Runtime vs Gumloop
Runtime is the agent harness for payment teams, with agents that investigate cases in your cloud, with approvals and an audit trail. Gumloop is a visual AI workflow builder for general business automation.
TL;DR: Runtime is the stronger choice for mission-critical payment and risk operations, where agents run inside your cloud, reason through exceptions, and leave an examiner-ready record. Gumloop is a good fit for visual AI workflows across general business tools.
| Feature |  Runtime |  Gumloop |
|---|---|---|
| What it is | Agent harness for payment and risk ops | Visual AI workflow builder |
| How work is defined | SOPs in plain English, agents reason through exceptions | Node-based workflows on a canvas |
| Runs in your cloud | Yes, or fully self-hosted | Hosted platform |
| PCI and PII handling | Masking, local open models, data stays in your VPC | General data handling |
| Approvals on money movement | Built in, from Slack, Teams, or the dashboard | Build into each workflow |
| Audit trail | Every query, tool call, approval, and cost per run | Workflow run history |
| Payments domain depth | Processor codes, bank files, network rules | General purpose |
| Visual workflow canvas | Not the focus | Core product |
| Forward-deployed engineers | Included for rollouts | Self-serve and enterprise support |
| Pricing | Priced against the headcount it replaces | Credits by usage |
Two different jobs
Runtime is the AI agent harness for payment teams. From payment ops to risk, compliance, and support, anyone on the team can build agents that work their queues on their own computers inside your cloud, called from Slack, Teams, email, SMS, or voice, with approvals and a full audit trail.
Gumloop is a visual AI workflow builder. You drag nodes onto a canvas, connect apps, and add AI steps for things like research, enrichment, and content. It is backed by Y Combinator and Benchmark, and priced on credits that cover AI reasoning, workflow steps, and data services.
Both are good products. The right one depends on the work you're trying to hand off.
Why payment teams choose Runtime
Built for money movement
Payment work is mostly exceptions: a held payout, an ACH return with an odd code, a dispute where the evidence contradicts the claim. Runtime agents reason through those cases on their own computers, across your ledger, processor, bank files, and tickets, and stop for a person before anything moves money.
Your data stays yours
Agent computers run in your AWS, GCP, or Azure account, or you self-host the whole platform. Card numbers, SSNs, and secrets are masked before any prompt or log, and you can serve open-weight models locally for PCI and PII work.
One harness for every team
Support, payment ops, finance, risk, and compliance all build on the same harness, so one stuck payment is one investigation, not four. Every run is stored with its evidence, approvals, and cost, ready when an auditor or sponsor bank asks.
An engineer, not an account executive
A forward-deployed AI engineer who has built payment infrastructure maps your processes, builds the first agents with your team, and sets up guardrails.
When Gumloop makes sense
- You want a visual canvas to build AI automations quickly
- Your workflows touch general business apps like CRM, docs, and email
- The work is well defined and rarely needs judgment on exceptions
- You are comfortable with a hosted, credit-based platform
When Runtime makes sense
- You run payments, banking, or lending operations and the queue keeps growing
- The work needs judgment on exceptions, not just a fixed sequence of steps
- Card data or PII has to stay in your environment
- You need approvals on money movement and an audit trail your sponsor bank will accept
- You want one platform for every team that touches a transaction
See Runtime on your busiest queue
Bring one SOP. A forward-deployed AI engineer builds the first agent with your team, inside your cloud.