AI 智能體編排平台的六大韌性要素

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 their machine-speed workflows, while credentials stay outside the agent’s reach and every action remains attributable and auditable.”

Recommendation: Access policies and entitlements will need to be managed centrally as organizations deploy more agents, and workflows utilize Model Context Protocol and agent-to-agent interactions.

Standardizing decision observability

Implementing observable applications and data pipelines is a de facto devops standard. But look under the hood in any organization, and you’re likely to see different tools and information quality across teams, platforms, and applications. Observability for AI agents matters even more, helping diagnose decision quality, performance, and costs. Organizations will need observability requirements so that agents are tracked consistently. OpenTelemetry is working on a standard that I expect several AI agent orchestration platforms will implement.

“Typically when you’re implementing observability, you care about capturing latency, responding to information requests, addressing errors, and maybe monitoring costs, but with agents or anything generative AI, capturing the actual quality of response is important,” says Morgan McLean, senior director of product management at Splunk. “Not only do you want to see that a service call is performant, but you want to make sure that the behavior is actually what you would expect.”

To diagnose issues with agent behavior, platforms must capture the thinking steps, the connections made to outside resources, the decisions considered, the resolutions made, and the actions taken.

“Most teams log an agent’s final answer and call it done, but what you actually need is the decision trail with every handoff included,” says Michael Castro, CISO at Liferaft. “That’s the only way an incident review at 500 agents doesn’t turn into guesswork.”

Mat Ryer, senior director of AI at Grafana Labs, adds, “When a multi-agent chain fails, teams need to see which tool call, which retrieved data, or which handoff introduced the error. That visibility lets an orchestration layer catch a stalled or looping agent and hand engineers a hypothesis instead of raw telemetry, which is the real difference between a framework teams pilot once and one they trust to run unattended.”

Recommendation: IT ops, devops, and site reliability engineers (SREs) were the primary beneficiaries of strong observability implementations. With AI agents, other functions, including risk management, information security, and legal, will also need this information. When agents experience quality issues or contribute to a security incident, these other groups will want to review the decision traces.

Enabling flexible operations

Several experts suggested capabilities that can help reduce costs, improve performance, or support agentic decision-making. One example is dynamic model selection so simplified tasks can be offloaded to smaller, lower-cost, internally hosted models.

“Resiliency in orchestration is refusing to hardwire a workflow to one vendor’s model,” says Babak Pahlavan, founder, CEO, and CPO at NinjaTech AI. “If your platform can route a task to Claude for high-stakes reasoning and to an open-weight model for high-volume, lower-stakes work, and swap either out without rearchitecting anything, you’re resilient to model deprecation and pricing changes.”

Karan Munalingal, senior vice president for AI strategy and innovation at Itential, says that an autonomous agent that can act but can’t undo its action is a liability. “Resilient orchestration depends on the ability to detect an unintended outcome and roll infrastructure back to a known-good state automatically without waiting on a human to notice first. That recovery path is what turns agentic adoption from a controlled pilot into something operations teams trust in production,” says Munalingal.

A related capability is to centralize agent failure detection and incident resolution. “The challenge with orchestrating agents isn’t coordinating workflows, but ensuring those workflows recover automatically from inevitable failures,” says Mark Fussell, CEO and cofounder at Diagrid. “Agentic durable execution is driving enterprise adoption because it lets agents detect failures and automatically resume from the exact point of interruption, rather than restarting from scratch. This reduces cost, eliminates duplicate work, and makes long-running AI systems resilient enough for mission-critical workloads.”

Recommendation: AI agents are nondeterministic, so even when accuracy and value are proven pre-production, human and automated controls during the initial rollout will help AI agent orchestration platforms differentiate by automating paths to address unexpected behaviors.

Improving the context layer

Some AI orchestration platforms can monitor AI agent accuracy and model drift. Platforms can also improve the context layer that AI agents tap into, which contains the business’s knowledge, semantics, and business rules.

“Agents that understand prior actions, customer history, and persistent business rules will respond with greater relevance and continuity,” says Guy Bourgault, head of agentic systems at Concentrix. “When that capability is mature, customers are not repeating steps or information, processes become more efficient, and decisions improve as the system acts on what it already knows.”

Matt Wallace, cofounder and CTO at Kamiwaza AI, says that increasing model coherency over long horizons and complex tasks means a lot of engineering is actually about system constraints. “Define constraints and enforcement, objectives and architecture, tests and validation. It’s for the era of not asking, ‘Can AI do this?’ but, ‘How will I know for sure that AI did this and did it right?’ ” Wallace says.

Lastly, some platforms help standardize human-to-AI collaborations. These can include

• A human-in-the-middle tasked with reviewing decisions before actions are taken
• Human augmentation workflows where AI agents recommend actions, and people collaborate on what to do
• Human overrides and kill switches when AI agents need course corrections or need to be pulled from operational responsibilities

Sanjay Gidwani, CEO and founder of Kosmos, says a human check should occur between what an agent finds and its next action. “The agent lays out its findings and evidence, a person reviews it and says yes or no, and only a yes moves the agent forward. That one step does three things: (1) It stops a wrong answer from spreading across four systems before anyone notices, (2) it gives the next agent in line something a person has already approved, and (3) it leaves a record of who agreed to what, which is the first thing anyone asks for when an agent gets it wrong.”

Recommendation: AI agents will get smarter and make better decisions when organizations invest in knowledge management to improve the quality of the context layer’s information. Additionally, look for AI orchestration platforms that implement feedback loops between AI agents, people’s validations, and the context layer. Until AI agents have a history of performance and trust from agent owners and subject matter experts, many organizations will instrument human-in-the-middle and other human-to-AI collaborations.

As organizations deploy more AI agents to production, leaders will recognize the need for enabling orchestration, governance, controls, observability, and metrics. AI orchestration platforms provide a path to resilient operations.