Why Agent Evaluation is Different from Model Evaluation
As AI systems become more Agentic and take on complex, real-world tasks, the need for rigorous evaluation is greater than ever. For background, see our earlier blog on why evaluations matter - https://www.innowhyte.ai/blogs/the-crucial-role-of-evaluation-in-agentic-ai-development.
Before we get into why evaluating agents is different from evaluating models, it’s important to first pin down what an agent actually is. The word "agent" gets thrown around a lot, and often without much clarity. A non-technical person may think of it one way, while a technical person may have a very different interpretation. Sometimes the term is even used to create hype, like claiming an agent can replace humans, without ever stopping to define what an agent really means.
This blog from Simon Willison explains that problem well: https://simonwillison.net/2025/Sep/18/agents/.
At Innowhyte, we borrow Anthropic’s definition: building-effective-agents. An agent is best understood as a do-while loop. In each loop, it reasons, decides the next step, takes action, looks at the feedback from that action, and then decides whether to continue or stop. The actions themselves are carried out through tools. Tools are just extensions that let the agent interact with the outside world. The LLM is the brain behind it, reasoning at each step and deciding which tools to use and how to use them.
Now let’s understand the core capabilities of an LLM:
Language understanding: syntax, grammar, semantics
Instruction following: interpreting natural language instructions, constraints, and goals
World knowledge recall: bringing up relevant facts, entities, and dates when needed
Reasoning: logical (if–then, deduction), quantitative, and analogical reasoning
Generative capabilities: producing text, summarization, paraphrasing, rewriting
Most model evaluations focus on testing these core capabilities. They can be measured using a static set of test cases, as long as you have good diversity and coverage. Depending on the capability you care about, different metrics come into play: Perplexity, BLEU/ROUGE/METEOR for generation, hallucination and faithfulness for instruction following, and so on. A wide range of datasets already exist to benchmark models on these dimensions, and to some extent, you can compare LLMs based on their scores. Of course, if you’re using a model in your own product, you’ll still need custom benchmarks because your data and requirements are unique.
This is exactly where things diverge when it comes to agents.
General-purpose agent benchmarks for agent evaluation are only useful for research and comparison at a high level but not in practice. Why? Because agents, by definition, interact with an environment in a loop. That environment is never the same across two use cases. Which means the only meaningful way to evaluate an agent is to design your own evaluation pipeline and datasets that reflect your environment and data (context).
And this is also what makes agent evaluation trickier. The environment is dynamic and open-ended, what gets fed back from the environment as feedback into the LLM matters a lot and the popular saying about LLM - Garbage in, Garbage out - is now more applicable than ever before. The environment keeps evolving and the LLM should be resistant to that. Most user facing agents are conversational agents which further increases the complexity of agent evaluation as it needs to be tested across multiple turns and user personas.
Side-by-side View of Model vs. Agent Evaluation
| Dimension | Model Evaluation | Agent Evaluation |
|---|---|---|
| Primary Question | "Is this output correct (for this input)?" | "Did the system achieve the user’s goal (end-to-end)?" |
| Level of Evaluation | Final outcome (for reasoning models, this may include evaluating the reasoning, but ultimately the reasoning is part of the output tokens) | Trajectory (the do-while loop) and the final outcome |
| Environment | Static, controlled, closed-world (custom domain-specific datasets) | Dynamic, stateful, open-world (APIs, web, user sessions) |
| Key Metrics (examples) | Accuracy, BLEU/ROUGE, Perplexity, Hallucination, Faithfulness, etc. | Task success rate, goal completion, number of steps, tool-call success, latency/cost per task, custom final outcome metrics |
| Core Challenge | Reducing hallucination; aligning outputs to references | Sequential decision-making, planning, tool integration and reliability, state/memory, emergent behaviors |
| Output Determinism | More deterministic under fixed data, though generation is stochastic by default | Often non-deterministic, depends on internal state, tool outcomes, memory, etc. |
| Observability Needs | Prompt logs and outputs | Full trace (plans, tool calls, responses, errors) |
Model vs. Agent Evaluation: How the Workflows Differ
How model evaluation usually works
Create a dataset, either real or synthetic, with clear single input–output pairs
Run the model on that dataset to generate predictions
Define metrics that test the core capabilities (e.g., accuracy, BLEU, hallucination checks, bias, etc.) and adapt them for the specific use case
Compare results against baselines, refine the setup, and repeat as the data or requirements evolve
How agent evaluation plays out instead
Start with tracing and observability, since agents act in loops and you need to capture their full trajectory, not just the final output
For conversational agents, define personas and scenarios, then simulate multi-turn interactions to test behavior under realistic conditions
Evaluate both the outcome and the path the agent took - was it accurate, efficient, and cost-effective? This calls for a mix of heuristic and model-based evaluation lenses.
Keep refining evaluation as the agent runs in production, updating test cases, scenarios, personas, and metrics to reflect changing environments
The takeaway
Model evaluation is dataset-driven and repeatable, but needs adjustment when data or objectives shift
Agent evaluation is scenario-driven, trajectory-focused, and continuously evolving in step with the environments agents operate in
Conclusion
Evaluating a model is like interviewing a candidate. You sit them down and ask questions to test what they already know. You check how clearly they can explain their answers, throw in some domain-specific questions, and see how they handle uncertainty. Do they admit when they don’t know, or do they bluff?
Evaluating an agent is like putting that candidate through an internship/probation. Instead of asking questions, you give them a real task along with the tools they need. Now you’re watching how they actually work. Do they reach the right outcome? How long do they take? Where do they struggle? Do they plan well, or do they waste time backtracking? Here the focus shifts from static knowledge to performance in a real and dynamic environment.
Agent evaluation is far more complex. It’s messy, dynamic, and context-dependent. That’s why it requires a structured approach. At Innowhyte, we help you design that structure. We bring our own evaluation accelerators that make the process faster, easier, and tailored to your environment.