
BYOC for AI agents
What runs in an AWS account in your AWS Organization, and what you keep if we part ways. - Blog
What it actually means to agentify a workflow
Mapping the work, placing approval points, narrow agents, evaluations, and measurement. - Use cases
Workflows we agentify
Accounts payable, support triage, lead qualification, onboarding, contract review, and scheduling.
Back to all comparisons.
Not sure which way fits?
Bring the workflow you're weighing. We'll tell you plainly whether we're the right fit, and if another option suits you better, we'll say so.