The question "which is the best AI agent platform" has no useful answer, because the answer depends on who builds, who sustains, what has to be integrated and what your audit will ask for. The "best platforms" lists that come up in search are mostly feature comparisons, and features change every quarter. What does not change is the bar. This text gives you the bar.
The eight criteria
1. Who builds, and who sustains it afterwards
It is the question that decides the project and the one that shows up least in comparisons. Some platforms hand over the tool and your team builds. Others hand over engineers who build with your team until it runs. Neither is wrong, but they are different models with different costs, and choosing one thinking it is the other is the origin of a good share of the projects that never reach production. Ask: who writes the first agent, and who answers for it six months after go-live?
2. Integration with what you already have
The agent will have to read the ERP, write to the CRM and speak through the official channel. A ready connector reduces that to configuration; its absence turns it into a project. Ask for the connector list, check whether your systems are on it, and ask what happens with the legacy system that has no API. The answer to that last question usually defines the real schedule.
3. Model portability
The model that is best today will not be best in six months, and the price changes before that. The platform has to let you switch provider without rewriting agents, route different tasks to different models, and fall back when a provider goes down. A platform tied to a single model transfers that vendor's business risk to you.
4. Governance and human approval
Access control by action, not only by user. Human approval configurable by type of operation and by value, not a global on-off switch. A cost limit per team. Sanitization of sensitive data before the information reaches the model. Each of those items is a question your risk team will ask, and you are better off asking it first.
5. Observability and auditing
Every execution, every routing decision and every tool call, with parameters and return, recorded and reproducible. It is what answers "why did the agent do that" and what the audit will demand. Ask to see the trail of a real execution, not a summary dashboard. If the vendor shows you a chart instead of an execution, the trail probably does not exist at the level you need.
6. Where the data sits, and what it passes through
Per-client isolation, processing region, the model provider's retention policy, and the option to run in your own cloud when the sector requires it. Does the vendor train models on your data? The answer has to be in the contract, not in the conversation. For a regulated sector this criterion is a condition of entry.
7. How it is billed
Per execution, per seat, per agent, per model consumption, or a combination. No billing model is wrong, but each one creates an incentive. Per-execution billing penalizes volume; per-seat penalizes growing the team; per-consumption passes the provider's price swings on to you. Simulate the cost at your real volume, in year one and in year three, and ask what happens to the price when the fifth agent arrives.
8. What happens after go-live
An agent in production is never finished: the process changes, the catalog changes, the quality falls if nobody follows along. Who reviews a sample, who tunes, who answers when it breaks at 10pm? Support with an SLA in your own language is part of that. The proposal has to describe the next twelve months of operation with the same clarity it describes the deployment.
The questions to take into the meeting
| Criterion | The question that exposes the real answer |
|---|---|
| Who builds | Who writes the first agent, and who answers for it in six months? |
| Integration | Is my system X on the connector list? And the legacy one with no API? |
| Portability | If I switch model provider tomorrow, what has to be rewritten? |
| Governance | Can I require human approval only for actions above a certain value? |
| Observability | Show me the trail of a real execution, with the tool calls. |
| Data | Do you train models on my data? Where is that in the contract? |
| Billing | What is the cost at my real volume, in year one and in year three? |
| After go-live | What does the proposal cover in the week the agent starts getting things wrong? |
Warning signs
- A demo with prepared data, and none with yours. Ask to run one of your cases before signing. A vendor who refuses has a reason.
- "We are compliant" as an answer. It answers none of the questions under criterion 6.
- A feature comparison with more than twenty rows. Features age in three months. What decides is the delivery model, the integration and the operation.
- A price that only exists in conversation, with no simulation at your volume. You will discover the real cost in month three.
- A proposal that ends at go-live. The text describes the deployment and says nothing about the operation. That is where most projects die.
- A single model provider, with no path to switch. That vendor's business risk becomes yours.
About the "best platforms" lists
A word about the format search delivers most. Best-platform lists tend to be produced by sites earning a referral commission, or by vendors listing themselves, and they compare features because features are easy to tabulate. None of that makes them useless, but none of them answers criteria 1, 7 and 8, which are the ones that decide most. Use the list to learn who exists, and the bar above to learn who fits.
When you want the side by side with specific tools, our comparisons do that against a declared bar: every claim about the other tool comes from that tool's public material, with the date it was consulted, and every page has the section where the right choice is the other tool.
How Runflow answers the eight
In order. Who builds: the Forward Deployed engineers, with your team, and they stay after go-live. Integration: more than 150 connectors for ERPs, CRMs, APIs and channels, with MCP and internal tools for whatever has no connector. Portability: multi-LLM in the SDK and the LLM Hub with routing and fallback between providers. Governance: human approval where the risk demands it, access control, cost limits by team and by user, sanitization of sensitive data. Observability: every execution, decision and tool call recorded and reproducible. Data: isolated per client, never used to train models, in our cloud or in yours (AWS, Azure, GCP). After go-live: the team stays on the account, and the people who operate it are trained during deployment.
On billing, the right answer is the simulation at your volume, and that is what we do in the free assessment, with the real process and the real data. If the conclusion is that your case calls for another tool, we say so.
