While many organizations are in early AI agent rollouts, research suggests that more will scale to greater numbers of agents and assistants in the coming year. An August 2026 report states that 66% of enterprises are likely to invest in digital workers in the next 12 months. It reports that the top requirements for these agents include demonstrated accuracy, clear explanations around decisions, and human override capabilities.
At some point in that journey, IT, data, and security leaders will have to consider how to manage, monitor, and measure thousands of AI agents running in production. This is where AI agent orchestration platforms are valuable as they become a central control plane for agent-to-agent and human-to-agent interactions.
“The future of agent orchestration depends on openness, and organizations won’t trust agents to work across applications, clouds, and company boundaries unless they can communicate through open, interoperable standards,” says Jonathan von Ruden, chief AI officer and head of platform and AI at SAP. “Open standards help customers avoid fragmented AI environments, accelerate adoption, and maintain control over how agents access enterprise data, processes, and governance.”
In researching ways to evaluate AI agent orchestration platforms, I identified more than 60 commercial and open source platforms in this relatively new category. This article follows up with six expert recommendations of the capabilities that drive resilience.
Measuring outcomes and business value
“Tokenomics,” the tracking and managing of both costs and business value delivered from AI agents, is an important AI finops capability to instrument consistently across deployed agents. Top platforms will consider financial controls alongside other key performance indicators such as trust, customer impact, and carbon consumption.
“Every agent has to be measured against revenue growth, customer retention, cSAT, cost reduction, eNPS, and capacity gains—the same numbers a CFO already tracks,” says Rob Scudiere, CTO at Verint, “Gaps in measurement mean you can’t prove ROI, optimize routing, or determine where to invest further.”
Recommendation: AI cost debt is a risk for organizations that rapidly deploy AI agents without measuring value and cost. This one feature alone can justify investing in an AI agent orchestration platform.
Running evals before deployments
Responsible devops engineers create unit tests and establish continuous testing in their deployment pipelines. The equivalent for AI agents is writing evals, which are natural language expressions used to determine an agent’s accuracy and other quality factors.
Vishal Sood, president of research and development at Typeface, says that agent orchestration without evaluation is CI/CD without tests. “Evals and telemetry tell you whether the output was good and feed that back into the next run. A pipeline that ships without checking isn’t resilient; it’s just fast, and every team that learned this lesson in software is about to learn it again with agents,” Sood says.
Recommendation: Review a platform’s eval engines, such as capabilities from LLM judges in Databricks, agent evaluations in Snowflake, the evaluation platform in LangChain, eval feedback loops in Agentforce, and the evaluation agent in Dataiku.
Establishing runtime governance
Deploy and then bolt on governance was a common approach in the cloud migration and modernization era, but that is risky when deploying AI agents. Agents can often access the business’s intellectual property, customer data, and other sensitive information, so consistent role-based access controls (RBAC) are a necessary prerequisite for agent deployment. Additionally, hard rules on who gets access to what worked sufficiently when apps and APIs were deterministic. But with AI agents, you must evaluate the request’s context against governance policies at runtime.
“If there’s one capability driving both adoption and resiliency, it’s governance built into execution, not bolted on afterward,” says Chetak Shankar, senior product marketing manager for orchestration at UiPath. “That means policy enforced and audit trails captured the moment an agent acts, not reconstructed after something breaks.”
Gil Elbaz, cofounder and chief AI officer at Onyx Security, adds, “The pace and sophistication of agents require runtime protection that understands user intent, evaluates agent behavior as it happens, and ensures agents stay within the permitted path based on the organization’s own governance and runtime policies.”
For those organizations developing proprietary AI agents, dev teams will need implementation standards, especially when developing with AI code generators. “Use APIs and automation to grant narrowly scoped, short-lived access for a specific task, verify who authorized it, and revoke it when the work is complete,” says Nancy Wang, CTO at 1Password. “Developers keep
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