A practical framework for evaluating agentic AI systems
There's a particular flavor of chaos that emerges when organizations deploy agentic AI without knowing how to evaluate it.
You've seen it. Someone on the leadership team gets excited about autonomous agents handling customer inquiries or processing exceptions. A pilot launches. The demo looked incredible. And then, three months later, you're fielding complaints about responses that made no sense, approvals that went to the wrong people, and a compliance officer asking questions you genuinely cannot answer.
The problem isn't the technology. It's that traditional evaluation methods were built for a different era. Agentic systems reason, plan, act, and adapt across multi-step workflows. Measuring only their final output is like evaluating a pilot by whether the plane landed, ignoring everything that happened at 30,000 feet.
According to Gartner, 33% of enterprise software applications will include agentic AI by 2028. But over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.
This article presents a practical framework for operations executives who need to assess agentic AI systems for reliability, efficiency, and scalability.
Key takeaways
Evaluating agentic AI requires a multi-layered approach. You need to assess planning capability, tool usage, memory coherence, and behavior across entire workflows, not just individual outputs.
Key metrics must connect to business outcomes. Task success, tool call accuracy, and error recovery only matter when tied to operational impact like cycle time, throughput, and SLA compliance.
Evaluation belongs in governance, not as an afterthought. Embedding evaluation into deployment pipelines ensures continuous trust, not just launch-day confidence.
How to evaluate agentic AI systems
Here's a structured framework for comprehensive evaluation. Each layer builds on the previous, moving from business alignment through technical precision to ongoing governance.
1. Business goals and operational alignment
Define success criteria before you touch technical evaluation. Start with the outcome you're trying to achieve: reduced onboarding cycle time by 30%, fewer manual handoffs in exception management, lower error rates in order-to-cash workflows.
Then identify the KPIs that will measure success: throughput, error rates, SLA compliance, cost per transaction. And critically, name the risks the system could introduce.
Platforms like Moxo make this alignment easier by structuring AI within defined workflows where business goals are explicit and measurable from the start.
2. System efficiency and scalability
Assess how the system performs under real operational load. A system that works beautifully in a demo environment might crumble when processing hundreds of concurrent workflows.
Latency and throughput matter because slow agents create bottlenecks. Cost structure includes tokens, compute costs, and service fees relative to performance. Concurrency handling determines whether the system scales without degradation.
Process orchestration platforms address this by coordinating AI agents within structured workflows, ensuring consistent performance as volume scales.
3. Session-level outcomes and task completion
This layer evaluates end-to-end performance across entire workflows. Individual step accuracy means nothing if the process doesn't reliably reach completion.
Task completion rate answers the essential question: does the agent finish workflows without human rescue? Trajectory quality examines whether the agent follows a logical sequence of actions. Goal alignment ensures results match user intent and business objectives.
Moxo's AI-driven workflow automation tracks session outcomes by design, making it easy to identify where trajectories break down and why.
4. Node-level precision and tool use accuracy
Drill down to individual actions to understand where and why workflows fail. This is where you find the problems that aggregate metrics hide.
Tool selection accuracy asks whether the agent chose the correct tool or API for each decision point. Parameter correctness examines whether tool calls are formatted and executed properly. Error handling reveals whether the agent recovers gracefully or spirals into confusion.
5. Memory and context coherence
Agentic systems must remember and utilize context consistently across interactions. This is where many systems quietly fail.
Short-term memory keeps relevant context within a session. Long-term memory recalls historical interactions where needed. Memory drift measures whether contextual relevance degrades over time.
This is why embedding AI within structured processes matters. When context lives inside a workflow rather than depending on the agent's memory alone, coherence becomes a system property rather than an agent capability.
6. Robustness, safety, and predictability
Measure how the agent behaves when things go wrong. Because things will go wrong.
Error recovery determines whether the system detects failures and responds appropriately. Safety adherence ensures the system respects policy and compliance constraints. Predictability asks whether similar inputs yield consistent, explainable behavior.
Process orchestration addresses this by keeping humans accountable at decision points while AI handles coordination.
7. User and stakeholder feedback
Include human evaluation alongside automated metrics. Numbers don't capture everything.
Human-in-the-loop review validates reasoning chains and decisions. User satisfaction metrics measure how end users perceive the assistance. Feedback loops continuously refine evaluation criteria based on real usage.
Embedding evaluation into deployment and governance
Evaluation isn't a one-time checklist. It's a continuous process. Systems that pass initial testing can degrade over time as data patterns shift and edge cases emerge.
Offline testing should simulate edge cases and stress tests with realistic workload variety before deployment. Online monitoring tracks production KPIs and live session outcomes. Governance and alerts integrate thresholds that trigger human review when violations occur.
Moxo's workflow visibility features provide real-time insights into where work stands, what is blocked, and where intervention is needed.
Common pitfalls and how to avoid them
Relying on single metrics creates blind spots. Measuring only accuracy or latency misses behavioral nuances that determine operational reliability.
Ignoring tool integration errors lets workflow failures go unnoticed. API invocation issues often cascade silently until something visibly breaks downstream.
Neglecting memory evaluation allows performance to degrade over time.
Underweighting human feedback treats automation as infallible. Some decisions require human judgment, and evaluation frameworks must account for that.
Why Moxo helps
Evaluating agentic AI becomes significantly easier when AI operates within structured, accountable processes rather than as a standalone layer on top of fragmented workflows.
Moxo is built around a core distinction. Every complex process contains judgment work that only humans can do (approvals, exceptions, risk decisions) and execution work that surrounds those decisions (preparation, validation, routing, follow-ups). AI agents handle the coordination. Humans remain accountable for outcomes.
Here's what exception management looks like with Moxo. A process stalls when an approval exceeds policy limits or documentation is missing. An AI agent reviews the submission, flags the exception, and prepares the approval request with relevant context.
The workflow routes to the appropriate decision-maker, notifying them only when action is required. A human reviews, makes the judgment call, and approves or escalates. The process moves forward without email chains or manual chasing.
Operations teams using this approach report faster cycle times, reduced coordination overhead, and the ability to handle higher volumes without proportional headcount increases.
Conclusion
Evaluating agentic AI systems requires moving beyond model-centric metrics to system behavior, business alignment, and operational trust. Align evaluation to business outcomes, measure across multiple layers, include human feedback, and embed evaluation into governance rather than treating it as a launch-day checkbox.
The organizations getting this right treat evaluation as an ongoing process. They build observability into deployment, set thresholds that trigger human review, and continuously refine based on real usage.
For teams looking to deploy agentic AI within structured, human-accountable processes, Moxo provides a process orchestration platform where AI agents handle coordination while humans remain responsible for critical decisions. Get started with Moxo
FAQs
What should I look for when evaluating agentic AI for business operations?
Focus on orchestration capabilities, human oversight controls, observability into system behavior, and failure handling mechanisms. Agent intelligence matters less than how reliably the system operates within your actual workflows.
How is evaluating agentic AI different from evaluating traditional automation like RPA?
Agentic AI introduces non-determinism, meaning the same inputs might produce different action sequences. This requires stronger governance controls, clearer accountability boundaries, and evaluation frameworks that assess reasoning quality and error recovery.
Can agentic AI be used safely in regulated operations today?
Yes, when deployed within systems that enforce rules, require approvals at critical points, and maintain audit trails. The key is embedding AI within structured processes where human accountability is explicit.
Should agentic AI replace existing operational workflows?
In most cases, agentic AI should augment workflows rather than replace them entirely. The most effective implementations use AI to handle coordination and routine execution while preserving human judgment for decisions and exceptions.
