Evaluate an enterprise AI agent platform on five axes: governance (identity, permissions, and audit for every agent action), integration depth (how it connects to your real tools and data), orchestration model (how multiple agents coordinate and hand off work), production reliability (observability, failure handling, and evaluation tooling), and economics measured as cost per completed task rather than per seat. The demos all look similar. The differences show up in governance and operations, which is where enterprise deployments actually succeed or fail.
Governance is the axis I weight most heavily, because it is the one you cannot retrofit. Ask whether each agent has its own identity distinct from the user who launched it, whether permissions are enforced by the platform or merely described in prompts, and whether there is a tamper-evident log of what every agent did and why. A platform that cannot answer "which agent took this action, under whose authority" will fail its first serious audit, whatever its benchmark scores say.
Integration depth is where pilot success diverges from production success. The question is not how many connectors the vendor lists but how the platform reaches systems it has never seen: whether it supports open tool protocols such as MCP, how it handles authentication into your systems, and what building a custom connection actually takes. A platform that only shines inside its own vendor's suite is a suite feature, not a platform.
Orchestration model determines what the platform can grow into. Single-agent products handle bounded tasks; the harder question is how work moves between agents, how a supervisor constrains a subordinate, and what happens when one step in a chain fails. Ask to see a multi-agent workflow fail and recover. Vendors rehearse the success path.
Reliability and evaluation tooling separate production platforms from demo platforms. You want tracing on every run, the ability to replay a failed task, and built-in evaluation so regressions surface before customers do. Agents are non-deterministic; the platform's job is making that manageable.
On economics, per-seat pricing is a legacy frame. The meaningful number is what a completed task costs, including model tokens, retries, and the human review the workflow still needs. As of September 2026, token consumption on complex multi-step tasks varies enormously between platforms performing the same job, and that variance compounds at scale.
One test cuts across all five axes: run a two-week pilot on a real workflow with your own data, and count completed tasks against total cost. The number that comes out is rarely the number in the deck.
Go deeper: my CrewAI agent orchestration review, my Google Vertex AI Agent Builder review, and the agent org chart.