In the last few years, we’ve seen AI evolve at breakneck speed, from large language models (LLMs) that generate text on demand to full-fledged AI agents capable of reasoning, orchestrating tools, and completing tasks end-to-end. For enterprises, this shift can transform entire business models, but only if evaluation practices keep pace.
To unlock real value, organizations must understand what makes agents different from traditional software applications and how to evaluate them with rigor, oversight, and business context.
In this post, we’ll break down where agents came from, what makes them special, why evaluating them isn’t exactly like evaluating traditional software applications and, most importantly, how to do it right if you want to use them effectively in the enterprise.
From Generative AI to Agents: The Next Leap
Generative AI introduced the ability to produce human-like text, images, and code. At first, that felt like magic: Suddenly you could draft an email in seconds, generate a slide deck outline, or spin up a code snippet with a single prompt.
But enterprises are quickly realizing they need to go beyond generic GPTs. A beautifully worded answer is nice, but it doesn’t finish the job. Someone still has to copy that text into an email client, check it for accuracy, send it, track responses, and maybe even update a CRM.
In other words, GenAI is great at producing content, but not at producing outcomes.
That’s where agents come in. Instead of just handing you a draft or a suggestion, agents can autonomously:
- Reason through tasks: Break down a request into multiple steps, rather than responding in isolation.
- Make decisions: Choose between tools, APIs, or knowledge sources based on context.
- Take action: Execute workflows inside enterprise systems, whether that’s booking a meeting, updating a database, or triaging a support ticket.
This leap is critical for enterprises. It’s the difference between having a smart auto-complete and having a digital teammate who can actually move a process forward. Agents turn GenAI from a productivity boost into a true operational engine for the business.
But it also raises new challenges: How do you measure success? What does reliability mean? And how do you balance autonomy with oversight?
Applications vs. Agents: Similarities and Key Differences
At first glance, agents might seem like just another type of software. Both traditional software applications and agents:
- Serve end users within workflows.
- Must meet enterprise standards for uptime, scalability, and compliance.
- Require thoughtful design and governance.
How Agents Are Different
Where agents diverge is in how they work. They differ in two important ways: reasoning and non-determinism.
- Reasoning and Adaptivity: Unlike deterministic software, agents generate plans on the fly, adapt mid-task, and may take multiple valid paths to success.
- Non-Determinism: Outputs can vary between runs, even with the same inputs, due to probabilistic reasoning.
Why Traditional Evaluation Methods Fall Short
That flexibility is what makes them powerful, but also what makes evaluating them a whole new challenge. Because agents behave more like people (unpredictably) and less like code, we need to evaluate them differently.
The scope of success and failure goes beyond just task completion. Failure can look like agents that hallucinate, drift, perform unnecessary steps, leak personally identifiable information (PII), and more.
This means binary pass/fail tests and code reviews that work with traditional software applications don’t work with agents because they can fail in more than one way. Instead, enterprises must evaluate how well agents reason, adapt, and deliver consistent value against real-world constraints.
In the worst cases, skipping agent-specific evaluation means risking business failures (wrong outputs, poor UX), compliance failures (PII leaks, bias), and operational failures (latency, cost overruns). These aren’t corner cases; they’re systemic, enterprise-wide risks.
5 Key Categories for Evaluating Agents in the Enterprise
To ensure agents deliver impact reliably, safely, and effectively at scale, enterprises should focus on five categories of evaluation, each requiring clear measures, feedback loops, and monitoring.
1. Task Success and Output Quality
Outputs need to be consistently accurate, reliable, and aligned with business expectations. If the agent can’t do what it’s supposed to, nothing else matters.
What to measure:
- Task completion rate on critical business workflows.
- Accuracy, precision, or compliance of outputs, based on subject matter expert (SME) judgment.
- Error rates, retries, and escalation frequency.
How to measure:
- Define gold-standard benchmarks with SMEs for priority use cases.
- Use human-in-the-loop review for high-stakes tasks.
- Track longitudinal performance to ensure improvement over time.
2. Business Value and User Satisfaction
Agents aren’t just about being “right”; they’re about making life easier for end users. Evaluate business value and user satisfaction to ensure your agent delivers a smooth, intuitive user journey that drives adoption and ROI.
What to measure:
- Time saved per workflow compared to baseline processes.
- End user adoption and repeat usage rates.
- Net Promoter Score (NPS) or satisfaction surveys tailored to agent interactions.
How to measure:
- A/B testing between agent-powered and traditional workflows.
- Instrument user journeys to capture friction points.
- Collect qualitative feedback to guide agent refinement.
3. Reasoning and Tool Use Effectiveness
Agents need to string together steps, call the right tools, and finish the job without spinning in circles. By evaluating their reasoning and tool use effectiveness, you ensure the agent’s steps are efficient, transparent, and verifiable from input to outcome.
What to measure:
- Ability to select and sequence tools appropriately.
- Number of unnecessary or redundant steps per task.
- Frequency of abandoned or looping reasoning chains.
How to measure:
- Trace reasoning paths and tool invocations in logs.
- Visualize “agent trails” to identify inefficiencies.
- Use scenario-based testing to validate reasoning under edge cases.
4. Trust, Oversight, and Compliance
In the enterprise, trust is everything. You need transparency, auditability, and safeguards to ensure agents behave within safe, ethical, and regulatory boundaries with full auditability.
What to measure:
- Instances of policy-violating, biased, or harmful outputs.
- Auditability of decision-making and tool usage.
- Effectiveness of safeguards (red-teaming, guardrails, moderation).
How to measure:
- Run automated safety tests on a recurring basis.
- Maintain full audit logs for compliance teams.
- Integrate escalation workflows for SME review when risk thresholds are triggered.
5. Scale and Operational Performance
A great demo doesn’t mean much if it falls apart in production. Evaluating operational performance at scale ensures the system meets enterprise-grade requirements under real-world workloads.
What to measure:
- Latency and response times under load.
- Uptime and error rates versus defined service-level objectives (SLOs).
- Cost per interaction, including drift over time.
How to measure:
- Continuous monitoring dashboards with alerting for anomalies.
- Stress testing at projected usage peaks.
- Cost tracking at user, team, and workflow levels to flag runaway expenses early.
A Framework for Enterprise Readiness
Evaluating agents effectively isn’t just about measuring today’s performance, it’s about creating a framework for continuous trust and improvement. That means:
- Iterative evaluation: Agents improve through cycles of deployment, monitoring, and expert-guided refinement.
- Contextual benchmarks: Success metrics should align with the business process, not generic AI benchmarks.
- Cross-functional governance: IT, business, and compliance teams must collaborate on evaluation to ensure balanced oversight.
- Humans-in-the loop and experts-in-the-loop: SMEs are critical. Humans-in-the-loop provide oversight, but experts bring domain depth. They know the business risks, define success criteria, and ultimately build trust that agents are safe and useful.
Why It Matters Now
As more enterprises experiment with agents, those who establish rigorous evaluation practices early will build a competitive moat. They will know not just that their agents “work,” but that they work reliably, safely, and at scale, delivering measurable business outcomes.
At Dataiku, we believe agents represent the next frontier of enterprise AI. By pairing robust evaluation with strong governance, organizations can move confidently from pilot projects to production, and unlock the full transformative potential of AI.